Six-dimensional Model Research Articles

A Physics-Informed Auto-Learning Framework for Developing Stochastic Conceptual Models for ENSO Diversity

Abstract Understanding ENSO dynamics has tremendously improved over past decades. The ENSO diversity in spatial pattern, peak intensity, and temporal evolution is however still poorly represented in conceptual ENSO models. In this paper, a physics-informed auto-learning framework is applied to derive ENSO stochastic conceptual models with varying degrees of freedom. The framework is computationally efficient and easy to apply. Once the state vector of the target model is set, causal inference is exploited to build the right-hand side of the equations based on a mathematical function library. Fundamentally different from standard nonlinear regression, the auto-learning framework provides a parsimonious model by retaining only terms that improve the dynamical consistency with observations. It can also identify crucial latent variables and provide physical explanations. This methodology successfully reconstructs the equations of a realistic six-dimensional reference ENSO model based on the recharge oscillator theory from its data. A hierarchy of lower-dimensional models is derived and their representation of ENSO (including its diversity) systematically assessed. The minimum model that represents ENSO diversity is four-dimensional, with three interannual variables describing the western Pacific thermocline depth, the eastern and central Pacific sea surface temperatures (SSTs), and one intraseasonal variable for westerly wind events. Without the intraseasonal variable, the resulting three-dimensional model underestimates extreme events and is too regular. The limited number of weak nonlinearities in the model are essential in reproducing the observed extreme El Niño events and the observed nonlinear relationship between eastern and western Pacific SSTs.

Extracting organizational culture from text: the development and validation of a theory-driven tool for digital data

ABSTRACT Understanding organizational culture and practices (OCPs) is crucial for business success, yet current assessments rely heavily on self-reports. We developed the “Dictionary of Organizational Culture and Practices”, the first theory-based text analysis tool designed to quantify OCPs as an alternative to surveys. Our approach integrates computational natural language processing with expert human judgement, using a 26 million words large corpus consisting of employee reviews of 526 companies, 1,121 mission and vision statements, and 285 company websites. We replicate Hofstede’s six-dimensional model of OCPs, not to be confused with Hofstede’s widely known model of national culture. Criterion validity is established by comparing our tool to existing ones that used data-driven approaches. Notably, our tool demonstrates construct validity through the striking similarity of the dimensional structure with survey-based measures. The level of alignment varied depending on the type of text, with congruence coefficients exceeding .9. Our findings offer valuable insights for academics and managers seeking to understand and shape an organization’s culture more effectively.

Modeling Dimensionality of Bilingual Kindergarteners' Language Knowledge in Spanish and English.

This study used structural equation modeling to investigate the dimensionality of language in Spanish-English bilingual kindergartners. Five theoretical models were compared, including (a) a unidimensional model; (b) a two-dimensional model by language (Spanish, English); (c) a three-dimensional model by domain of language (phonology, semantics, morphosyntax); (d) a second-order model organized first by language (Spanish, English), with each language dimension comprising three domain-specific dimensions (phonology, semantics, morphosyntax); and (e) a six-dimensional model with freely covarying language-specific domains. Participants included 238 Spanish-English bilingual kindergartens, as identified by parent report of current language exposure and direct language measures. All participants completed a battery of phonology, semantics, and morphosyntactic test items in English and in Spanish. The six-dimensional covarying model constrained by domain and language provided the best fit for the data, with six separate factors for phonology, morphosyntax, and semantics in English and Spanish. The excellent model fit is supported by findings of a χ2 to df (degrees of freedom) ratio < 2 with no significance, comparative fit index > .95, standardized root-mean-square residual < .08, and root-mean-square error of approximation values ≤ .05. Results support emergent theories of bilingual language development. Application of the results to the evaluation and intervention of oral language abilities in bilingual children entering the formal education setting are considered. https://doi.org/10.23641/asha.25843846.

EGame-flow: psychometric properties of the scale in the Mexican context

PurposeThe present study examined the psychometric properties of the EGame- flow scale in a Mexican sample, presenting evidence of construct validity (exploratory factor analysis and confirmatory factor analysis), reliability (Cronbach’s alpha and McDonald’s omega) and discriminant validity (mean variance extracted).Design/methodology/approachParticipants: Of the 255 Mexican participants in the non-probabilistic sample who had previously interacted with the LOST logistics simulator, 166 (65%) were men and 89 (35%) were women; their ages ranged from 22 to 45. The statistical packages SPSS 25, JASP 0.16 and AMOS 23 facilitated the corresponding analyses. First, we calculated the means and standard deviations of the scale items. Next, we performed an exploratory factor analysis to examine the measurement model’s internal structure and a confirmatory factor analysis to confirm the structure proposed in the exploratory factor analysis. To analyze the internal structure of the measurement model so that the estimates were not affected by multivariate normality problems, we utilized the AMOS bootstrap method (with 500 repetitions, 95% CI), the maximum likelihood (MV) estimation method, and the fit indices: X2, p (chi-square and associated likelihood), Tucker–Lewis index (TLI), standardized statistical mean square residual (SRMR), comparative fit index (CFI) and root mean Square error approximation (RMSEA) with its confidence interval, the values of X2 with p &lt; 0.001; TLI, CFI, AGFI = 0.95; RMSEA and SRMR = 0.08 (Byrne, 2016). Finally, we estimated the reliability of the measurement model with Cronbach’s alpha (a), McDonald’s omega (ω) coefficient and the mean variance extracted (VME).FindingsAn exploratory factor analysis with the MV method and obliminal rotation showed a good fit of the data to the model, which aligns with the significance of the Barlette sphericity test (X2 = 8443.2, p &lt; 0.000) and the Kaiser–Meyer-Olkin (KMO) value of 0.94. The indices confirmed the fit of the data to the six-dimensional model for measuring the users' level of enjoyment of online games (X2 = 678.2 gl = 411, p = 0.000; SRMR = 0.05; TLI = 0.95, CFI = 0.95 and RMSEA = 0.05, IC 90% [0.04, 0.05]).Research limitations/implicationsThe self-reporting format of the scale increases the social desirability of the responses, but the sample only collects information from a specific geographic location, so these findings cannot extrapolate to populations with very marked cultural differences. Finally, the study did not measure other validity evidence, such as predictive and concurrent validity, which should be considered in future studies.Practical implicationsFrom a practical perspective, the study offers a measurement scale with fewer items and robust psychometric evidence that ensures the fit of the data to the EGame-flow measurement scale. Further research must continue to learn about the behavior of the EGame-flow scale in different samples that new evidence of psychometric properties continues to appear and that other factors associated with the users' gaming enjoyment experience are analyzed.Originality/valueThe value and originality of the study lie in the type of evidence of psychometric properties that the instrument has and particularly in the style of sample in which the study is carried out, in this case, in the context of Mexico, where there are not enough instruments that measure the flow experience of users.

A novel fractionalized investigation of tuberculosis disease

ABSTRACT In this study, we investigate novel fractional tuberculosis model with Caputo fractional derivative. The computational solution of the fractional tuberculosis disease is obtained with the help of the generalized Euler's method (GEM). This article considers two treatment strategies: protective treatment for latent populations and main treatment for infected ones. The compartmental structure of the six-dimensional model includes the susceptible, latent, infected, recovered, and treatment classes. To examine the nature of differential equations of fractional order that arise in biological illness, the findings produced utilizing the method under consideration are more accurate and straightforward. Additionally, the stability of the equilibrium point is also investigated. The fractional model has been graphically simulated using MATLAB22.

Research on Decoupling Model of Six-Component Force Sensor Based on Artificial Neural Network and Polynomial Regression.

A two-stage decoupling model based on an artificial neural network with polynomial regression is proposed for the six-component force sensor load decoupling problem in the case of multidimensional mixed loading. The six-dimensional load categorization stage model constructed in the first stage combines 63 load category label sets with a deep BP neural network. The six-dimensional load regression stage model was constructed by combining polynomial regression with a BP neural network in the second stage. Meanwhile, the six-component force sensor with a fiber Bragg grating (FBG) sensor as the sensitive element was designed, and the elastomer simulation and calibration experimental dataset was established to realize the validation of the two-stage decoupling model. The results based on the simulation data show that the accuracy of the classification stage is 93.65%. The MAPE for the force channel in the regression stage is 6.29%, and 3.24% for the moment channel. The results based on experimental data show that the accuracy of the classification stage is 87.80%. The MAPE for the force channel in the regression phase is 5.63%, and 4.82% for the moment channel.

A novel six-dimensional digital twin model for data management and its application in roll forming

Roll forming is a cost-effective, efficient, and flexible plastic processing technique that involves the gradual transverse bending of metal strips using sequentially arranged forming dies. It is an important technological approach in response to lightweight, energy efficiency, and safety in various sectors, including new energy vehicles, aerospace, and rail transportation. However, the complexity of the process, diverse data types, and the effects of coupling and nonlinearity have led to challenges in process stability and quality control. The unclear impact mechanism of heterogeneous time-series data from multiple sources on product quality significantly hinders the development and widespread adoption of roll forming in industries. To facilitate the study of its mechanisms and optimize the control of forming quality, this paper introduces a digital twin (DT) model tailored for the roll forming field. It also presents a product-oriented feature data management framework based on the DT model. This framework facilitates feature-based data categorization across the complete lifecycle, enabling advanced data analysis in the roll forming domain. The feasibility and advantages of the proposed model and framework are validated through application to produce hat-shaped components. It is hoped that the work can provide valuable insights to the digitalization and intelligent transformation of roll forming field.

Conceptualization and measurement of owner BIM capabilities: from a project owner organization perspective

PurposeBuilding Information Modeling (BIM) capabilities have been studied at the individual, project, organizational, and even industry levels to ensure the realization of BIM value in the architectural, engineering, construction, and operation industry. However, limited research has focused on a project owner organization perspective to investigate owner BIM capabilities that are required to ensure effective project management and delivery. This present study aims to propose an indicator framework to evaluate owner BIM capabilities at the organizational level.Design/methodology/approachLeveraging the existing literature on BIM capabilities and synthesizing insights from the resource-based view and information technology capabilities research, this study conceptualizes the BIM capabilities of project owner organizations and offers a framework of indicators for measurement. Semi-structured interviews with BIM experts and a questionnaire survey were conducted to identify key indicators affecting owner BIM capabilities. A six-dimensional structural equation model with 29 indicators was then established.FindingsThe findings highlight the multidimensionality of owner BIM capabilities and show that process capabilities play a crucial role in enhancing owner BIM capabilities, while technical capabilities are considered as the least important aspect.Research limitations/implicationsThe study sheds light on the key role of project owner organizations in ensuring BIM value and suggests that project owners focus more on the organizational processes of introducing BIM in managing projects.Originality/valueThis study reconceptualizes owner BIM capabilities drawing on the idea of resource-based view and information technology capabilities and highlights the important dimensions and indicators of owner BIM capabilities at the organizational level.

Measuring Occupational Well-Being Indicators: Scale Construction and Validation.

The purpose of this study is to carry out the scale development of occupational well-being (OWB) (affective, professional, social, cognitive, psychological and psychosomatic well-being) in Laos. Using multiple sampling data, we developed a valid OWB scale with a large Laotian sample (n = 1745). The validity of the OWB-47 scale was analyzed using exploratory factor analysis (EFA). Cross-validity, the initial model, and confirmatory factor analysis (CFA) were performed using Stata 19 to assess the validity of the scale development. Consistent with the valid model, the CFA revealed a unidimensional structure in the OWB scale. The initial measurement of the OWB scale was significantly correlated with the measure of the six-dimensional model. Regarding the full model testing, the CFA model was developed to test the validity of the OWB-47 scale, suggesting the acceptability of the fit model.

Application of Neural Radiance Fields (NeRFs) for 3D Model Representation in the Industrial Metaverse

This study explores the utilization of Neural Radiance Fields (NeRFs), with a specific focus on the Instant NeRFs technique. The objective is to represent three-dimensional (3D) models within the context of the industrial metaverse, aiming to achieve a high-fidelity reconstruction of objects in virtual environments. NeRFs, renowned for their innovative approach, enable comprehensive model reconstructions by integrating diverse viewpoints and lighting conditions. The study employs tools such as Unity, Photon Pun2, and Oculus Interaction SDK to develop an immersive metaverse. Within this virtual industrial environment, users encounter numerous interactive six-dimensional (6D) models, fostering active engagement and enriching the overall experience. While initial implementations showcase promising results, they also introduce computational complexities. Nevertheless, this integration forms the basis for immersive comprehension and collaborative interactions within the industrial metaverse. The evolving potential of NeRF technology promises even more exciting prospects in the future.

Validity, reliability, and invariance across sex of a German version of the Behavioral Regulation in Exercise Questionnaire.

Since there is no scientifically validated German version of the Behavioral Regulation in Exercise Questionnaire (BREQ-3), the aim of this study was to assess its psychometric parameters and invariance across sex in a sample of German-speaking young adults. The BREQ-3 is an instrument measuring the social and internal influences of motivation toward exercising. This tool is widespread within the scientific community and has been validated in several languages. A total of 271 participants (45% women; mean age = 20.67 ± 2.17 years; effect size ≥ 0.5) filled in the BREQ-3 at one time point, with a small sub-sample (n = 37) responding it a second time after 15 days. Confirmatory Factorial Analysis, Structural Modeling, and Intraclass Correlation Coefficient were used to examine the German version of the questionnaire. Results highlighted a good fit of the six-dimensional model after the removal of two items (CFI = 0.912; SRMR = 0.0594; RMSEA = 0.064), as well as full invariance across sex (pχ2 = 0.218; ΔCFI < 0.01). Internal consistency and reliability were moderate to good. The 22-item German BREQ-3 is a scientifically valid instrument that can be used in cross-national studies dealing with social aspects of exercise behaviors.

Human and nonhuman norms: a dimensional framework.

Human communities teem with a variety of social norms. In order to change unjust and harmful social norms, it is crucial to identify the psychological processes that give rise to them. Most researchers take it for granted that social norms are uniquely human. By contrast, we approach this matter from a comparative perspective, leveraging recent research on animal social behaviour. While there is currently only suggestive evidence for norms in nonhuman communities, we argue that human social norms are likely produced by a wide range of mechanisms, many of which we share with nonhuman animals. Approaching this variability from a comparative perspective can help norm researchers expand and reframe the range of hypotheses they test when attempting to understand the causes of socially normative behaviours in humans. First, we diagnose some of the theoretical obstacles to developing a comparative science of social norms, and offer a few basic constructs and distinctions to help norm researchers overcome these obstacles. Then we develop a six-dimensional model of the psychological and social factors that contribute to variability in both human and potential nonhuman norms. This article is part of the theme issue 'Social norm change: drivers and consequences'.

Modified fractional order social media addiction modeling and sliding mode control considering a professionally operating population

&lt;abstract&gt;&lt;p&gt;With the advancement of technology, social media has become an integral part of people's daily lives. This has resulted in the emergence of a new group of individuals known as "professional operation people". These individuals actively engage with social media platforms, taking on roles as content creators, influencers, or professionals utilizing social media for marketing and networking purposes. Therefore, in this article, we designed a six-dimensional fractional-order social media addiction model (FOSMA) in the sense of Caputo, which took into account the professional operations population. Initially, we established the positivity and boundedness of the FOSMA model. After that, the basic regeneration number and the equilibrium points (no addiction equilibrium point and addiction equilibrium point) were computed. Then, the local asymptotic stability of the equilibrium points were proved. In order to investigate the bifurcation behavior of the model when $ R_0 = 1, $ we extended the Sotomayor theorem from integer-order to fractional-order systems. Next, by the frequency analysis method, we converted the fractional order model into an equivalent partial differential system. The tanh function was introduced into the scheme of sliding mode surface. The elimination of addiction was achieved by the action of the fractional order sliding mode control law. Finally, simulation results showed that fractional order values, nonlinear transmission rates, and specialized operating populations had a significant impact on predicting and controlling addiction. The fractional-order sliding mode control we designed played an important role in eliminating chatter, controlling addiction, and ensuring long-term effectiveness. The results of this paper have far-reaching implications for future work on modeling and control of fractional-order systems in different scenarios, such as epidemic spread, ecosystem stabilization, and game addiction.&lt;/p&gt;&lt;/abstract&gt;

PROPERTI PSIKOMETRI BAHASA INDONESIA DARI KUALITAS HIDUP TERKAIT KESEHATAN TERHADAP REMAJA (KINDL-R)

Adolescence and smoking present a significant public health concern, with estimates suggesting 30% of global smokers fall within this age group. Smoking negatively impacts adolescents' daily lives across physical, psychological, and social domains, collectively known as Health-Related Quality of Life (HRQoL). This study aimed to evaluate the psychometric properties of the KINDL-R HRQoL instrument for Indonesian children and adolescents. The study involved 206 adolescents aged 14-17 years in Bandung, recruited using purposive sampling. Results of the development of the Indonesian version of the KINDL-R adaptation using Confirmatory factor analysis (validity) supported a six-dimensional model (p &lt; .05). Internal consistency reliability (reliability) was good, with Cronbach's alpha values = 0.92. Test-retest reliability was also satisfactory, with item discrimination values exceeding 0.40 for most items. These findings suggest that the KINDL-R demonstrates good reliability and validity for assessing HRQoL in Indonesian adolescents.

Psychometric evaluation of the United States-adapted perceived perioperative competence scale-revised: A national survey

BackgroundNursing competence is integral to ensuring patient safety, especially in high-risk environments such as the operating room. Instruments which facilitate self-assessment of specialty specific nursing competence allow nurses to gain important insights into their practice to facilitate continuous growth in their professional practice. Currently, there are no psychometrically tested tools to assess perioperative competence applicable to the United States context. ObjectiveTest the psychometric properties of the Perceived Perioperative Competence Scale-Revised in the United States context. MethodsA cross-sectional survey design was used. Perioperative nurses were recruited via four professional associations and the survey was administered online. Construct validity of the six-dimensional Perceived Perioperative Competence Scale-Revised was tested using a multidimensional item response theory model known as the graded response model. Measurement invariance was assessed relative to years of perioperative experience. Internal consistency was estimated using McDonald's Omega and Cronbach's alpha reliability coefficients. ResultsResponses from a total of 1,581 participants were analyzed in the psychometric analysis. The six-dimensional graded response model of the Perceived Perioperative Competence Scale-Revised displayed satisfactory model fit for the sample (Chi-square(df) = 5,699.09(725); root mean square error of approximation = 0.066, 90% confidence interval: 0.064, 0.067; comparative fit index = 0.955; Tucker-Lewis index = 0.952; standardized root mean squared residual = 0.045). Scalar invariance was established when assessing the psychometric equivalence of the scale across years of perioperative experience (<10 years, ≥ 10 years) (Chi-square(df) = 5,785.29(1,573); root mean square error of approximation = 0.058, 90% confidence interval: 0.057, 0.060; comparative fit index = 0.959; Tucker–Lewis index = 0.959). Reliability across the six subscales ranged from alpha = 0.87 - 0.94 and Omega = 0.93 - 0.97. ConclusionsResults suggest that the Perceived Perioperative Competence Scale-Revised is suitable to use with perioperative nurses practicing in clinical settings in the United States. Measurement invariance testing indicates the scale is measuring the same construct and is being interpreted in a conceptually similar manner across groups based on years of perioperative experience.

Exploring the development of students’ sports interests in college smart physical education based on cluster analysis

Abstract This paper firstly analyzes the smart campus, gives the six-dimensional assessment model of the smart campus, and understands that college smart sports are a new type of manifestation of smart sports in colleges and universities. Secondly, it constructs the intelligent sports education platform model of colleges and universities based on cluster analysis and utilizes the K-means algorithm to calculate the metrics and analyze the data by clustering. Finally, taking the college students majoring in physical education at University B as the research object, the students’ sports interest cluster analysis was conducted based on the cluster analysis of the student’s application of the smart sports education platform. The results show that the horizontal coordinate K=3 tends to level off, which is the recommended optimal K value, and the clustering can be completed by using the K-Means clustering method, and the average standardized scores of each clustered variable of athletic students’ sports interests are the highest, and all of them are positive, which are 0.6, 1.2, 1, 0.8, and 0.85, respectively.

Ground state wavefunctions of elliptic relativistic integrable Hamiltonians

We derive ground state eigenfunctions and eigenvalues of various relativistic elliptic integrable models. The models we discuss appear in computations of superconformal indices of four-dimensional theories obtained by compactifying six-dimensional models on Riemann surfaces. These include, among others, the Ruijsenaars-Schneider model and the van Diejen model. The derivation of the eigenfunctions builds on physical inputs, such as conjectured Lagrangian across dimensions IR dualities and assumptions about the behavior of the indices in the limit of compactifications on surfaces with large genus/number of punctures/flux.

The 18-item Swedish version of Ryff's psychological wellbeing scale: psychometric properties based on classical test theory and item response theory.

Psychological wellbeing is conceptualized as the full engagement and optimal performance in existential challenges of life. Our understanding of psychological wellbeing is important for us humans to survive, adapt, and thrive during the challenges of the 21st century. Hence, the measurement of psychological wellbeing is one cornerstone for the identification and treatment of both mental illness and health promotion. In this context, Ryff operationalized psychological wellbeing as a six-dimensional model of human characteristics: self-acceptance, positive relations with others, environmental mastery, personal growth, autonomy, and purpose in life. Ryff's Psychological Wellbeing Scale has been developed and translated into different versions. Here, we examine and describe the psychometric properties of the 18-item Swedish version of Ryff's Psychological Wellbeing Scale using both Classical Test Theory (CTT) and Item Response Theory (IRT). The data used in the present study was earlier published elsewhere and consists of 768 participants (279 women and 489 men). In addition to the 18-item version of the scale, participants answered the Temporal Satisfaction with Life Scale, the Positive Affect Negative Affect Schedule, and the Background and Health Questionnaire. We examined, the 18-item version's factor structure using different models and its relationship with subjective wellbeing, sociodemographic factors (e.g., education level, gender, age), lifestyle habits (i.e., smoking, frequency of doing exercise, and exercise intensity), and health issues (i.e., pain and sleeping problems). We also analyzed measurement invariance with regard to gender. Moreover, as an addition to the existing literature, we analyzed the properties of the 18 items using Graded Response Model (GRM). Although the original six-factor structure showed a good fit, both CTT and IRT indicated that a five-factor model, without the purpose in life subscale, provided a better fit. The results supported the internal consistency and concurrent validity of the 18-item Swedish version. Moreover, invariance testing showed similar measurement precision by the scale across gender. Finally, we found several items, especially the purpose in life's item "I live life one day at a time and do not really think about the future," that might need revision or modification in order to improve measurement. A five-factor solution is a valid and reliable measure for the assessment of psychological wellbeing in the general Swedish population. With some modifications, the scale might achieve enough accuracy to measure the more appropriate and correct six-dimensional theoretical framework as detailed by Ryff. Fortunately, Ryff's original version contains 20 items per subscale and should therefore act as a perfect pool of items in this endeavor.

Dynamics of Competitive Two-Strain Stochastic SIR Epidemics on Heterogeneous Networks

Mathematical modeling in epidemiology, biology, and life sciences requires the use of stochastic models. In this paper, we derive a competitive two-strain stochastic SIR epidemic model by considering the change in state of the epidemic process due to an event. Based on the density-dependent process theory, we construct a six-dimensional deterministic model that can be used to describe the diffusion limit of the stochastic epidemic on a heterogeneous network. Furthermore, we show the explicit expressions for the variances of infectious individuals with strain 1 and strain 2 when the level of infection is increasing exponentially. In particular, we find that the expressions of the variances are symmetric. Finally, simulations for epidemics spreading on networks are performed to confirm our analytical results. We find a close agreement between the simulations and theoretical predictions.

A fractional-order tuberculosis model with efficient and cost-effective optimal control interventions

This study is concerned with applying fractional calculus in modelling tuberculosis (TB) transmission dynamics. A new six-dimensional fractional-order mathematical model in the sense of the Caputo derivative operator is formulated to capture memory effects in the spread process of tuberculosis. Some key features such as vaccination, forces of TB infection, and exogenous re-infection by two actively infectious classes are considered. Banach fixed point theory is employed to prove the existence and uniqueness of a solution for the fractional-order model. Equilibrium points and effective reproduction number of the model are determined. Analysis reveals that the model exhibits backward bifurcation where stable TB-free and TB-present equilibrium points co-exist when the effective reproduction number is below unity. Some sensitive parameters of the model are identified to help design intervention measures against the disease. Hence, four time-dependent controls, namely advocacy effort against infection and re-infection, vaccination control, prophylaxis against latent infection, and treatment control, are considered to form a fractional-order optimal control model. The controls are characterized using Pontryagin’s maximum principle. Efficiency and cost-effectiveness assessments are conducted to quantify the most efficient and cost-effective intervention plan to minimize TB spread in the population. Behaviours of the fractional-order TB model at varying values of the order of the Caputo derivative are extensively explored to show memory effects in the transmission dynamics of the disease.