Deductive Model Research Articles

Ultraviolet, Did the Cell See It from the Side or the Bottom? Assessment and Modeling of UV Effects on Cultured Cells Using the CL-1000 UV-Crosslinker

Numerous natural extracts and compounds have been evaluated for their ability to mitigate the adverse effects of ultraviolet (UV) overexposure. However, variability in the UV doses that trigger biological responses across studies likely arises from inconsistencies in UV exposure standardization. We hypothesize that these discrepancies are due to variations in culture plates and dishes. The UV dose (D) required to reduce cell viability by 50% differed by a factor of ten between 3.5 cm dishes and 96-well plates. Similarly, the EC50 dose for IL-6 release (D1/2) varied, potentially correlating with the surface area (S). UV exposure to wells with increasing height in 3.5 cm dishes resulted in a decrease in IL-6 release, suggesting that the greater the well height, the more it may influence UV exposure through reflection or shielding effects, thereby contributing to the physiological effects on the cells. To compare these differences among plates, we defined the height-to-diameter ratio (r). Analysis revealed a linear correlation between D1/2 and S in a log-log plot, and between D1/2 and r in a semi-log plot. From this, we defined two empirical indices σ and ρ for UV dose adjustment. A deductive model was also developed to derive a D′ value that adjusts UV doses without requiring training. As with σ and ρ, the UV dose D was effectively adjusted using D′ as well. These attempts suggest that D′ offers a foundational framework for evaluating UVB effects on cultured cells.

Fear and Public Safety From the Baumanian Perspective

Objective: The aim is to analyze, in view of baumanian ideas, the fear and public insecurity that affect the Brazilian population in the post-modern era, particularly in recent decades, a phenomenon present in the fluid society, which propagates uncertainty and determines the way of thinking, acting and feelings of individuals. Theoretical Framework: Zygmunt Bauman developed research into the complex web that involves individuals' personal and social ties, in which he produced studies on fear and (in)security, as well as coining the generic expression liquid life, characterized by a precarious routine, with uncertainty, devoid of of a defined and stable model, resembling a liquid. Method: A theoretical, critical and dialectical methodological research, of a bibliographic nature, is carried out, based on a qualitative study, in which a deductive model is used, in light of the contributions of the polish professor. Results and Discussion: It is hoped to provoke a level of discussion that leads to understanding and solutions regarding certain conflicts that exist in the social fabric, such as fear and insecurity that affect citizens. Research Implications: El tema investigado está lleno de interés público, porque involucra y afecta al Estado y a todos los segmentos de la sociedad; por lo tanto, no se pueden ignorar sus causas, efectos y consecuencias. Originality/Value: The approach used in the research is authentic and original, because there is no specific production on fear and Brazilian net public security, categories that impact the lives of citizens, which justifies the study of matters that affect cultural, political and social development from the country.

Building Character Through Teaching Literature to Children who are in Trouble with the Law

This research aims to build character through teaching literature to children who had problems with the law at the Special Children's Development Institute (LPKA) Maros, South Sulawesi. The research method used is descriptive qualitative with a field study approach with a deductive model in understanding the research problem. The data source in this research is the Head of the Child Convict Development Section of LPKA Maros and Andikpas (Correctional Students), totaling 45 men. The results of this research show that through teaching literature Andikpas' desires can be known. The most dominant desire while at LPKA is to gather with parents or family. From this literature teaching, it can be seen that Andikpas has the most impressive objects while at LPKA. These objects include a toothbrush (plate), bet ( beater used in table tennis), television, Al-Quran, books, guitar, and photos. These objects will be a reminder of the past to look to the future for Andikpas, to do better and even become creative individuals amidst advances in technology and information. Of the seven most impressive objects, Andikpas stated that the ompreng was the most dominant.

Applying the Parameters of Impacted Population and Gully Social Impact Factor to Prioritize Choice of Gully Erosion Intervention Programs in Resource Limited Settings: Lessons from World Bank Funded Intervention in Anambra State, Nigeria

Background: Following a presidential request for assistance in the gully erosion menacing many States in Nigeria, the World Bank intervened through the Nigeria Erosion and Watershed Management Program (NEWMAP) in 23 States including Anambra, from 2014 - 2022. This study examined NEWMAP’s intervention in order to learn crucial lessons to improve subsequent gully remediation interventions in Anambra State. Methods: A 7-man multidisciplinary focus group was constituted to study NEWMAP’s intervention in Anambra State, determine the most striking innovation in the intervention, and explore that innovation further. Data extraction, content analysis comparative analysis, rational decision framework and the deductive model were applied to make findings and draw conclusion. Results: The study found that: (i) the most striking innovation in NEWMAP’s intervention in Anambra State is the introduction of elaborate and comprehensive package of rehabilitation of impacted population. (ii). 13 major gully erosion sites were remedied totaling 32 544.87 meters long and directly impacting a population of 163 209 persons (iii). As at June 2021, 150 000 impacted population had benefitted from micro-credit grants to engage in alternative livelihood businesses that contributed in revamping the State’s economy. (iv). The 13 gully sites remedied had a wide range of gully social impact factor, the highest three values being 101 440; 67 587.5 and 41 778; while the lowest three values are 1 204; 1 694.7 and 3 186. Conclusion: The study concludes that elaborate and comprehensive package of rehabilitation of gully impacted population is an innovation in Anambra State introduced by NEWMAP; that gully impacted population is a good measure of vulnerability; and that the parameters of impacted population and gully social impact factor are sensitive, reliable and effective parameters to prioritize choice of gully intervention programs in resource-limited settings.

Prediction of Cleaning Loss of Rice Wheat Combine Harvester Based on Dynamic Bayesian

The cleaning loss rate is a crucial performance metric for rice-wheat combine harvesters. Most studies on the relationship between the cleaning operation parameters of the combined harvester and the cleaning loss rate lack research on the dynamic correlation between harvest losses and operational parameters, specifically focusing on the dynamic correlation between harvest losses and multiple operational parameters such as the cleaning loss rate. In this paper, we formulate a dynamic deductive model for the scenario of harvesting loss regulation involving multiple operational parameters, delving into the dynamic associations between cleaning loss and the regulation of multiple operational parameters and constructing dynamic Bayesian network prediction model. Through experimental analysis of the dynamic Bayesian network prediction model for cleaning loss rates, the comparison of results among the other three algorithms demonstrated that it was efficient for our DBN to predict the cleaning loss rate.

Neural Networks in Legal Theory

Abstract This article explores the domain of legal analysis and its methodologies, emphasising the significance of generalisation in legal systems. It discusses the process of generalisation in relation to legal concepts and the development of ideal concepts that form the foundation of law. The article examines the role of logical induction and its similarities with semantic generalisation, highlighting their importance in legal decision-making. It also critiques the formal-deductive approach in legal practice and advocates for more adaptable models, incorporating fuzzy logic, non-monotonic defeasible reasoning, and artificial intelligence. The potential application of neural networks, specifically deep learning algorithms, in legal theory is also discussed. The article discusses how neural networks encode legal knowledge in their synaptic connections, while the syllogistic model condenses legal information into axioms. The article also highlights how neural networks assimilate novel experiences and exhibit evolutionary progression, unlike the deductive model of law. Additionally, the article examines the historical and theoretical foundations of jurisprudence that align with the basic principles of neural networks. It delves into the statistical analysis of legal phenomena and theories that view legal development as an evolutionary process. The article then explores Friedrich Hayek’s theory of law as an autonomous self-organising system and its compatibility with neural network models. It concludes by discussing the implications of Hayek’s theory on the role of a lawyer and the precision of neural networks.

Development of an optimization model for a monitoring point in tunnel stress deduction using a machine learning algorithm

AbstractMonitoring of the mechanical behavior of underwater shield tunnels is vital for ensuring their long‐term structural stability. Typically determined by empirical or semi‐empirical methods, the limited number of monitoring points and coarse monitoring schemes pose huge challenges in terms of capturing the complete mechanical state of the entire structure. Therefore, with the aim of optimizing the monitoring scheme, this study introduces a spatial deduction model for the stress distribution of the overall structure using a machine learning algorithm. Initially, clustering experiments were performed on a numerical data set to determine the typical positions of structural mechanical responses. Subsequently, supervised learning methods were applied to derive the data information across the entire surface by using the data from these typical positions, which allows flexibility in the number and combinations of these points. According to the evaluation results of the model under various conditions, the optimized number of monitoring points and their locations are determined. Experimental findings suggest that an excessive number of monitoring points results in information redundancy, thus diminishing the deduction capability. The primary positions for monitoring points are determined as the spandrel and hance of the tunnel structure, with the arch crown and inch arch serving as additional positions to enhance the monitoring network. Compared with common methods, the proposed model shows significantly improved characterization abilities, establishing its reliability for optimizing the monitoring scheme.

Isometric fiber distribution in the roller drafting zone

Roller drafting, a key mechanical operation for attenuating slivers to a desired thickness, holds critical significance in the spinning processes of the textile industry. Within the drafting stage of spinning, the motion state of fibers is influenced by their intrinsic characteristics, the force applied, and the distribution of both fast-moving and slow-moving fibers. This research focused on man-made fibers, which are isometric, and studied their distributions in the roller drafting zone. In addition, the cut-off weighting method was incorporated to measure the fiber distributions in the drafting zone, the impact of drafting parameters on fiber distribution was discussed, and the associated fiber distribution curves were generated. Findings reveal that fiber distributions are highly affected by factors such as the fiber length, roller-setting, and draft ratio. Conversely, the input sliver linear density and doubling number exhibited negligible influence on the fiber distribution. The total fiber distribution curve appeared as a dichotomy, including a horizontal line and an oblique line. This study also presented a second-order regression model and a deduction model to predict the distribution of isometric fibers in the roller drafting zone.

Trailing World Health Assembly Resolution 73.1: Pathway to Quick and Accurate Determination of the Source of Outbreak of COVID-19 in Wuhan, China.

For more than four years now, the objective of determining the source of the outbreak of COVID-19 in Wuhan, China and the route of transmission of the infection to the human population has not been achieved despite existing capacity in molecular epidemiology and World Health Assembly Resolution mandating that objective. This study seeks to ascertain the reasons for failure to achieve that objective and to propose feasible alternative strategies to attain that objective quickly and accurately. Exploratory research was adopted, applying the tools of input-output device, data extraction, management by objective, rational decision approach and the deductive model. Fitting the objective of determining the source of the outbreak of COVID-19 as expected output, the actions of key players in the pandemic response (fitted as processors) were evaluated for consistence or otherwise in transforming data/inputs to achieve the output/objective. The study found that the World Health Assembly (WHA) Resolution 73.1 (9 (6)) mandating the World Health Organization (WHO) to pursue the objective of determining the source of the outbreak of COVID-19 was not diligently and committedly implemented; that the WHO and China have deliberately evaded that objective; that previous and ongoing studies focusing on the circulation of SARS-CoV-2 in other countries and similar themes are diversionary and unnecessary workload that depart from the core objective; and that adopting a five-point alternative strategy will ensure the attainment of the core objective within 14 days, including: (i) re-phrasing of the WHA mandate for specificity; (ii) subjecting all possible pathways of transmission to scrutiny; (iii) adopting “confirmed” genetic sequences of SARS-CoV-2 for subsequent use in molecular epidemiology; (iv) establishing a ‘red circle’ of 2 km radius from the Huanan wet market for top-priority epidemiologic investigation; (v) review of Resolution WHA73.1(9 (6)) at upcoming WHA to expressly prescribe unfettered right of access to the international study team and sanction for non-compliance. The study concludes that determining the source of the outbreak of COVI-19 in Wuhan, China is an important objective that should be accomplished within 14 days considering existing capacity in molecular epidemiology, if avoidable impediments are removed.

Matching visually observed 3D model to original reference object

In the development of a visual environment simulator or a thermal imager simulator, one of the challenges encountered when assessing the external appearance of engineering structures is the need to ensure the correspondence of the developed 3D model of a reference object to the original 3D object. Since creating a complete model is currently impractical, the criterion for assessment is based on the conformity of forming specified components of the cognitive model of a human observer necessary in their professional activities. Research has shown that to address this challenge, it is advisable to choose one of the artificial intelligence methods to entrust it with the task of evaluating the developed 3D model and minimize the subjectivity in the decision-making process of “whether the external appearance of the 3D model meets the client's requirements.” The article proposes using deductive reasoning in ChatGPT to assess the conformity of the external appearance of a 3D model to a specified 3D object by representing the model as incomplete sequences and evaluating its ability to complete them in accordance with conclusions made by humans.The suggested approach involves solving the problem based on scenarios where humans formulate logical conclusions from provided information, followed by the application of ChatGPT in processing the presented sequences of conclusions. A comparative analysis is presented to determine the extent to which ChatGPT demonstrates deductive reasoning and how closely it aligns with human deductive models. This allows for the creation of a model with deductive reasoning and contributes to discussions on the competencies of artificial intelligence.

How can population models contribute to contemporary pest management practices?

AbstractPopulation models provide a logical knowledge base before conducting laborious and expensive field experiments. Historically, two types of population models have been developed: highly realistic simulations and simple analytical models. Highly realistic simulations comprise a complicated systems model, whereas simple analytical models comprise various analytical models that focus only on the fundamental structure of the target pest population. Although both approaches have contributed to pest management science, each has limitations, poor predictability, and lacks substantial connections to reality. Assimilation by state-space modeling, in which observation and process models are jointly incorporated, is a good compromise between a simple model and reality in nature. In the big data era, artificial intelligence (AI), specifically aimed at high predictability, has recently become popular. If vital physical and biological records are automatically censored in the field with high precision, AI will produce the most plausible predictions, providing the best practical solution given our current knowledge. AI can be a powerful tool in the contemporary world; however, deductive modeling approaches are still important when considering the behavior of AIs and may also provide important insights to detect deficient information in the data.

INCREASING THE CAPACITY OF POSYANDU CADRES IN DETECTION EARLY DANGEROUS SIGNS OF PREGNANCY THROUGH EXPOSURE WARNENG BOOKLET

Intoduction : One of indicator of a country's health status is the Maternal Mortality Rate (MMR). Maternal mortality during pregnancy and childbirth and postpartum can theoretically be prevented, if pregnant women and the community understand how to detect and treat early warning signs when there are dangers. Methode: organizing training using deductive models, using the Warneng Booklet for posyandu cadres Aim: To increase the knowledge and skills of posyandu cadres in early detection of pregnancy danger signs and early management. Result : There is an increase in knowledge of early detection of pregnancy danger signs by 5.76 points from the average pre-test value of 2.47 to 8.23 points. Increased knowledge of the early management of pregnancy danger signs from the average pre- test score of 1.78 to 7.9 and there was an increase in the skills of the posyandu cadres in providing education to pregnant women, as much as 6% can be seen from the difference in the average number of Yes, no questions on the checklist which was from 4, 90% to 10.90% from 12 observation points. Conclusion: Deductive model tutoring using the Warneng Booklet can increase knowledge about early detection of danger signs and early management of pregnancy complications as well as skills in providing education by cadres to pregnant women

When "who I am" is under threat: Measures of threat to identity value, meanings, and enactment.

Although scholars across fields have studied threats to individuals' identities for their impact and ubiquity, the absence of standard scales has hindered the advancement of this research. Due to the lack of identity threat measures, the myriad existing propositions and models remain untested which may generate skepticism of the field. In the comparatively rare instances where deductive models have been tested, studies often suffer from methodological shortcomings related to the absence of a standard measure (e.g., the use of scales that tap into adjacent constructs) or an assumption of unidimensionality, despite recognition that identity threat can take various forms. Such shortcomings can yield inaccurate conclusions and threaten content validity. In response to these issues, we followed recommended steps to develop three measures capturing threats to identity value, meanings, and enactment. We rigorously validated these measures across different contexts: threats to teachers' work-related identity, to pregnant women's leader identity, and to organizational members' lesbian, gay, bisexual, transgender, queer or questioning identities. Our results provide evidence of the psychometric validity of the three measures and their applicability to different types of identities individuals hold. Using our measures, scholars will be able to further explore identity threat triggers and outcomes, the mechanisms underlying the effects of the three different types of threat on outcomes, and temporal dynamics. Researchers can also use our measures in designing interventions. Ultimately, this will allow management and applied psychology scholars to develop better guidance for organizations and employees dealing with the commonplace, yet difficult experience of identity threat. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Situational deductions and emergency decisions in response to food quality and safety emergencies in food production enterprises

Food quality and safety emergencies pose significant challenges to public health and safety, requiring effective emergency decision-making strategies. However, much scholarly attention has focused on the characteristics, evolutionary mechanism, and operational mechanism of emergencies, but those debates typically overlook the particularity of food production enterprises and the complexity, urgency, and harmfulness of such emergencies. Considering these, we explore the occurrence, development, evolutionary mechanism, and development process of food quality and safety emergencies from the perspective of food production enterprises. Then, taking Coca-Cola's chlorine pollution emergency as an example, we establish a situational deduction model using the Bayesian network. Subsequently, an emergency decision-making system is constructed, and emergency decision schemes are provided. Our findings show that food quality and safety emergencies occur when the entropy value exceeds the system threshold. The implementation of emergency decision-making is a process of energy exchange with the external environment to form a new dissipative structure. Scenario analysis is a cyclic process influenced by the emergency evolution, external environment, and emergency decision-making. Effective emergency decision-making requires a comprehensive analysis of the event situation, rational resource mobilization, and the use of professional knowledge and experience. This study provides a novel approach to understanding and managing food quality and safety emergencies in food production enterprises, emphasizing the need to consider the particularity and complexity of such emergencies when developing decision-making strategies. Our situational deduction model and emergency decision-making system provide a practical tool for enhancing emergency response capabilities, ensuring public health and safety, and maintaining brand image.

A novel approach to building digital twin transformers by combining virtual-real sensing: An example of degree of polymerization distribution

Digital twin (DT) in the power industry faces challenges due to complex structures and limited distributed sensors for power equipment. In this work, a novel approach combining virtual-real sensing (VRS) was used to build DT for transformers, taking the degree of polymerization (DP) distribution as an example. Temperature and moisture distributions were calculated, and a dynamic deduction model for DP distribution was established. The visualization of DP distribution was achieved through surface-rebuild. Results showed a 3.5% error in temperature distribution compared to monitoring data. Moisture distribution was concentrated in the cardboard and bottom solid insulation, reaching 3.2% in the 48th year. DP distribution pattern aligned with Cigre’s report, with the lowest value at the hot spot location of the LV windings, deduced to be 252.15 in the 48th year. This study provides a data base for realizing transformers DT and is a key step towards building a vital DT transformer.

LITERATURE LITERACY IN CHILD SPECIAL DEVELOPMENT INSTITUTIONS (LPKA) CLASS II MAROS, SOUTH SULAWESI

This study aims to apply literary literacy at the Class II Child Special Development Institute (LPKA) Maros, South Sulawesi. This research method is descriptive qualitative with a field study approach (field study) through a deductive model to understand the research problem. The source of the data in this study was Andikpas (Correctal Students), totaling 28 men. The results of this study indicate that Andikpas is able to convey empirical experiences in the coaching process they experience at LPKA through literary literacy. They all have the same hopes, namely they want to be free soon so they can be with their parents (family) and friends, they want to be free so they can continue their education, and they want to be free so they can work to help support their family. Of these three things, Andikpas hopes most dominantly, so that he wants to be free soon, is to gather with his parents.

Revealing Driving Factors of Urban O3 Based on Explainable Machine Learning

Driven by precursor emissions, meteorological conditions, and other factors, atmospheric ozone (O3) has become the main pollutant affecting urban air quality in summer. The current deductive models driven by physical and chemical mechanisms require a large number of parameters for the analysis of O3 pollution, and the calculation timeliness is poor. The data-driven inductive models are efficient but have problems such as poor explanation. In this study, an explainable model of data-driven Correlation-ML-SHAP was established to reveal the strongly correlated influencing factors of O3 concentration. Additionally, the machine learning ML module coupled with the explainable SHAP module was used to calculate the contributions of driving factors to O3 concentration, so as to realize the quantitative analysis of driving factors. The O3 pollution process in the summer of 2021 in Jincheng City was used as an example to carry out the application research. The results showed that the Correlation-ML-SHAP model could reveal and use strong driving factors to simulate O3 concentration and quantify influence contribution, and the ML module used the XGBoost model to achieve the best simulation accuracy. Air temperature, solar radiation, relative humidity, and precursor emission level were the strong driving factors of O3 pollution in Jincheng City in summer 2021, and the contribution weights were 32.1%, 21.3%, 16.5%, and 15.6%. The contribution weights of air temperature, solar radiation, and precursor emission level increased by 3.4%, 1.2%, and 1.2% on polluted days, respectively, and the contribution weights of precursor emission level rose to third place on polluted days. Each driving factor had a nonlinear interaction effect on O3 concentration. When the air temperature exceeded 24℃, or the relative humidity was lower than 70%, there was a 94.9% and 94.1% probability of positive contribution to O3 pollution, respectively. Under such meteorological conditions, ρ(NO2) exceeded 9 μg·m-3, or ρ(CO) exceeded 0.7 mg·m-3, and there was a 94.9% and 99.3% probability of positive contribution to O3 pollution, respectively. The southeast wind speed was lower than 5.8 m·s-1, or the south wind speed was lower than 5.3 m·s-1, both of which contributed positively to O3 pollution. The model quantitatively analyzed the influence contribution of various driving factors on urban O3 concentration, which could provide a basis for the prevention and control of urban atmospheric O3 pollution in summer.

A Study on the Creative Approach of Luo Zhenyu's Oracle Bone Calligraphy

In the history of Chinese culture, the oracle bone script, along with the Dunhuang documents, the Han jian of the Western region, and the Great Treasury of the Cabinet, are known as the four major discoveries of the twentieth century. The oracle bone script has a history of more than 3,000 years. In the field of philology and archaeology, its appearance has brought original materials for the examination of the origin of Chinese characters; in the field of art, its appearance is a great asset for the study of oracle bone script calligraphy. From the dimension of the study of the history of calligraphy, the oracle bone script already possesses the three basic elements of calligraphy: the brush, the knotted character, and the chapter, and is the earliest system of writing with a more mature system and aesthetic nature in the development of Chinese writing. From the perspective of the pen, the oracle bone script was carved with a knife on hard turtle shells or animal bones, so the lines of the script are mainly straight lines of uniform thickness, showing a slightly thicker middle and slightly thinner ends, showing the aesthetic characteristics of thinness and solidity, uprightness and sharpness, giving people a strong three-dimensional sense and visual impact. As a script, oracle bones have both their linguistic communication and their calligraphic artistry.We examine oracle bone calligraphy in terms of writing instruments, brushwork, and chapter forms. The purpose of this paper is to identify problems and analyze them in a step-by-step manner by using a logical tree model of deduction through writing practice and documentary evidence to explore the feasibility of oracle bone calligraphy from three aspects: writing materials, brush structure, and composition form

Judicial Commission Role to Handle Contempt of Court in Indonesia from the Perspective of Islamic Legal Thought

Contempt of Court still occurs in many courts in Indonesia. Therefore, the government established the Judicial Commission of the Republic of Indonesia to maintain the dignity and dignity of the courts in Indonesia. This study discusses how the Judicial Commission of the Republic of Indonesia plays a role in handling the Contempt of Court in Indonesia from the perspective of Islamic Legal Thought. This research is literature with a qualitative approach. Data was collected through literature studies in the form of laws, books, and journals. The collected data is analyzed descriptively with a deductive model. This research shows that the Judicial Commission has been essential in preventing the Contempt of Court in Indonesia. The Judicial Commission of the Republic of Indonesia carries preventive and repressive actions. Preventive actions are carried out to avoid the Contempt of Court, while repressive actions are carried out to deal with the Contempt of Court in Indonesia. In Islamic legal thought, the role of the Judicial Commission is according to the role of Diwan al-Hisbah and the rule of Islamic Law "dar' al-mafasid muqaddam ala jalb al-mashalih" (rejecting harm is prioritized over bringing benefit) and "al-dharar yuzal" (damage must be eliminated). The role of the Judicial Commission in resolving Contempt of Court cases can make courts a safe place to enforce the law in Indonesia.

A unified method for assessing the observability of dynamic complex systems

ProblemSystems theory applied to biology and medicine assumes that the complexity of a system can be described by quasi-generic models to predict the behavior of many other similar systems. To this end, the aim of various research works in systems theory is to develop inductive modeling (based on data-intensive analysis) or deductive modeling (based on the deduction of mechanistic principles) to discover patterns and identify plausible correlations between past and present events, or to connect different causal relationships of interacting elements at different scales and compute mathematical predictions. Mathematical principles assume that there are constant and observable universal causal principles that apply to all biological systems. Nowadays, there are no suitable tools to assess the soundness of these universal causal principles, especially considering that organisms not only respond to environmental stimuli (and inherent processes) across multiple scales but also integrate information about and within these scales. This implies an uncontrollable degree of uncertainty. MethodologyA method has been developed to detect the stability of causal processes by evaluating the information contained in the trajectories identified in a phase space. Time series patterns are analyzed using concepts from geometric information theory and persistent homology. In essence, recognizing these patterns in different time periods and evaluating their geometrically integrated information leads to the assessment of causal relationships. With this method, and together with the evaluation of persistent entropy in trajectories in relation to different individual systems, we have developed a method called Φ-S diagram as a complexity measure to recognize when organisms follow causal pathways leading to mechanistic responses. ResultsWe calculated the Φ-S diagram of a deterministic dataset available in the ICU repository to test the method's interpretability. We also calculated the Φ-S diagram of time series from health data available in the same repository. This includes patients' physiological response to sport measured with wearables outside laboratory conditions. We confirmed the mechanistic nature of both datasets in both calculations. In addition, there is evidence that some individuals show a high degree of autonomous response and variability. Therefore, persistent individual variability may limit the ability to observe the cardiac response. In this study, we present the first demonstration of the concept of developing a more robust framework for representing complex biological systems.