Graphical Modeling Research Articles

METHODOLOGY OF TEACHING ELEMENTS OF COMBINATORICS AT THE COMPREHENSIVE SECONDARY SCHOOL

The object of the study is the elements of combinatorics at the comprehensive secondary school. The aim of the article is to improve the methodological system of teaching elements of combinatorics at the comprehensive secondary school. The article discusses methodological issues and problems of studying combinatorics elements in the process of teaching mathematics. The methodological features of studying combinatorics elements at the comprehensive secondary school are identified and characterized, their role in the course of mathematics from the point of view of improving the effectiveness of learning is considered. The methods and methodical techniques of teaching combinatorial material, which were previously tested in our educational process, are presented. The possibilities and expediency of their application in connection with the age peculiarities of students are substantiated. Methodological guidelines for teaching individual topics of combinatorics are given. Within the framework of teaching each topic, interesting tasks of applied significance are presented. Effective methods of their solution are given. The presented tasks have been tested for years in our teaching practice. In the process of teaching the elements of combinatorics, methods of cooperative learning, mathematical modeling, graphical modeling, coding, methods that promoting inter- and intra-subject connections were used.

Logical-semantic models and methods of knowledge representation: cases for energy management systems and SMR digital infrastructures

The subject of this article is the development of information technologies at the end of the 20th and the beginning of the 21st century for the Fourth Industrial Revolution in the form of Industry 4.0, i.e., Internet of Things (IoT) technologies. Successes in the implementation of this technology have led to the development of new applications in the energy sector, such as energy management systems, small modular reactor (SMR) management systems, and alternative power supply systems based on renewable energy sources. The digital infrastructure of these management systems is characterized by a high "density of knowledge", which requires clarification of fundamental concepts in information theory, namely the content of the concepts "data", "information", "knowledge" and "meaning of knowledge". Special attention was given to defining the role of knowledge. The aim of this study is to further develop methods and models of semiotic theory by determining the role and place of logical as well as eight- and four-factor logical-semantic models of knowledge bases in semiotic space. The tasks: comparing existing knowledge representation methods and models. Research results: We found that the logical models used in the development of knowledge bases are based on the principles of artificial intelligence theory, which relies on sign system hypotheses and formal logic theory. The main drawback is the complexity of practical implementation in the form of expert systems. For logical-semantic models in the form of eight-vector graphic models, it was found that there is currently no theoretical justification for defining the vectors that form the coordinate axes, making these models unique to specific subject areas. It was determined that the advantage of using these methods is that an expert can independently form such a knowledge model. For logical-semantic models in the form of four-factor graphic models, there is a theoretical justification for defining the factors of the model that form the coordinate axes, making these models universal for specific subject areas. It was established that the advantage of these models is that they can be developed by experts without the involvement of a knowledge engineer. Therefore, it is proposed to use four-factor logical-semantic knowledge representation models for further application. It is also proposed to split the element "logical-semantic models" into two elements in the semiotic spatial model in vector K8 "Knowledge Representation Models", namely: "logical-semantic eight-vector models" and "logical-semantic four-factor models". Additionally, it is proposed to add the element "post-Cartesian representation of meta-knowledge" to the element "geometric" in vector K5 "Ideal Models". Conclusions: The theoretical basis for developing eight-factor logical-semantic knowledge representation models is the form of connections between adjacent vectors in the form of Cartesian products on elements of the corresponding inter-coordinate matrices. The theoretical basis for the methodology of developing four-factor logical-semantic knowledge representation models is the form of connections between adjacent vectors in the form of Cartesian products for elements of the corresponding inter-coordinate matrices, as well as for diametrically opposite pairs of factors in the form of dialectical unity of the concepts "general" and "particular." The application of logical-semantic knowledge representation models for alternative energy-source management systems will ensure increased energy efficiency. Other cases related to the development of databases for SMR digital infrastructure are discussed.

Root cause localization for wind turbines using physics guided multivariate graphical modeling and fault propagation analysis

Under the background of widespread utilization of wind energy, the improvement of power generation efficiency and the reduction of operation and maintenance costs have increased the demand for anomaly detection and localization of wind turbines through fault propagation analysis, while the large-scale data collected by SCADA systems provide sufficient data support and promising assistance for this purpose. It is an effective and cost-saving solution to this demand to construct a high-precision wind turbine digital twin model under normal operating conditions based on multivariate monitoring data, and to realize root cause localization based on prediction deviation and backtracking analysis. This paper proposes a root cause localization framework for wind turbines based on explicit-implicit knowledge fusion and multivariate time-series graph neural networks, fully utilizing the inter-variable dependencies mining capability of graph structure learning and the temporal prediction advantage of time-series graph neural networks. Five types of explicit knowledge based on explicit rules are constructed to describe the explicit relationships between monitoring variables. The fused knowledge (i.e., graph adjacency matrix) is obtained by fusing the explicit knowledge with the implicit knowledge hidden in the data through a quasi-hard-attention mechanism based on the idea of graph structure learning. A high-precision digital twin model for wind turbines based on fused knowledge is constructed using the multivariate time-series graph neural network. The anomaly degree of monitoring variables’ state is defined based on the prediction deviation, with which the propagation of fault's state over time can be accurately described, and the root cause localization through backtracking analysis can be achieved. Case studies are conducted using field fault data from a wind farm, a sugar factory and a thermal power plant. The results show that the proposed framework can provide a solution for studying the fault propagation process and root cause localization, also demonstrating certain robustness and generalizability.

REENGINEERING THE EDUCATIONAL PROCESS IN HIGHER EDUCATION INSTITUTIONS ON A DUAL BASIS

The purpose of the paper is to present the authors' conclusions and practical recommendations regarding the restructuring of the educational process of higher education institutions – providers of educational services in partnership with specialised companies. Methodology. The following methods were used: structural-functional method – in the study of the internal structure of the educational process, based on the principles of duality, where each component has a corresponding purpose in the value chain of intellectual capital; graphical modelling – to systematically reflect the author's views on relevant aspects of the research. Results. The sequence of the reengineering cycle and the directions it should take have been defined. It is emphasised that reengineering includes the competences acquired by the recipients of educational services, their communication with teachers, curators from the company, teaching methods and technologies that make up the instrumental mix, the structure of which depends on the mix of expected professional competences. Practical implications. It is emphasised that an applicant should have the right to make an independent choice between the forms of education, taking into account his/her economic circumstances and plans for future professional life in a democratic country. For each of the options, guidelines are proposed for completing the standard number of classroom hours, independent work and other forms of academic activity.

Dynamic Bayesian-Network-Based Approach to Enhance the Performance of Monthly Streamflow Prediction Considering Nonstationarity

In recognizing the pervasive nonstationarity of hydrometeorological variables, a paradigm shift towards alternative analytical methodologies is imperative for refining hydroclimatic data modeling and prediction. We introduce a novel approach leveraging nonstationary Graphical Modeling and Bayesian Networks (NGM-BNs) tailored for hydrometeorological applications. Demonstrated through monthly streamflow forecasting in the Kashgar River Basin of China, our method illuminates the temporal evolution of network relationships, underscoring the dynamism inherent in both input variables and modeling parameters. The key to our approach is identifying the most suitable time horizon (MST) for model updates, which is intricately problem-specific and crucial for peak performance. This methodology not only unveils changing predictor significance across varying flow conditions but also elucidates the fluctuating temporal links between variables, especially under the lens of climate change, for instance, the growing impact of snowmelt on the Kashgar Basin’s streamflow. Compared to stationary counterparts, our nonstationary Bayesian framework excels in capturing extreme events by adeptly accommodating temporal shifts, outperforming traditional models including both stationary and nonstationary variants of Support Vector Regression (SVR) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS).

Network analysis of the proteome and peptidome sheds light on human milk as a biological system

Proteins and peptides found in human milk have bioactive potential to benefit the newborn and support healthy development. Research has been carried out on the health benefits of proteins and peptides, but many questions still need to be answered about the nature of these components, how they are formed, and how they end up in the milk. This study explored and elucidated the complexity of the human milk proteome and peptidome. Proteins and peptides were analyzed with non-targeted nanoLC-Orbitrap-MS/MS in a selection of 297 milk samples from the CHILD Cohort Study. Protein and peptide abundances were determined, and a network was inferred using Gaussian graphical modeling (GGM), allowing an investigation of direct associations. This study showed that signatures of (1) specific mechanisms of transport of different groups of proteins, (2) proteolytic degradation by proteases and aminopeptidases, and (3) coagulation and complement activation are present in human milk. These results show the value of an integrated approach in evaluating large-scale omics data sets and provide valuable information for studies that aim to associate protein or peptide profiles from biofluids such as milk with specific physiological characteristics.

Intelligent Identification of Chinese and Australian Merino Wool Fibers Based on Image Recognition

In order to promote the sustainable growth of the wool industry and protect consumers' legitimate rights, rapid identification of the country of origin for wool of the same type is deemed crucial. This research presents a computer graphic recognition training model that utilizes median and Wiener filtering techniques to effectively reduce noise in the raw wool fiber images. Employing a support vector machine as the classifier and integrating a polynomial kernel function, this model achieves swift and accurate identification of Chinese and Australian Merino wool fibers. Experimental results underscore that following image recognition training, the model attains an impressive 92.5% comprehensive and precise identification rate for Chinese and Australian Merino wool fibers, effectively distinguishing the origin of wool from the same category. This approach not only provides a valuable reference for identifying the origin of similar wool types but also holds the potential to standardizing the wool fibre material market and assuring the consumer’s confidence on wool products.

Meeting the challenges and strategies regarding malaria elimination: qualitative evaluation of perceptions from a local population in Colombia

“One Health” and “Social Determinants of Health” approaches are crucial for implementation of sustainable strategies for the control and elimination of infectious diseases such as malaria. These approaches highlight the importance of the social component in the design of elimination strategies, as well as the communities’ involvement in the implementation of these measures at local levels. Qualitative analysis approaches for malaria research can be useful for assessing population acceptance of intervention measures and for addressing negative perceptions that can threaten the success of control goals by undermining the community’s trust in interventions. In this paper, we explore some observations and interpretations from the qualitative content analysis of two open-ended questions about malaria, from a survey conducted between September and November 2016 in Guapi, a high risk transmission area in the Colombian Pacific Region. Using the spatial transmission unit concept for the analysis of the conditions and factors involved in malaria transmission under the “One Health” and “Social Determinants of Health” approaches, we reveal that responders’ perceptions reflects both the need and requirement for continuous assistance to solve local problems and also a comprehensive problem-solving effort by the government that includes basic sanitation. From the analyses, we present a graphic model that illustrates how to address the problem of malaria control and elimination from the perspectives of the community’s perceptions in Guapi, highlighting the need for joint efforts of community, academy, and government to achieve the goal of controlling and eliminating malaria in the region.

Predicting game popularity through graphic modeling

Predicting game popularity through graphic modeling

Integration of digital health applications into the German healthcare system: development of "The DiGA-Care Path".

Since 2019 people who have insured in the German statutory health insurance are entitled to use certified apps called the Digitale Gesundheitsanwendungen [Digital Health Applications (DiGAs)]. The prerequisite for this is that an app certified as DiGA and suitable for their diagnosis exists. The DiGA can then either be prescribed by a physician or psychotherapist or requested by the patient from the statutory health insurance fund. Given the novelty of this type of healthcare, the implementation of a DiGA should be closely monitored to identify potential weaknesses and achieve quality improvements. To enable an analysis of the supply of DiGAs step-by-step, we aimed to create the DiGA-Care Path. We conducted three steps to create the DiGA-Care Path. First, a knowledge base was created based on a structured literature research matched with knowledge gathered from the superordinate research project "QuaSiApps" funded by the German Federal Joint Committee. Second, we aimed to create an "ideal-typical" DiGA-Care Path using a flowchart. Third, based on the first path, a final path was developed using the graphical modeling language "Event-Driven Process Chain." The DiGA-Care Path was developed to depict the supply of DiGAs in Germany. The final path is constituted by a "main path" as well as a corresponding "sub-path". While the "main path" focuses more on the supply environment in which a DiGA is used, the "sub-path" depicts the supply delivered by the DiGA itself. Besides the process itself, the paths include relevant actors to indicate responsibilities for individual process steps. The DiGA-Care Path helps to analyze the current supply of DiGAs step-by-step. Thereby, each step can be investigated in detail to identify problems and to detect further steps where quality improvements can be enabled. Depending on the perspective, focused either on the supply environment, or the supply delivered by the DiGA itself, the "main path" or the "sub-path" can be used, respectively. Besides the potential of the DiGA-Care Path to improve the current supply of DiGAs, it can help as an orientation for international policymakers or further stakeholders either to develop their own integration of apps into healthcare systems or for international manufacturers to consider entering the German market.

Bidirectional graphics-based digital twin framework for quantifying seismic damage of structures using deep learning networks

Tremendous effort has been devoted toward developing automated post-earthquake inspection techniques, including automated image collection and damage identification. However, few studies have attempted to establish the complex relationship between visible damage and structural conditions. Moreover, the lack of training data further hinders the potential use of deep learning algorithms. This paper proposes a framework, termed Bidirectional Graphics-based Digital Twin (Bi-GBDT), that allows for assessment of the structural condition based on post-earthquake photographic surveys. The procedure contains two parts: (i) A GBDT of a target structure, containing a computer graphics model and a finite element (FE) model, is developed (ii) Synthetic data subsequently is generated from the GBDT to train neural networks to predict the damage measures and structural conditions. To demonstrate the proposed approach, a seismically-designed reinforced concrete shear wall is considered. First, the GBDT of the shear wall is constructed and calibrated so that the associated damage patterns simulated by the corresponding FE model match well with the experimental results. Next, synthetic images of the damage patterns are created from the validated GBDT, along with the corresponding structural damage measures, and used to train Residual Neural Network and Conditional Generative Adversarial Networks to determine the maximum drift, the stress/strain fields and the structural condition. The neural networks trained on synthetic data are shown to perform well for the experimental data, confirming the proposed approach. Subsequently, the neural networks are tested on the synthetic data for a wide variety of loading conditions to demonstrate the robustness of the approach. In addition, the realistic images rendered from the GBDT are utilized as input to predict the structural condition, showcasing the comprehensive Bi-GBDT framework. These results demonstrate the efficacy of the proposed approach to generate accurate digital twins and point toward its future application for development of automated post-earthquake assessment strategies.

Identifying the risks of armed conflict impact on the ecosystem

The object of the study is the territory that became the object of the influence of hostilities. The study is dedicated to finding ways to assess the state of ecosystems and ensure their stability in the conditions of military conflicts. The impact of military conflict on the environment is highlighted through the analysis of risks and losses in ecosystems. It has been studied that the intensity of armed conflicts determines the scale and degree of damage to ecosystems, and the use of destructive weapons leads to the direct destruction of natural environments and their components. It has been determined by an expert survey that the most significant changes in natural ecosystems are the biodiversity losses, deforestation, territorial changes and the destruction of natural objects (scores ≥9.0). Factors of low impact (score <6.75) are air pollution, erosion due to air pollution, biodiversity losses due to water pollution. The interrelationships between military actions and environmental pollution are studied for the importance of understanding these processes for effective management of environmental security during military conflicts. A mathematical and graphic model of material flows has been developed, which allows taking into account resource flows, pollution and losses in the ecosystem during armed conflicts. The model, tested in the Kyiv region, made it possible to determine the priorities of conservation and risk management. Analysis of 9,000 km2 revealed contaminated areas, in particular, in the southwestern and western directions. The practical use of the research results is in the development of strategies for minimizing and restoring the ecological consequences of armed conflicts. This can help improve the management of natural resources in the context of military operations and increase the resilience of ecosystems

Health Workforce Planning: Designing a Specialty and Subspecialty Supply Model for Iran.

One of the approaches to health workforce planning is supply-based. It has been emphasized that countries should model health workforce based on evidence and their context. The objective of this study is to "design a supply health workforce planning model for specialty and subspecialty in Iran." This is a study using Walker and Avant's (2018) theory synthesis framework to construct the model. This method has three steps. According to the viewpoint of the research team and the needs of the country, the focal concept is determined. Then, a literature review was done to determine related factors and their relationships. In the third step, according to the review, the viewpoint of the research team, the rationale of the connection between components, and the graphic model were presented. "Supply" was selected as the focal concept. In the literature review, 42 components were obtained from the systematic review, 43 components obtained from the study of other texts were combined with the opinion of the research team about the field of Iran, and the connections between them were determined. In the third step, the supply model was designed using the Stock and Flow method. Finally, by applying the "functional full-time coefficient", the number of full-time equivalent physicians was calculated. The presented model is an evidence-based model that follows stock and flow design. Stock is the number of specialties or subspecialties that exist in the labor market. Flow includes inflow and outflow according to the educational pathway in the context of Iran.

BSDD FOR ARTWORKS IN HBIM OPEN AND STANDARD-ORIENTED DOCUMENTATION

Abstract. This contribution shows part of a research activity aimed at supporting data documentation related to artworks and decorative apparatuses that characterize the architectural heritage using bSDD as a methodological tool. The proposal approach aims to provide specific information in SCAN-to-BIM processes, implementing HBIM models of decorative apparatuses or artworks with data on the state of conservation and morphological-structural composition according to the material of each component of the system (Lanzara and Scandurra, 2023). The main goal of this activity is the creation of open and detailed information models shareable and consultable via IFC exchange format (ISO 16739:2018), supporting conservation, prevention, maintenance and restoration of historical-artistic heritage. The use of codes, manuals, vocabularies and thesauri, and the multidisciplinary knowledge of specialists, shared within the theoretical and operational framework of the restoration and conservation of cultural heritage, informally contribute to supplement existing codes and to implement/define new datasets for information about deficient and not codified conservation states for specific materials. The structuring of the Data Dictionary is supported by ACCA software's usBIM.bSDD application, accessible via a collaborative web-based platform (Scandurra and di Luggo, 2023). According to the proposed workflow, shared terms and definitions can be integrated within the graphic model of the manufacts and structured within a CDE – Common Data Environment. The system, which has already been tested in the context of historical architecture, is currently being tested on movable and immovable decorative apparatuses and artworks, selected for their constituent materials and compositional complexity. This contribution presents the experimentation on the decorative apparatus of the major altar of the Church of Santa Maria di Costantinopoli in Naples.

A Scoping Review of Food Systems Governance Frameworks and Models to Develop a Typology for Social Change Movements to Transform Food Systems for People and Planetary Health

Effective governance is essential to transform food systems and achieve the United Nations (UN) Sustainable Development Goals 2030. Different political ideologies and paradigms inhibit or drive social change movements. This study examined how food systems governance has been described. Thereafter, we reviewed graphic frameworks and models to develop a typology for civil society actors to catalyze social change movements to transform food systems for people and the planet. The scoping review involved (1) formulating research questions; (2) developing a search strategy to identify evidence from four English-language electronic databases and reports, 2010–2023; and (3–4) selecting, analyzing, and synthesizing evidence into a narrative review. Results yielded 5715 records, and 36 sources were selected that described and depicted graphic frameworks and models examined for purpose, scale, political ideology, paradigm, discourse, principles, governance, and democracy. Evidence was used to develop a graphic food systems governance typology with distinct political ideologies (i.e., neoliberal, reformist, progressive, radical); paradigms (i.e., maintain, reform, transition, transform); discourses (i.e., food enterprise, food security, food justice, food sovereignty); types of governance (i.e., multistakeholder, shared, self); and democracy (i.e., representative, participatory, deliberative). This proof-of-concept typology could be applied to examine how change agents use advocacy and activism to strengthen governance for sustainable diets, regenerative food systems, and planetary health.

Methodological aspects of assessing the level of risk resistance of retail trade enterprises

The article aims to improve the scientific and methodological approach to assessing retail enterprises' risk tolerance levels. The research process used the following methods: analysis, synthesis, structuring, systematization, graphic modeling, multidimensional analysis, and integral assessment. The article describes the main features of the modern external environment. Risk management is developing forward-looking thinking and forming readiness for risky situations. The essence of the "risk resistance" concept is revealed, and the main concepts that define it are characterized. A three-dimensional graphic model has been developed, and a methodological toolkit for assessing the level of risk tolerance of retail trade enterprises has been substantiated. An economic-mathematical model is proposed to estimate the probability of achieving a retail enterprise's target level of risk tolerance. A system of system-forming factors that influence the provision of the retail trade enterprise's target level of risk resistance has been formed. The scientific-methodical approach to assessing the level of risk resistance of a retail trade enterprise has been improved, considering the ability to resist external risks and the level of internal risks. It allows the formation of a system of management decisions to ensure a timely response to a wide range of resource and time constraints risks while contributing to achieving target indicators of activity retail trade enterprises. The graphic model of the values of the concepts of risk resistance of a retail trade company, developed based on the results of the study, focuses attention on the dynamics of the concept over time and creates an opportunity to ensure the control of the actual values of the concepts of risk resistance and determine their compliance with permissible, nominal, and critical values. Further research is planned to be devoted to conducting studies of current practices of external and internal risk management in Kharkiv's retail trade enterprises. Keywords: risk, risk resistance, level, risk-generating factors, concepts, integral assessment, integral indicator.

Factors influencing the work of researchers in Scientific Initiation: A systematic review protocol.

Scientific Initiation (SI) is an educational activity that allows students to begin their scientific training and research under the guidance of an experienced researcher. While several studies have examined students' perceptions of SI, research on the perspective of researchers working in this field is currently lacking. Thus, this study's aim is to describe the protocol design for conducting a systematic review. At the same time, the review aims also to identify factors influencing the work of researchers in SI and explore the motivations leading researchers to engage in research projects within institutions and their respective impacts. Literature search will be done using the bibliographic databases, including Academic Search Premier, APA PsycNet, CINAHL Plus, ERIC, SocINDEX, Scopus, and Web of Science. The search strategy was guided by the PICo framework (Population, phenomenon of Interest, and Context). The preparation and development of this protocol following guidelines were employed: Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Protocols 2015 (PRISMA-P 2015), Peer Review of Electronic Search Strategies 2015 (PRESS 2015), and PRISMA-Search (PRISMA-S). Original, peer-reviewed articles that examine the factors related to the work of researchers in SI will be included without any language or publication date restrictions. Qualitative, quantitative, and mixed-methods studies will be screened by two independent researchers. The included studies will be analyzed to identify factors, policies, and their impacts obtained analytically. Findings will be objectively categorized and synthetically represented through figures, diagrams, and graphic models. The risk of bias will be assessed using the Critical Appraisal Skills Program (CASP) and the Downs and Black checklists. A third senior reviewer will resolve any discrepancies. We aim to understand the factors that drive researchers to engage in SI research through the dissemination of the findings of this systematic review. This may aid the development of institutional strategies and actions that can support the enhancement of SI programs and encourage greater researcher participation.

Usefulness of the Ordinal Logistic Biplot: Analysis of the Path Taken towards a Circular Primary Sector in Spain

Population growth and greater global interconnection require a profound transformation in how we produce, consume, and manage natural resources. In this sense, the circular transition of the agricultural and livestock sectors is vital to guarantee adequate production without compromising the availability of resources for future generations. In this work, we analyze the level of circular development of the primary sector in Spain using a sample of the 84 largest companies. We utilize the biplot analysis, multivariate graphic models that represent the joint distribution of four scores (constructed with sixteen ecological initiatives), and three financial and geographic variables. These techniques allow for visualization of the status and the relationships between all of them. We evidence an important degree of progress in initiatives associated with the use of non-polluting energy, eco-design, eco-innovation, management, and care of ecosystems. We also observe that the current transition towards a circular primary sector is strongly associated with firms’ capital investments and long-term innovations. On the other hand, the actions aimed at protecting water resources are in an intermediate state of progress, being necessary to invest in additional friendly water initiatives. In this vein, it is advisable to promote public policies focused on promoting the ecological transition of this sector and the research that advances efficient water management.

Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey

In many fields of scientific research and real-world applications, unbiased estimation of causal effects from non-experimental data is crucial for understanding the mechanism underlying the data and for decision-making on effective responses or interventions. A great deal of research has been conducted to address this challenging problem from different angles. For estimating causal effect in observational data, assumptions such as Markov condition, faithfulness, and causal sufficiency are always made. Under the assumptions, full knowledge, such as a set of covariates or an underlying causal graph, is typically required. A practical challenge is that in many applications, no such full knowledge or only some partial knowledge is available. In recent years, research has emerged to use search strategies based on graphical causal modelling to discover useful knowledge from data for causal effect estimation, with some mild assumptions, and has shown promise in tackling the practical challenge. In this survey, we review these data-driven methods on causal effect estimation for a single treatment with a single outcome of interest and focus on the challenges faced by data-driven causal effect estimation. We concisely summarise the basic concepts and theories that are essential for data-driven causal effect estimation using graphical causal modelling but are scattered around the literature. We identify and discuss the challenges faced by data-driven causal effect estimation and characterise the existing methods by their assumptions and the approaches to tackling the challenges. We analyse the strengths and limitations of the different types of methods and present an empirical evaluation to support the discussions. We hope this review will motivate more researchers to design better data-driven methods based on graphical causal modelling for the challenging problem of causal effect estimation.

Geoscientific Analysis of Structural Collapses in the City of Juan C. Bonilla, Puebla, Mexico (2021)SES IN THE CITY OF JUAN C. BONILLA, PUEBLA, MEXICO (2021).

This paper presents a general analysis from the geosciences of structural collapses. The analysis was conducted in the crop fields of Santa María Zacatepec, in the city of Juan C. Bonilla, Mexico. The analysis implemented the Remote Sensing technique, facilitating the interpretation of band combinations and determining the geometry, geology, geomorphology and cartography of the site under study. A summary analysis of the work performed is presented, in addition to showing simulation models of the hydrological outflow and water acceleration in the runoff structural slopes, data that were used for the final mapping and graphic modeling. A geomorphological, geological, and topographical study of the site was conducted, using satellite and aerial telemetric techniques. Specialized software was also used for geodetic surveys, mapping in 2D and 3D models of the analysis area. Surveys of the site were carried out to determine the shape of the structure and identify potentially risk areas for the population near the collapse events. Orthophotos were obtained to calculate and determine the slope by distance-elevation, acceleration-height, water flow and force, which served to conclude the possible causes that led to the collapses and have sufficient technical data that were shared with the civil protection authorities.