Cause-effect Models Research Articles

GOALNET: Interleaving Neural Goal Predicate Inference with Classical Planning for Generalization in Robot Instruction Following

Our goal is to enable a robot to learn how to sequence its actions to perform high-level tasks specified as natural language instructions, given successful demonstrations from a human partner. Our novel neuro-symbolic solution GOALNET builds an iterative two-step approach that interleaves (i) inferring next subgoal predicate implied by the language instruction, for a given world state, and (ii) synthesizing a feasible subgoal-reaching plan from that state. The agent executes the plan, and the two steps are repeated. GOALNET combines (i) learning, where dense representations are acquired for language instruction and the world state via a neural network prediction model, enabling generalization to novel settings and (ii) planning, where the cause-effect modeling by a classical planner eschews irrelevant predicates, facilitating multi-stage decision making in large domains. GOALNET obtains 78% improvement in the goal reaching rate in comparison to several state-of-the-art approaches on benchmark data with multi-stage instructions. Further, GOALNET can generalize to novel instructions for scenes with unseen objects. Source code available at https://github. com/reail-iitd/goalnet.

Games, hormones, and “dark” personalities: Dark tetrad and the effects of violent gaming on aggression, cortisol, and testosterone

We investigated how playing a violent versus non-violent video game affects cortisol and testosterone levels, whether these hormonal changes increase implicit aggressive cognition, and whether so-called Dark Tetrad personality traits (i.e., Machiavellianism, psychopathy, narcissism, everyday sadism) moderate these effects. Fifty-four men played either a violent or a non-violent video game for 25 min. Participants provided salivary samples at the beginning of the experiment (T1), after 25 min of gameplay (T2), and 20 min after gameplay ended (T3). In the violent condition, participants showed a significant decrease in cortisol levels (T1 to T2) and a significant negative trend in cortisol levels from T1 to T3. Moreover, higher Machiavellianism scores were related to a significantly stronger decrease in cortisol (T1 to T2) in this condition. In the non-violent condition, however, participants with higher scores in Machiavellianism had a higher increase in cortisol (T1 to T2). In contrast to changes in hormonal levels, there were no significant effects on implicit aggressive cognition. The present findings illustrate the complex interplay between personality, hormones, and game content, thus further specifying current notions on the effects of violent video games. Playing a violent video game can have a stress-reducing calming effect depending on personality traits such as Machiavellianism and the psychological need satisfaction associated with it. Also, the fact that VVG exposure was not automatically accompanied by an aggression-increasing effect proves that simple cause-effect models are not sufficiently specified without taking the underlying mechanisms into account.

Simulation of Supply Chain Resilience Model of the Pharmaceutical Industry in the Country in the Supply of Remdesivir Using System Dynamics Approach

Background: In the event of a crisis and epidemic of infectious disease, ensuring the proper and timely supply of necessary medicine is one of the main priorities of the health care system in any country. Therefore, the present study investigates the resilience of the Iranian medicine supply chain using the system dynamics simulation method in increasing the level of access to Remdesivir.
 Methods: This is a development-applied study to provide a model using the system dynamics approach, which first presents a rich image that is based on the model, and then cause-effect models appropriate to the observations made. It was structured on the behavior of the system and also inspired by valid theories. The effect of key factors affecting the supply chain resilience of the country’s pharmaceutical industry was designed and analyzed using a system dynamics approach using decision support system (DSS) Vensim software. The time horizon considered for this research was 5 years, from 2019 to 2023. To predict and simulate the system dynamics model, the data collected from the questionnaires and the interviews with experts in this field were used.
 Results: Based on the result of this study, it can be expected that by reducing the schedule pressure by 1 and 3 % during the time of the study, the resilience of the supply chain of remedesvir in the country upgraded to about 32% and 47%.
 Conclusions: According to the complexity of health care systems, it is difficult to recognize the interaction of different variables, therefore, the effect of interventions is not immediately recognizable and requires the passage of time. In addition, the majority of the factors influencing health care outcomes are nonlinear. Therefore, the use of simulation models can help to clarify the indirect behavior of complex health care problems.

Water-quality and ecosystem impacts of recreation in streams: Monitoring and management

There is limited published information on the impact of bathing on stream water quality and ecology, except on human pathogens and health. We investigated the relationships between environmental quality of streams and recreational activity at five sites in the Australian Wet Tropics. The streams normally had very low concentrations of nutrients and suspended solids (TSS), but concentrations fluctuated widely during spates, thereby causing difficulties in discriminating impacts. Daily bathing activity disturbed sediments causing an increase in TSS and turbidity, which greatly exceeded national guidelines for maintenance of aesthetic qualities. TSS returned to background levels overnight as bathing areas were flushed clean. Total nitrogen and phosphate concentrations also increased with bather numbers, and phosphate concentrations were directly proportional to bather density. Faecal coliform concentrations were elevated by bathers at one site. Ecological effects of bathers were equivocal and greater on algal than invertebrate assemblages. Water quality degradation, although transient, suggested that some sites were close to their carrying capacity for bathers. Our results show that water quality may vary with local conditions and that cost-effective monitoring and management require development of cause-effect models of water quality processes for each stream site.

PROBLEMS OF APPLICATION OF DECISION-MAKING MODELS IN THE REGIONAL ECONOMY UNDER CONDITIONS OF PARTIALLY RELIABLE INFORMATION

In this study, attention is drawn to the under-explored area of strategic content analysis and the development of strategic vision for managers, with the supporting role of interpreting visualized big data to apply appropriate knowledge management strategies in regional companies. The study suggests improved models that can be used to process data and apply solutions to Big Data. The paper proposes a model of business processes in the region in the context of information clusters, which become the object of analysis in the conditions of active accumulation of big data about the external and internal environment. Research has shown that traditional econometric and data collection techniques cannot be directly applied to Big Data analysis due to computational volatility or computational complexity. The paper provides a brief description of the essence of the methods of associative and causal data analysis and the problems that complicate its application in Big Data. The scheme of accelerated search for a set of causal relationships is described. The use of semantically structured models, cause-effect models and the K-clustering method for decision making in big data is practical and ensures the adequacy of the results. The article explains the stages of applying these models in practice. In the course of the study, content analysis was carried out using the main methods of processing structured data on the example of the countries of the world using synthetic indicators showing the trends of Industry 4.0. When assessing Industry 4.0 technologies by region, the diversity of country grouping attributes should be considered. Therefore, during the analysis, the countries of the world were compared in two groups. The first group - the results for developed countries are presented in tabular form. For the second group, the results are presented in an explanatory form. In the process of assessing industrial 4.0 technologies, statistical indicators were used: "The share of medium and high-tech activities", "Competitiveness indicators", "Results in the field of knowledge and technology", "The share of medium and high-tech production in the total value added in the manufacturing industry", “Industrial Competitiveness Index (CIP score)”. As a result, the rating of the countries was determined based on the analysis of these indicators. . The reasons for the difficulties of calculations when processing Big Data are given in the concluding part of the article. Keywords: K - clustering method, causal links, data point, Euclidean distance

Patient Safety Strategies in Psychiatry and How They Construct the Notion of Preventable Harm: A Scoping Review.

ObjectivesThe literature on patient safety in psychiatry has not been explored systematically in terms of what interventions are used, how they are used, and what type of (preventable) harm is targeted. The aims of this scoping review are to explore patient safety strategies used in psychiatry and determine how they construct the notion of preventable harm.MethodA scoping review of literature on patient safety in psychiatry published in English between 2000 and 2019 was conducted using Scopus, MEDLINE, PsycInfo, and CINAHL. Keywords of patient safety strategies and possible outcomes were coded from the results, discussion, or conclusion. Patient safety strategies were inductively categorized into themes according to the focus of the strategy.ResultsThe review introduces 7 focus areas of patient safety strategies identified within the psychiatric literature: “risk management,” “healthcare practitioners,” “patient observation,” “patient involvement,” “computerized methods,” “admission and discharge,” and “security.” The result shows that patient safety strategies mainly aim to reduce suicide, self-harm, violence, and falls and present a large diversity of measures, often aimed at reducing variability while increasing standardization.ConclusionsThe strategies that are supported in the literature to achieve safer psychiatry mainly arise from linear cause-effect models and rely on staff performance, competence, and compliance. Contemporary safety science acknowledges the performance variability of everyday normal work and sees risk as the dynamic migration of these daily activities. The field of psychiatry has not yet included this view of safety in the strategic actions to reduce preventable harm.

The Structural Equation Modeling of the Relationship Between Spirituality and Psychological Wellbeing Mediated by Spiritual Health

Background and Objectives: Spirituality is an individual’s effort and searches for meaning and purpose in life. Spirituality includes the connection with religious beliefs and some of the chosen beliefs, values, and duties that give meaning to life to achieve the desired perfection. This study aimed to model the structural equations of the relationship between spirituality and psychological wellbeing mediated by spiritual health. Methods: This was a descriptive and correlational study, especially of cause-effect modeling. The statistical population included students of higher education centers in Sanandaj City, Iran, in 2018. Of them, 464 subjects were selected as the research sample by cluster random sampling approach. Measurement tools included researcher-made spirituality questionnaires (2020), Ryff’s Scales of Psychological Well-Being (1980), and the Spiritual Well-Being Scale (Paloutzian & Ellison, 1983). Results: Data analysis was performed using Cronbach’s alpha coefficient, heuristic factor analysis, and confirmatory factor analysis by structural equation modeling. The results on the model of the relationship between spirituality, psychological well-being, and spiritual health indicated the acceptable desirability of the indicators of goodness of fit of the model (P≤0.01). Other results suggested that higher levels of spirituality were associated with psychological wellbeing and spiritual health. Conclusion: The present research data revealed an overlap between spirituality, psychological well-being, and spiritual health. Spirituality positively affected psychological wellbeing and spiritual health concerning individual and social adjustment

Extended active inference: Constructing predictive cognition beyond skulls.

Cognitive niche construction is the process whereby organisms create and maintain cause–effect models of their niche as guides for fitness influencing behavior. Extended mind theory claims that cognitive processes extend beyond the brain to include predictable states of the world. Active inference and predictive processing in cognitive science assume that organisms embody predictive (i.e., generative) models of the world optimized by standard cognitive functions (e.g., perception, action, learning). This paper presents an active inference formulation that views cognitive niche construction as a cognitive function aimed at optimizing organisms' generative models. We call that process of optimization extended active inference.

Capability of social life cycle assessment for analyzing the artisanal small-scale gold mining sector\u2014case study in the Amazonian rainforest in Brazil

PurposeGold extraction in the Amazonian rainforest is accompanied by ecological threats and social grievances, but at the same time, the artisanal small-scale gold mining sector (ASGM) provides a livelihood for many people. To address this tradeoff, this paper analyzed the social aspects and their possible relations by conducting a case study based on a Social Life Cycle Assessment (SLCA). This study seeks to determine whether SLCA is capable of reflecting the sector.MethodA literature-based guideline was used for collecting primary data during several field trips to the Tapajós Region in Brazil. This research instrument constituted the basis for information-oriented interviews and on-site observations. The SLCA categories used in this study were based on the United Nations Environment Programme and the Society of Environmental Toxicology and Chemistry (UNEP/SETAC) guidelines and the categories in the Fairmined Standard. In addition, secondary data obtained from the literature were used to provide insights into the sector. The data were analyzed using qualitative content analysis with both deductive and inductive approaches.Results and discussionThis study described the social aspects of the ASGM sector in the Tapajós Region, including the absence of the state, the illegality and informality of mining operations, the remote and rural area, and the organizational structure. In addition, the extraction methods, poor working conditions, and difficult living conditions were considered. Certain characteristics of the industry like unstable payments, worker movements, and low education levels were recognized, and thus, the relations among the social categories and rebound effects were identified. Several issues were proven to be key factors: unstable payments, autonomy, and the rebound effects of excavators. Complex relations among social issues but also towards ecological and economic issues do exist. The suitability of using the SLCA to reflect the ASGM sector was tested on this basis.ConclusionIn some respects, the SLCA had limitations, e.g., due to the nonlinear relation between working hours and the amount of extracted gold. The impacts of technology depend on the underlying definition that is used. The current lack of cause-effect models impedes the assessment of an overall picture that considers the relations among the social aspects and other elements of sustainability. A holistic view must be applied if ecological problems are to be solved.

THE ISSUE OF PRICING POLICY IN OIL AND GAS SECTOR: WHY OIL PRICES ARE RISING

The given article is dedicated to the current issues of pricing policy in the oil & gas sector. Particularly, it studies the factors, causes and consequences of oil prices rising. The research methodology of this paper is comparative analysis of the existing patterns of macroeconomic development, political differences and social issues within the global oil and gas industry providers. The given analysis is based on SWOT, PESTLE, cause-effect modeling, risk management techniques, as well as the five forces model by M. Porter detecting competitive advantages of the related countries. The authors also consider possible solutions and provide recommendations on how to improve the current global economic situation in the given field.

A caution about causation

Educational technology research, like all education research, is dominated by explicit or implicit claims of causation. The dominance of cause-effect models in research is not surprising, and for many it is unnoticed and unquestioned. However, regardless of the cause-effect model being applied or the methodology in measuring it, we are unable to detect cause-effect directly. It is in this context that we need to be cautious in our interpretations of educational technology interventions and their implications for the future. Claims of causation are unlikely to decrease in the face of the increasing calls for “evidence-based” policy and practice. With this in mind it is even more important to consider how we can resist deterministic or mechanical claims of cause and effect. This dilemma should not stop our drive for evidence based approaches, but it is a reminder that we need to take care in the rigour of our research, and equally, in the way we describe it.

Managing food security through food waste and loss: Small data to big data

This paper provides a management perspective of organisational factors that contributes to the reduction of food waste through the application of design science principles to explore causal relationships between food distribution (organisational) and consumption (societal) factors. Qualitative data were collected with an organisational perspective from commercial food consumers along with large-scale food importers, distributors, and retailers. Cause-effect models are built and “what-if” simulations are conducted through the development and application of a Fuzzy Cognitive Map (FCM) approaches to elucidate dynamic interrelationships. The simulation models developed provide a practical insight into existing and emergent food losses scenarios, suggesting the need for big data sets to allow for generalizable findings to be extrapolated from a more detailed quantitative exercise. This research offers itself as evidence to support policy makers in the development of policies that facilitate interventions to reduce food losses. It also contributes to the literature through sustaining, impacting and potentially improving levels of food security, underpinned by empirically constructed policy models that identify potential behavioural changes. It is the extension of these simulation models set against a backdrop of a proposed big data framework for food security, where this study sets avenues for future research for others to design and construct big data research in food supply chains. This research has therefore sought to provide policymakers with a means to evaluate new and existing policies, whilst also offering a practical basis through which food chains can be made more resilient through the consideration of management practices and policy decisions.

Artificial Intelligence Techniques for Predicting and Mapping Daily Pan Evaporation

In this study, Artificial Intelligence techniques such as Artificial Neural Network (ANN), Model Tree (MT) and Genetic Programming (GP) are used to develop daily pan evaporation time-series (TS) prediction and cause-effect (CE) mapping models. Ten years of observed daily meteorological data such as maximum temperature, minimum temperature, relative humidity, sunshine hours, dew point temperature and pan evaporation are used for developing the models. For each technique, several models are developed by changing the number of inputs and other model parameters. The performance of each model is evaluated using standard statistical measures such as Mean Square Error, Mean Absolute Error, Normalized Mean Square Error and correlation coefficient (R). The results showed that daily TS-GP (4) model predicted better with a correlation coefficient of 0.959 than other TS models. Among various CE models, CE-ANN (6-10-1) resulted better than MT and GP models with a correlation coefficient of 0.881. Because of the complex non-linear inter-relationship among various meteorological variables, CE mapping models could not achieve the performance of TS models. From this study, it was found that GP performs better for recognizing single pattern (time series modelling), whereas ANN is better for modelling multiple patterns (cause-effect modelling) in the data.

The modeling and solution of course of action generation based on IN-DEA

The course of action (COA) generation is the course of designing the task planning flow, which is one of the key supporting techniques while the military organization executing aerial operation. Due to the outstanding advantage of influence nets (IN), describing the causal logic between random variable, this proposal will introduce IN into the fast building mechanism of COA model. The COA generation model utilize influence nets is established. The probability propagation method is designed, which is based on influence intensity. An improved differential evolution algorithm (IDEA) is designed to solve the action policy optimized selected model. At last, the simulated result of aerial arrack campaign case show the action policy optimized selected in variety influence constants can improve the capability of cause-effect modeling, and the improved differential evolution algorithm has fine convergent and optimized capability.

The explanatory significance of wholes: How exclusive reliance on antecedent-consequent models of explanation undermines the study of persons

Psychology has long labored under a mechanistic view of persons as reducible to parts (i.e., traits) that dictate human functioning. Efforts to study persons holistically—as embodied wholes embedded in the world—have resuscitated the study of personhood and its development, overhauling linear cause-effect models of psychological functioning in favor of emergence-focused, dynamic process alternatives rooted in the concept of persons as necessarily constituted within interactive context. Focused on agency and self-determination, the study of personhood also calls for an appreciation of the explanatory significance of persons as persons, as unified wholes who preserve their own organization in the face of ceaseless exchange with the world. Fully adopting this important vantage point for understanding persons, however, is only possible by expanding notions of scientific explanation beyond the temporal framework of antecedent-consequent, parts-to-whole relations in order to embrace a person's wholeness itself as a legitimate mode of explanation for understanding functioning.

Designing of Fuzzy Logic Controller for Set-Point Weight Tuning of PID Controllers

In the present study an Adaptive Neuro-Fuzzy Inference system (ANFIS) has been used to model the relationship between rainfall and runoff with lumped data. Intermittent runoff river system namely Kanand river in Maharashtra state, India is taken as the case study. The river has runoff response only during monsoon months (June to November). After finding the cross correlation between the rain gauge stations and runoff station; cause effect, combined and time series ANFIS models were developed using lumped data. All the ANFIS models were trained by varying the number of membership functions from one to five and it is found that all the ANFIS models performed better for three membership functions. Out of 276 monthly data sets, it is found that 70 % (193 data sets) of length for training and 30 % (83 data sets) of length for validation captured the trend better than any other combinations. It is also found that pure cause-effect ANFIS models are performing better than the time series and combined ANFIS models for the same number of time lagged input data sets.

Designing of Fuzzy Logic Controller for Set-Point Weight Tuning of Pid Controllers

In the present study an Adaptive Neuro-Fuzzy Inference system (ANFIS) has been used to model the relationship between rainfall and runoff with lumped data. Intermittent runoff river system namely Kanand river in Maharashtra state, India is taken as the case study. The river has runoff response only during monsoon months (June to November). After finding the cross correlation between the rain gauge stations and runoff station; cause effect, combined and time series ANFIS models were developed using lumped data. All the ANFIS models were trained by varying the number of membership functions from one to five and it is found that all the ANFIS models performed better for three membership functions. Out of 276 monthly data sets, it is found that 70 % (193 data sets) of length for training and 30 % (83 data sets) of length for validation captured the trend better than any other combinations. It is also found that pure cause-effect ANFIS models are performing better than the time series and combined ANFIS models for the same number of time lagged input data sets.

Cause-effect Relationships Between Objective and Subjective Measures of Quality of Life in Lithuania Municipalities

The Quality of Life (QoL) in the local place concept anticipates that overall individually perceived QoL is being constituted both by objective local living conditions as well as subjective individual experience and evaluation of these local place's external QoL factors. However, the mathematical-statistical research based scientific evidences about some certain context related direction and strength of relationships between objective local place related conditions (as a cause side) and subjective QoL perception (as an effect side) are lacking. By taking into account specific characteristics, i.e. the extent and nature of the data available to measure the objective and subjective QoL, the article explores methodological possibilities to model statistically probable cause-effect relationships established and empirically observed in Lithuania municipalities’. Such statistical probability based cause-effect models would be extremely valuable in building and justifying empirical observation based and any single municipality local place context relevant QoL improvement strategies.

Social sciences research in neglected tropical diseases 1: the ongoing neglect in the neglected tropical diseases

Centuries of scientific advances and developments in biomedical sciences have brought us a long way to understanding and managing disease processes, by reducing them to simplified cause-effect models. For most of the infectious diseases known today, we have the methods and technology to identify the causative agent, understand the mechanism by which pathology is induced and develop the treatment (drugs, vaccines, medical or surgical procedures) to cure, manage or control.Disease, however, occurs within a context of lives fraught with complexity. For any given infectious disease, who gets it, when, why, the duration, the severity, the outcome, the sequelae, are bound by a complex interplay of factors related as much to the individual as it is to the physical, social, cultural, political and economic environments. Furthermore each of these factors is in a dynamic state of change, evolving over time as they interact with each other. Simple solutions to infectious diseases are therefore rarely sustainable solutions. Sustainability would require the development of interdisciplinary sciences that allow us to acknowledge, understand and address these complexities as they occur, rather than rely solely on a form of science based on reducing the management of disease to simple paradigms.In this review we examine the current global health responses to the 'neglected' tropical diseases, which have been prioritised on the basis of an acknowledgment of the complexity of the poverty-disease cycle. However research and interventions for neglected tropical diseases, largely neglect the social and ecological contextual, factors that make these diseases persist in the target populations, continuing instead to focus on the simple biomedical interventions. We highlight the gaps in the approaches and explore the potential of enhanced interdisciplinary work in the development of long term solutions to disease control.

Development of automatic fault tree synthesis system using decision matrix

Fault trees synthesis, the basis for fault tree analysis (FTA), serves as a powerful tool for risk analysis. It has become a trend to accomplish computer-assisted fault tree synthesis in the field of system safety engineering because conventional manual construction of fault trees can be extremely time-consuming and vulnerable to human errors. This paper expounds upon a fault tree synthesis information system (FTSIS) developed by means of decision matrix for the purpose of its application to process plants, where the objective system is decomposed into a series of system components whose cause-effect models are constructed and stored in the relational database and transferred into decision matrix. The fault tree is synthesized automatically after the decision matrix is fully searched in FTSIS, the availability of which has been verified after it was put into effect successfully.