Cause-effect Chain Research Articles

End-To-End Latency of Cause-Effect Chains: A Tutorial

In many applications of cyber-physical systems, a sequence of tasks is necessary to perform a certain functionality. For example, from a sensor to an actuator, the first task reads the sensor value (cause), the second task processes the data, and the third task produces an output for the actuator (an effect is triggered). For such scenarios, the end-to-end timing properties (the so-called end-to-end latency) of the sequence of tasks (the so-called cause-effect chain) are of importance. This tutorial recaps different metrics for the end-to-end latency of cause-effect chains, and summarizes fundamental properties and existing analytical results in a systematic manner. To that end, this tutorial has a special focus on the reaction time (how fast can a reaction be in the worst case) and the data age (how old is the data source of an actuation in the worst case). The goal of this tutorial is to provide a systematic view of the fundamental end-to-end timing properties of cause-effect chains and offer an outlook of possible research directions in the near future. Furthermore, we extend the proof of one fundamental property in the literature to comply with the current state-of-the-art definition of end-to-end latencies.

Spatially Explicit Impact of Land Use Changes in the Bay Area on Anthropogenic Phosphorus Emissions and Freshwater Eutrophication Potential.

Land use changes significantly impact anthropogenic phosphorus (P) emissions, their migration to a water environment, and the formation of freshwater eutrophication potential (FEP), yet the spatiotemporally heterogeneous relationships at the regional scale have been less explored. This study combines land use classification, P-flow modeling, spatial analysis, and cause-effect chain modeling to assess P emissions and P-induced FEP at a fine spatial resolution in Guangdong-Hong Kong-Macao Greater Bay Area and reveals their dynamic responses to land use changes. We find that land conversion from cultivated land to impervious land corresponded to an increase in P emissions of 4.1, 1.8, and 0.5 Gg during 2000-2005, 2005-2010, and 2010-2015 periods, respectively, revealing its dominant but weakening role in the intensification of P emissions especially in less-developed cities. Expansion of aquacultural land gradually became the primary contributor to the increase in both the amount and intensity of P emissions. Land conversions from cultivated land to impervious land and from natural water bodies to aquacultural land led to 35.9% and 25.3% of the increase in FEP, respectively. Our study identifies hotspots for mitigating the environmental pressure from P emissions and provides tailored land management strategies at specific regional development stages and within sensitive areas.

Extracting Evidence from Health IT Studies to Populate Logic Models.

Logic models graphically present the socio-technical components of a variety of 'programs' such as educational programs. They show the underlying logic and assumptions of how a program is supposed to work. We suggest that they can be used to describe the mechanisms of complex socio-technical health IT interventions. To assess the suitability of logic models to describe cause-effect chains of health IT. We are currently conducting an integrative review of the impact of patient portals on patient outcomes. We extracted the following elements of logic models from the found publications: resources, activities, output, outcome, and impact. These factors are then used to populate the logic model and form a structured graphical representation of the evidence. Until now, all the evidence we found could be fit into the logic model. The logic model was able to accommodate diverse types of evidence. Logic models seem to be suitable for representing evidence on the impact of health IT.

Risk evaluation for oil tanker berthing and handling operations using the improved DEMATEL-ISM approach

The berthing and handling operation of oil tankers is an important task in the operation of oil ports. Therefore, it is crucial to identify the risk factors in the process of the berthing and handling operation of oil tankers, to explore the relationship between the influencing factors of the risk, and to find out the key factors that incur risk. In this paper, the risk factors of oil tankers’ berthing and handling operations are identified through on-site investigation, interviews, questionnaire surveys and expert consultation. Aiming at the uncertainty of the weight of each risk index during oil tanker berthing and handling, a machine learning algorithm (i.e. Random Forest Weighting) is applied. In this sense, the reliability analysis is used to calculate the generalization error of expert scoring data, and the weight of each index of each evaluation item is obtained according to the minimum error rate, which reduces the impact of subjective weighting in the identification of cause-effect chain components. In order to examine the internal relationship among various factors, the Interpretive Structural Model (ISM) of risk factors is constructed. Furthermore, we analyse the hierarchical relationship between various factors, clarify the mechanism of risk generation, and propose strategies to improve the safety management level of oil tanker terminals. Results show that the used algorithm could identify the key risk factors in the risk oil tanker terminal operations, and provide more effective managerial recommendations.

Probabilistic Reaction Time Analysis

In many embedded systems, for instance, in the automotive, avionic, or robotics domain, critical functionalities are implemented via chains of communicating recurrent tasks. To ensure safety and correctness of such systems, guarantees on the reaction time, that is, the delay between a cause (e.g., an external activity or reading of a sensor) and the corresponding effect, must be provided. Current approaches focus on the maximum reaction time, considering the worst-case system behavior. However, in many scenarios, probabilistic guarantees on the reaction time are sufficient. That is, it is sufficient to provide a guarantee that the reaction does not exceed a certain threshold with (at least) a certain probability. This work provides such probabilistic guarantees on the reaction time, considering two types of randomness: response time randomness and failure probabilities. To the best of our knowledge, this is the first work that defines and analyzes probabilistic reaction time for cause-effect chains based on sporadic tasks.

OpenBIM-based assessment for social cost of carbon through building life cycle

Buildings produce many carbon emissions in their life cycle, which leads to global warming, human disease, ecosystem destruction, and food shortages. These harms are translated and revealed into the social cost of carbon (SCC), but assessing the building SCC considering protection objects is still difficult due to the lack of adequate and rapid tools. Therefore, this paper proposes a method based on OpenBIM to calculate the SCC through the building life cycle. This method reflects the cause-effect chains of carbon emissions on human health, ecosystem quality, and social assets under LIME, EPS, and ReCiPe approaches. An original tool, NMBIM, is developed based on this proposed method, which calculates the SCC of a case building by directly importing industry foundation classes (IFC) files. The case results show that the maximum life cycle SCC reaches 5872.4 RMB/m2, which exceeds the construction cost. Although SCC results obtained by different LCIA models vary widely, the ones of human health are the most concerned protection objects. This paper provides an effective method for standardly storing and optimizing SCC at the project design stage, which contributes to construction industry planning, construction company decision-making, and public regulation.

Compositional Timing Analysis of Asynchronized Distributed Cause-effect Chains

Real-time systems require the formal guarantee of timing constraints, not only for the individual tasks but also for the end-to-end latency of data flows. The data flow among multiple tasks, e.g., from sensors to actuators, is described by a cause-effect chain, independent from the priority order of the tasks. In this article, we provide an end-to-end timing-analysis for cause-effect chains on asynchronized distributed systems with periodic task activations, considering the maximum reaction time (MRT) (i.e., the duration of data processing) and the maximum data age (MDA) (i.e., the worst-case data freshness). We first provide an analysis of the end-to-end latency on one local electronic control unit (ECU) that has to consider only the jobs in a bounded time interval. We extend our analysis to globally asynchronized systems by exploiting a compositional property to combine the local results. Throughout synthesized data based on an automotive benchmark as well as on randomized parameters, we show that our analytical results improve the state-of-the-art.

Characterization of nitrogen emissions for freshwater eutrophication modelling in life cycle impact assessment at the damage level and urban scale

Human interventions mobilize massive nitrogen (N) emissions to freshwater and result in serious eutrophication impacts. Life cycle impact assessment (LCIA) provides a reliable quantitative tool to address the challenges of potential ecological impact induced by increasing nutrient inputs. However, characterization procedures that relate N emissions to ecological damage in freshwater ecosystems in LCIA methodology are still lacking. This study aims to propose a spatially explicit characterization model (1 km × 1 km) for N-induced freshwater eutrophication. A complete cause-effect chains linking the N emission with species loss endpoint impact was constructed, in which the site-specific fate factor modelling contained transfer and removal processes in terrestrial and freshwater compartments, and the mechanistic modelling of ecosystem exposure and damage effect were quantified with species loss. The eutrophication characterization was conducted with a case study in Guangzhou, China, and 1 km × 1 km spatial differentiation of eutrophication susceptibility was intuitively demonstrated, with higher values in the Pearl River Estuary area. Method comparisons and further improvements were discussed in support of model generalization and further application.

A Review on Human–Computer Interaction Education Based on Active Learning using the DEMATEL Method

The objective of the study was to explore how web lectures could be used to enhance the learning experience in an introductory human-computer interaction course. The researchers introduced web lectures as a method to deliver lecture material prior to class, allowing more interactive learning activities during in-class sessions instead of passive listening. A quasi-experiment was conducted over a 15-week semester, involving 46 students who were divided into two sections of the same course. One section utilized web lectures, while the other followed traditional lectures. To ensure fairness, several control measures were implemented, including the same instructor for both sections and blind grading. The findings revealed that the section using web lectures achieved significantly better grades compared to the section with traditional lectures. Moreover, students in the web lecture section reported increasingly positive attitudes towards this approach throughout the course. This research provides a twofold contribution: firstly, it demonstrates a new way of utilizing existing technology to enhance learning outcomes, and secondly, it presents a comprehensive, quasi-experimental evaluation of web lectures in the specific context of the course. Various undergraduate courses incorporate the field of human-computer interaction (HCI) and emphasize a theoretical approach that encompasses the entire process of creating effective interfaces for end users. However, instructors encounter difficulties in keeping students engaged during the introduction of these concepts and encouraging practical experience in HCI. The aim of this study is to propose a teaching and learning method that enhances student retention and promotes a deeper understanding of HCI. This method consists of two components. The first part focuses on explaining the theoretical concepts of the discipline, while the second part involves implementing problem-based learning (PBL) within The DEMATEL method involves a systematic process of analyzing the relationships between different factors or variables in a system. It aims to uncover the cause-and-effect chains and understand the influence and impact each factor has on others. By employing DEMATEL, decision-makers can gain insights into the interdependencies among factors and determine which components play a significant role in shaping the system's behaviour. This method facilitates a comprehensive evaluation of the relationships, allowing for a more informed decision-making process. This model provides a clear representation of the complex system, highlighting the critical components and their influence on other factors. By visualizing and understanding the cause-effect chains, decision-makers can make informed choices and develop effective strategies for system improvement. Stability (ST), Speed (SP), System integration (SI), Aesthetic visualization (Av), Friendliness other learner (UF), interactive GUI(IG), interface flexibility (IF), User interaction with (IWS) Human–Computer Interaction Education Based on Active Learning Methods IWS is got the first rank whereas is the system integration is having the lowest rank. Human–Computer Interaction Education Based on Active Learning Methods IWS is got the first rank whereas is the system integration is having the lowest rank.

Exploring risks and benefits of overshooting a 1.5 °C carbon budget over space and time

Temperature targets of the Paris Agreement limit global net cumulative emissions to very tight carbon budgets. The possibility to overshoot the budget and offset near-term excess emissions by net-negative emissions is considered economically attractive as it eases near-term mitigation pressure. While potential side effects of carbon removal deployment are discussed extensively, the additional climate risks and the impacts and damages have attracted less attention. We link six models for an integrative analysis of the climatic, environmental and socio-economic consequences of temporarily overshooting a carbon budget consistent with the 1.5 °C temperature target along the cause-effect chain from emissions and carbon removals to climate risks and impact. Global climatic indicators such as CO2-concentration and mean temperature closely follow the carbon budget overshoot with mid-century peaks of 50 ppmv and 0.35 °C, respectively. Our findings highlight that investigating overshoot scenarios requires temporally and spatially differentiated analysis of climate, environmental and socioeconomic systems. We find persistent and spatially heterogeneous differences in the distribution of carbon across various pools, ocean heat content, sea-level rise as well as economic damages. Moreover, we find that key impacts, including degradation of marine ecosystem, heat wave exposure and economic damages, are more severe in equatorial areas than in higher latitudes, although absolute temperature changes being stronger in higher latitudes. The detrimental effects of a 1.5 °C warming and the additional effects due to overshoots are strongest in non-OECD countries (Organization for Economic Cooperation and Development). Constraining the overshoot inflates CO2 prices, thus shifting carbon removal towards early afforestation while reducing the total cumulative deployment only slightly, while mitigation costs increase sharply in developing countries. Thus, scenarios with carbon budget overshoots can reverse global mean temperature increase but imply more persistent and geographically heterogeneous impacts. Overall, the decision about overshooting implies more severe trade-offs between mitigation and impacts in developing countries.

Robust Cause-Effect Chains with Bounded Execution Time and System-Level Logical Execution Time

In automotive and industrial real-time software systems, the primary timing constraints relate to cause-effect chains. A cause-effect chain is a sequence of linked tasks and it typically implements the process of reading sensor data, computing algorithms, and driving actuators. The classic timing analysis computes the maximum end-to-end latency of a given cause-effect chain to verify that its end-to-end deadline can be satisfied in all cases. This information is useful but not sufficient in practice: Software is usually evolving and updates may always alter the maximum end-to-end latency. It would be desirable to judge the quality of a software design a priori by quantifying how robust the timing of a given cause-effect chain will be in the presence of software updates. In this article, we derive robustness margins which guarantee that if software extensions stay within certain bounds, then the end-to-end deadline of a cause-effect chain can still be satisfied. Robustness margins are also useful to know if the system model has uncertain parameters. A robust system design can tolerate bounded deviations from the nominal system model without violating timing constraints. The results are applicable to both the bounded execution time programming model and the (system-level) logical execution time programming model. In this article, we study both an industrial use case from the automotive industry and analyze synthetically generated experiments with our open-source tool TORO.

Asthma and atopic dermatitis as risk factors for rheumatoid arthritis: a bidirectional mendelian randomization study

BackgroundPrevious observational studies have shown an association between asthma, atopic dermatitis (AD) and rheumatoid arthritis (RA). However, the bidirectional cause-effect chain between asthma and AD and RA has not been proven yet.MethodsWe performed bidirectional two-sample Mendelian randomization (TSMR) and selected single nucleotide polymorphisms (SNPs) associated with asthma, AD, and RA as instrumental variables. All of the SNPs were obtained from the latest genome-wide association study in Europeans. Inverse variance weighted (IVW) was the main method used in MR analysis. MR-Egger, weighted model, simple model, and weighted median were used for quality control. The robustness of the results was tested by sensitivity analysis.ResultsAsthma was found to be the largest effect size for RA susceptibility using the IVW method (OR, 1.35;95%CI, 1.13–1.60; P, 0.001), followed by AD (OR, 1.10;95%CI, 1.02–1.19; P, 0.019). In contrast, there was no causal relationship between RA and asthma (IVW: P = 0.673) or AD (IVW: P = 0.342). No pleiotropy or heterogeneity was found in the sensitivity analysis.ConclusionFindings from this study showed a causal relationship between genetic susceptibility to asthma or AD and increased risk of RA, but do not support a causal relationship between genetic susceptibility to RA and asthma or AD.

Comparing Communication Paradigms in Cause-Effect Chains

A cause-effect chain is a sequence of multi-rate real-time tasks with data dependency. Cause-effect chains are generally subject to end-to-end timing constraints, especially in safety-critical systems. Communication paradigms greatly affect the end-to-end latency of cause-effect chains. This paper compares different communication paradigms (implicit communication, LET, DBP) with regards to the end-to-end latency of cause-effect chains using them, and proposes priority assignment strategies to optimize the end-to-end latency with specific communication paradigm. Experiments with synthesized data based on an automotive benchmark and randomly generated parameters are conducted to evaluate our results.

Root cause analysis using extended cause-effect chain analysis plus method for cleanroom relative humidity problem in the semiconductor industry

Root cause analysis using extended cause-effect chain analysis plus method for cleanroom relative humidity problem in the semiconductor industry

Genetic link between rheumatoid arthritis and autoimmune liver diseases: A two-sample Mendelian randomization study

BackgroundAn association between rheumatoid arthritis (RA) and autoimmune liver diseases (AILDs) was found in observational studies. However, neither the direction nor the cause-effect chain was clear. This study aimed to assess the causal associations between AILDs and RA. MethodsWe performed a two-sample Mendelian randomization (MR) analysis. Following a strict assessment, genome-wide association study (GWAS) datasets were used to select potential candidate single-nucleotide polymorphisms. The inverse-variance weighted (IVW) was used as the primary analysis approach, supplemented with four sensitive analysis methods applied to assess the robustness of the results. ResultsWe discovered that a genetically increased primary biliary cholangitis (PBC) risk had a positive causal effect on RA (IVW OR=1.149, 95% CI=1.063–1.241, P<0.001). According to the MR-Egger regression, horizontal pleiotropy was unlikely to impact causality (intercept = -0.028, P = 0.263). Using the leave-one-out strategy, sensitivity studies revealed that the MR analysis results were robust and reliable. Genetically determined primary sclerosing cholangitis (PSC) was not linked with the risk of RA (IVW OR=1.071, 95%CI=0.984–1.166, P = 0.111). The results of the MR analysis were further validated by sensitivity analyses utilizing the leave-one-out approach. In the other direction, there was no causal relationship between RA and PBC (OR=1.132, 95% CI=0.881–1.454, P = 0.333) or PSC (OR=1.067, 95% CI=0.891–1.279, P = 0.088). ConclusionsUsing a two-sample MR analysis, we investigated the relationship between AILDs and RA and revealed first that PBC increases the risk of RA. Large-scale cross-disease GWAS are required to further illuminate the genomic landscape of AILDs and RA.

Challenges and How to Overcome Them in the Formulation and Implementation Process of a Sustainability Balanced Scorecard (SBSC)

To cope with the increasing importance of sustainability, the Sustainability Balanced Scorecard (SBSC) has been developed to support companies integrating the economic, social and environmental dimensions of sustainability into their business. However, the formulation and implementation process of an SBSC is confronted with multiple challenges that have to be dealt with. This article associates the challenges with four steps of the process in particular: (1) the conceptual integration of the three dimensions into the strategy, (2) the selection of the architecture of the SBSC, (3) the formulation of sustainable key performance indicators and the development of cause-effect chains, and (4) the implementation of the SBSC. For these steps, the article summarizes and outlines the identified challenges, discusses techniques, criteria, guidelines and success factors to overcome them and derives research gaps that need to be addressed. Hence, the article speaks to both researchers and practitioners. For researchers, the article’s contribution is to synthesize the findings of the literature and to identify research gaps. For practitioners, the article’s contribution is to provide a systematic process for companies to improve their sustainability management and performance.

A TRIZ-Integrated Conceptual Design Process of a Smart Lawnmower for Uneven Grassland

Existing smart lawnmowers, while convenient to use, have significant limitations, such as a lack of manoeuvrability on uneven agricultural grassland (constraint 1), high charging frequency (constraint 2) and low local market penetration (constraint 3). Although the effectiveness of the theory of inventive problem solving (TRIZ) has been demonstrated in several design studies, there also seems to be a lack of research addressing the design difficulties of smart lawnmowers using this method. With the use of the TRIZ method, this study seeks to conceptually design a smart lawnmower for uneven grassland. Tools from TRIZ were used, including cause-effect chain analysis, technical contradictions, physical contradictions, and substance field modelling. In developing a design concept, constraints were solved by inventive principles, separation strategies and standard inventive solutions. For constraint 1, the following solutions were chosen with the appropriate principles: using larger wheels (#17, another dimension: using a second or third dimension), a pivot design (#30, flexible shell: replacing rigidity with flexibility and movability) and replacing the motor with one that has more power or torque. For constraint 2, the following solutions were chosen: to reduce weight, add holes in the mower housing (#31, porous materials: making an object porous or adding porous elements) and attach a solar panel to recharge batteries with solar energy (#28, mechanical substitution: using electric, magnetic or other fields to interact with object). Using other materials or technologies to minimise costs (#13, the other way around: using the opposite way) and a modular design concept to reduce maintenance costs (#1, segmentation: dividing an object into independent parts) were the chosen ways to solve constraint 3. Conceptualisation and design analysis were also performed. Although the effectiveness of the concept is unclear, these suggestions are supported by previous research and could potentially solve some of the problems with smart lawnmowers.

Exploring feedback mechanisms for nitrogen and organic carbon cycling in tropical coastal zones

The anthropogenic introduction of significant amounts of reactive nitrogen in the coastal zone particularly since the discovery and application of the Haber-Bosch process has profound consequences over organic carbon storage and transformations at both regional and global scales. Here, we review our current knowledge on cause-effect chains for nitrogen, especially dissolved inorganic nitrogen, on organic carbon cycling in coastal tropical systems. We focus on the feedback mechanisms for turnover of different organic carbon species to nitrogen excess and links to current environmental and climate changes. We pay special attention to organic carbon dynamics in tropical coasts due to their high primary productivity, rapid sedimentation, and significant needs of nitrogen for agriculture and industry usages. Together with land-use changes and economy development, we highlight the vulnerability of carbon storage in tropical coasts triggered by nitrogen overloading and outline possible industrial strategies with low carbon cycling disturbance to benefit the development of tropical countries.

Extension of the DEMATEL Method for Multi-Criteria Market Segment Evaluation

When evaluating different market segments Segment size and growth segment structure Charisma and company objectives and resources are the three Factors Company should consider. Demographics, psychology, behavior, and geography are considered to be the four main types of market segmentation. However there are many strategies that you can use. In four main categories there are many differences. Here are several methods that can be done. Like check out. The decision-making test and evaluation laboratory (DEMATEL) is considered to be an effective method for identifying the cause-effect chain components of a complex system. It deals with the evaluation of interrelated Relationships between factors and by visual layout model Identify the important ones. In this paper we used DEMATEL for ranking the DEMATEL method is the most ideal solution Short-distance Evaluation Parameters in Segment factors, Competition, Technological factors, Socio-political factors, Financial and economic factors Use Attention deficit from the result it is seen that Competition is got the first rank whereas is the Financial and economic factors is having the Lowest rank

Monetary valuation of the environmental benefits of green building: A case study of China

Reducing negative impacts on the environment is the main benefit of green buildings. However, these benefits are typically evaluated using environmental indicators with different units and implications. Thus, comparisons among different technologies and projects with various benefits are difficult to perform. Moreover, the impact of green building development on economic system is difficult to analyze because of the lack of available data. This study proposes a method of identifying, quantifying and valuing the environmental benefits of green buildings based on the contributions of typical technologies. The environmental benefits are fully identified based on the cause-effect chain associated with changes in environmental endpoints caused by green technologies. Then, a valuation method is proposed, and it consists of the following four steps: quantifying the technical indicators using the application criteria and green technology impacts, quantifying the environmental indicators using available and reliable research data, valuing the environmental benefits using appropriate monetary valuation methods, and calculating the lifecycle benefits using the discounted cash flow method. All projects accredited by the Chinese Assessment Standard of Green Building from 2008 to 2015 were evaluated using the environmental valuation method. The results showed that 75% of typical green technologies could reduce the negative impacts on the environment through different endpoints. The benefit per unit area was valued as ¥ 3138.1 on average. The majority of the benefits (48.36%) were produced by underground space utilization. The public green building produced more environmental value than the residential green building. Overall, the environmental benefits were sufficient to cover the incremental costs of green building. The results obtained by the proposed assessment method provide more comprehensive information to related sectors than the green level standards; moreover, the benefits obtained by different technologies and projects with various environmental impacts can be compared, and important input data can be obtained for analyzing the macroeconomic effects of green building development.