Causal Loop Research Articles

Evaluating the Eco‐Efficiency of Urban Air Mobility: Understanding Environmental and Social Impacts for Informed Passenger Choices

AbstractFull electric aircraft with zero CO2 emissions during operations are a promising pathway towards achieving the Fly Net Zero by 2050 goal proposed by the International Air Transport Association (IATA). In this context, Urban Air Mobility (UAM) expects to offer an environmentally friendly alternative through electric Vertical Takeoff and Landing (eVTOL) aircraft. While eVTOLs produce no greenhouse gas emissions, the comprehensive eco‐efficiency of UAM goes beyond the flight phase. This paper delves into evaluating UAM operation's environmental and social impacts, considering the urban space, public perception, operational profiles, and power consumption. Employing causal loop analysis, the study uncovers the relationships that add value or increase impact, assessing UAM passenger transportation's eco‐efficiency. Moreover, the Unified Architecture Framework is used to model the UAM ecosystem and to propose strategies to balance values and impacts in achieving eco‐efficiency. By shedding light on the sustainability viewpoint, this study emphasizes the importance of holistically understanding UAM's operational impact and empowering users to make eco‐efficient choices when opting for UAM transportation. Finally, strategies to provide sustainability awareness to the user are discussed in the Mobility‐as‐a‐System (MaaS) context.

Enhancing dairy cow population through decreasing calf mortality and increasing calving rate in Indonesia: a system dynamics approach

Abstract The dairy industry in Indonesia is facing significant challenges due to the rates of calf mortality and low calving rates. Calf mortality and calving rate became critical factors that influence the sustainability of the dairy cow population. This study aimed to assess reproduction management by decreasing calf mortality and increasing calving rates to enhance the dairy cow population in Indonesia. This study employed secondary data in 2022 using technical parameter coefficients from previous research. A causal loop diagram and a simulation model using a system dynamics approach were developed to investigate the dynamics of the dairy cow population under different scenarios of calf mortality and calving rates from 2023 to 2030. The results showed that decreasing calf mortality by 2% may increase dairy cow population by 1,991 head/year (0.31%), heifer population by 417 head/year (0.63%), and adult female dairy cattle population by 389 head/year (0.09%). The finding of this study also showed that increasing calving rate by 5% may increase dairy cow population by 17,854 head/year (2.75%), heifer population by 3,698 head/year (5.56%), and adult female dairy cattle population by 2,137 head/year (0.51%). It is concluded that reducing calf mortality rates and improving calving rates should be prioritized to enhance the dairy cow population in Indonesia.

A Model for Trust and Distrust: The System Dynamics Approach

AbstractThe dynamics of trust have evolved from a reliance on human interactions to a newfound dependence on the seamless integration of automation and artificial intelligence (AI) in relationships. Because trust is still treated as elusive in prior research, in this study, we consider a society that utilizes trust as a system and present a panoramic perspective of trust in social systems using the causal loop of systems thinking. The perspective of systems thinking is holistic (integrative) and focuses on the interrelationships among components rather than on the components of the system. Thus, the architecture was presented by integrating the trust and distrust models identified in previous studies. To overcome the challenges presented in previous studies, a model for trust and distrust was developed using a system dynamics approach. By utilizing systems thinking, the dynamics of trust are clearly illustrated among individuals, between individuals and automation, and between individuals and AI. In addition, it will allow for a perspective on the dynamic relationship among trust, reliance, and dependability, which is being investigated in “Humans” and “Automation and AI,” and will contribute to trust research.

A System Dynamics Model for Rooftop Solar PV System Development in Indonesia

Despite of Indonesia’s vast solar energy potential according to the Indonesian Ministry of Energy and Mineral Resources, Indonesia’s total installed capacity of rooftop solar photovoltaic (PV) is very far from Indonesia’s target of 3.6 GW in 2025. Indonesia once applied net metering scheme for rooftop solar PV policy and was expected to be able to boost rooftop solar PV growth. A system dynamics approach is used in the research to develop an assessment model to evaluate the policies’ impact on residential rooftop solar PV system growth. A Causal Loop Diagram was established then transformed into Stock and Flow Diagram (SFD) using software Vensim PLE 10.1.3, which was used to simulate several policies’ scenarios related to residential rooftop solar PV adoption and CO2 emissions reduction. Ten scenarios were simulated in this study involving three groups of intervention: initial net metering tariff, reduction on initial solar PV cost, upper limit of ROI, and combination of initial net metering tariff and initial solar PV cost reduction. The simulations revealed that combination of increasing net metering tariff to 80% & initial cost reduction 30% has the highest potential solar PV installations, the highest CO2 emissions reduction, and the lowest accumulation cost of net metering in 2030. This study can be used as reference by the policy makers in Indonesia to formulate the optimum policy to boost rooftop solar PV growth as the simulations shows that residential rooftop solar PV with the right intervention can meet the government’s target of rooftop solar PV in 2030.

Using qualitative system dynamics modeling to understand overdose bystander behavior in the context of Connecticut’s Good Samaritan Laws and identify effective policy options

BackgroundGood Samaritan Laws are a harm reduction policy intended to facilitate a reduction in fatal opioid overdoses by enabling bystanders, first responders, and health care providers to assist individuals experiencing an overdose without facing civil or criminal liability. However, Good Samaritan Laws may not be reaching their full impact in many communities due to a lack of knowledge of protections under these laws, distrust in law enforcement, and fear of legal consequences among potential bystanders. The purpose of this study was to develop a systems-level understanding of the factors influencing bystander responses to opioid overdose in the context of Connecticut’s Good Samaritan Laws and identify high-leverage policies for improving opioid-related outcomes and implementation of these laws in Connecticut (CT).MethodsWe conducted six group model building (GMB) workshops that engaged a diverse set of participants with medical and community expertise and lived bystander experience. Through an iterative, stakeholder-engaged process, we developed, refined, and validated a qualitative system dynamics (SD) model in the form of a causal loop diagram (CLD).ResultsOur resulting qualitative SD model captures our GMB participants’ collective understanding of the dynamics driving bystander behavior and other factors influencing the effectiveness of Good Samaritan Laws in the state of CT. In this model, we identified seven balancing (B) and eight reinforcing (R) feedback loops within four narrative domains: Narrative 1 - Overdose, Calling 911, and First Responder Burnout; Narrative 2 - Naloxone Use, Acceptability, and Linking Patients to Services; Narrative 3 - Drug Arrests, Belief in Good Samaritan Laws, and Community Trust in Police; and Narrative 4 - Bystander Naloxone Use, Community Participation in Harm Reduction, and Cultural Change Towards Carrying Naloxone.ConclusionsOur qualitative SD model brings a nuanced systems perspective to the literature on bystander behavior in the context of Good Samaritan Laws. Our model, grounded in local knowledge and experience, shows how the hypothesized non-linear interdependencies of the social, structural, and policy determinants of bystander behavior collectively form endogenous feedback loops that can be leveraged to design policies to advance and sustain systems change.

Are we there yet? Saturation analysis as a foundation for confidence in system dynamics modeling, applied to a conceptualization process using qualitative data

AbstractSaturation is a useful concept for system dynamics, yet it has not been widely explored or integrated into the modeling process. In this article, we describe saturation as a metaphor describing the point at which a conceptual representation of a system meets the study purpose and no longer requires modification. When saturation is reached, additional data about the problem would not offer added information, thus indicating that additional data gathering and analysis would likely be redundant. We discuss two visualization techniques, “saturation curves” and “shared understanding diagrams,” for assessing saturation when conceptualizing with causal loop diagrams and show their application in a case example. Using saturation analysis during a system dynamics research process has many advantages, including: (i) identifying model structures potentially needing revisions, (ii) observing the extent to which evidence supports the current conceptualization, (iii) reflecting extensively, (iv) documenting important modeling decisions, and (v) potentially improving the problem statement. © 2024 The Author(s). System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

Modeling Pospay and Multi-Lane Free Flow Integration for Revenue Increase

The rise of e-commerce in Indonesia has made digital payment systems increasingly important. One such system is PT. Pos Indonesia’s Pospay application. However, the app is not very popular among users as indicated by its low download numbers and ratings. To address this issue, the potential benefits of combining the Pospay application with Cantas to facilitate toll road transactions were examined. The aim was to increase public awareness of the Pospay application, which would lead to increased revenue. A dynamic system approach was used to develop an optimal strategy for improving Pospay services in the Public Transport sector and integrating it as a toll payment method in the MLFF (Multi-Lane Free Flow) system. The initial dynamic system modeling, using a Causal Loop Diagram (CLD), revealed that service innovation, customer satisfaction, and company revenue are interrelated variables that influence each other. The results of this research can be used as suggestions for companies in developing service innovation strategies.

Interconnectedness of Health and Economy in Post-COVID-19: A Systems Thinking Approach to Synergies, Trade-Offs, and Policy Responses in Nepal.

This study addresses the economic challenges posed by the Coronavirus Disease 2019 (COVID-19) pandemic, with a specific focus on Nepal. Employing a Systems Thinking approach, the study develops a comprehensive model that integrates macroeconomic interventions, exploring the trade-offs and synergies among various economic sectors. The interconnectedness of health, economic, and policy domains highlights the need for a holistic understanding of the pandemic's effects. An exploratory research design was used. The model introduces three primary subsystems-import, local demand, and local production-illustrating their interdependence. The causal loop diagram depicts the implications of fiscal and monetary policies on the economy, emphasizing the synergy and trade-offs resulting from policy interventions. Findings reveal the significant economic impact of the pandemic on Nepal, leading to its negative growth rate in FY 2020/21, the first time Nepal has experienced this in the last two decades. The results show that both intended and unintended consequences are observed following the adoption of fiscal and monetary policy that aimed at minimizing the spread of virus suggesting a complex nature of relationship among the policy variables. The fiscal policy induced negative consequences on the increasing loan, slow revenue growth, while positive feedbacks are observed on increased investment opportunities, and boosting of local economy. The monetary measures yielded unintended consequences on the rising land prices, surge in share market. The study concludes by emphasizing the crucial role of informed policymaking in navigating the complex landscape, offering insights for creating a resilient and sustainable post-pandemic future.

Fostering systems thinking about socioscientific issues

ABSTRACT Systems thinking is essential for understanding wicked real-world problems and suggesting solutions. This is the case for addressing socioscientific issues (SSI) that involve inter-related personal, socio-political, cultural, scientific, economic and moral dimensions. It is challenging for teachers to support students in adopting and practising systems thinking when they explore SSI. In this study, we tested the extent to which causal loop diagramming (CLD) developed students’ systems thinking. We examined a 2-week teaching unit that used a computer-based CLD tool to foster students’ systems thinking about obesity in a first-year university general education course. Ninety-six students from a variety of majors/disciplines participated in this study. They worked in groups and engaged in reading news, proposing actions while creating causal loop diagrams about factors that contributed to obesity and approaches to tackle the issue. We analysed their pre-, mid-, and post-unit causal loop diagrams (3 × 26 groups) and weekly reflective memos. The data revealed their significant shift towards a systems approach of thinking. The reflective memos showed that the shift was afforded by cycles of construct–revise–use as they created the diagrams. The findings suggest the potential of causal loop diagramming to foster systems thinking in the sense-making and decision-making of SSI.

Mapping complex public health problems with causal loop diagrams.

This paper presents causal loop diagrams (CLDs) as tools for studying complex public health problems like health inequality. These problems often involve feedback loops-a characteristic of complex systems not fully integrated into mainstream epidemiology. CLDs are conceptual models that visualize connections between system variables. They are commonly developed through literature reviews or participatory methods with stakeholder groups. These diagrams often uncover feedback loops among variables across scales (e.g. biological, psychological and social), facilitating cross-disciplinary insights. We illustrate their use through a case example involving the feedback loop between sleep problems and depressive symptoms. We outline a typical step-by-step process for developing CLDs in epidemiology. These steps are defining a specific problem, identifying the key system variables involved, mapping these variables and analysing the CLD to find new insights and possible intervention targets. Throughout this process, we suggest triangulating between diverse sources of evidence, including domain knowledge, scientific literature and empirical data. CLDs can also be evaluated to guide policy changes and future research by revealing knowledge gaps. Finally, CLDs may be iteratively refined as new evidence emerges. We advocate for more widespread use of complex systems tools, like CLDs, in epidemiology to better understand and address complex public health problems.

Supporting whānau during COVID-19 pandemic in Aotearoa New Zealand: a systems thinking case study

BackgroundThe Aotearoa New Zealand COVID-19 pandemic response has been hailed as a success story, however, there are concerns about how equitable it has been. This study explored the experience of a collective of Māori health and social service providers in the greater Wellington region of Aotearoa New Zeland delivering COVID-19 responses.MethodsThe study was a collaboration between a large urban Māori health and social service provider, Tākiri Mai Te Ata whānau ora collective, and public health researchers in Aotearoa New Zealand. Two online workshops were held with staff of the Māori service provider, collectively developing a qualitative causal loop diagram and generating systemic insights. The causal loop diagram showed interactions of various factors affecting COVID-19 response for supporting whānau (Māori family/households) at a community level. The iceberg model of systems thinking offered insights for action in understanding causal loop diagrams, emphasizing impactful changes at less visible levels.ResultsSix interacting subsystems were identified within the causal loop diagram that highlighted the systemic barriers and opportunities for effective COVID-19 response to Māori whānau. The medical model of health service produces difficulties for delivering kaupapa Māori services. Along with pre-existing vulnerability and health system gaps, these difficulties increased the risk of negative impacts on Māori whānau as COVID-19 cases increased. The study highlighted a critical need to create equal power in health perspectives, reducing dominance of the individual-focused medical model for better support of whānau during future pandemics.ConclusionsThe study provided insights on systemic traps, their interactions and delays contributing to a relatively less effective COVID-19 response for Māori whānau and offered insights for improvement. In the light of recent changes in the Aotearoa New Zealand health system, the findings emphasize the urgent need for structural reform to address power imbalances and establish kaupapa Māori approach and equity as a norm in service planning and delivery.

Interaction of corporate social responsibility and attractiveness of organization to job seekers: a system dynamic study

Purpose This paper aims to understand the two-way interaction between corporate social responsibility (CSR) and the attractiveness of organization to job seekers (AOJS). Design/methodology/approach A system dynamics model is developed in the form of a causal loop diagram (CLD) that explains the CSR-AOJS interaction dynamically. To test the credibility of the developed model, the survey data are used to validate the causal relationships in the CLD. Findings This study found that developing an effective strategy or tool by capturing various essential CSR elements can attract potential job seekers. Originality/value The developed model is relevant to policymakers, decision-makers and managers when strategizing the CSR plan to attract potential job seekers.

Applying systems thinking in youth-centred participatory action research for health promotion in an underserved neighbourhood.

Childhood overweight is considered a complex problem influenced by a range of factors, including energy balance-related behaviours (EBRBs) and interacting drivers of these behaviours. There is growing support that applying a systems approach is required to tackle complex problems resulting in actions that attempt to change the system's dynamics. Additionally, a participatory approach is advocated to include the lived experience of the population of interest both in the understanding of the system as well as the development, implementation and evaluation of relevant actions. We therefore combined Intervention Mapping, Participatory Action Research (PAR) and system dynamics in the development, implementation and evaluation of actions contributing to healthy EBRBs together with adolescents. Four PAR groups comprising of 6-8 adolescent co-researchers (10-14 years) and 1-2 adult facilitators met weekly during 3-4 years. The structured Intervention Mapping protocol guided the process of the systematic development, implementation and evaluation of actions. System dynamics tools were included for the creation of Causal Loop Diagrams and development of systemic actions. Our approach comprised six steps that were executed by the PAR groups: (1) build Causal Loop Diagrams for each EBRB through peer research and identify overarching mechanisms, (2) determine leverage points using the Intervention Level Framework, (3) develop action ideas, (4) develop detailed actions including an implementation plan, (5) implement and, (6) evaluate the actions. PAR ensured that the actions fitted the lived experience of the adolescents, whilst system dynamics promoted actions at different levels of the system. The Intervention Mapping protocol ensured that the actions were theory-based. The main challenge involved integrating system dynamics within our practise in cooperation with adolescent co-researchers. We experienced that combining Intervention Mapping, PAR and system dynamics worked well in developing, implementing and evaluating actions that target different levels of the system that drive adolescents' EBRBs. This study serves as an example to other studies aimed at developing, implementing and evaluating actions using a participatory and systems approach.

Dynamic systems analysis of waste generation in Banda Aceh: a comprehensive evaluation

Abstract Many researchers state that municipal waste management is a city service that must be provided effectively to all residents to achieve prosperity because this problem has been considered a serious problem facing cities. One alternative to look at the waste problem is to evaluate the waste management system using a dynamic system model. The research aims to evaluate the conditions of waste generation in order to optimize waste management efforts in Banda Aceh. A simulation model was conducted in this research based on a dynamic system using Powersim and Stella software. The treatment carried out include collecting primary and secondary data, creating Causal Loop Diagrams and Stock Flow Diagrams, as well as simulations of waste generation based on existing data spanning the years 2017 to 2022. Results show that Banda Aceh City’s waste generation continues to increase with exponential growth behavior during 2017-2022. Simulation results show that in 2030, this waste will reach 255,043 tons with a waste generation rate of 0.63761 tons per year. Results of dynamic system modeling show a sustainable increase in waste production caused by an increase in population. Model validation analysis shows that AVE and MAVE values obtained were 0.0037 and 0.0631, respectively. These results were considered acceptable since they were both less than 10%.

Use of the National Climate and Energy Policy Simulation Tool in the Policy Making Process

Energy transition towards CO2 neutral energy systems is challenging as the national energy policy depends on numerous factors. In system dynamics approach these factors are identified as causal and feedback loops with lock-ins, as a result constituting stock-and-flow structure. Such a kind of structure is used in different simulation tools. This study analyses the use of the Latvian national climate and energy policy simulation tool in the policy making process. As a hypothesis of the study was set that the simulation tool, which is based on the principles of systemic thinking (explaining causal relationships) and an easy-to-use user interface, significantly improves the national energy and climate policy making process. Accordingly, to verify the hypothesis the task was to evaluate the use of the national climate and energy policy simulation tool in the policy-making process. For the evaluation the relevant literature was reviewed: climate and energy policy goals, policies and legislation in the European Union and Latvia; approaches to modeling national climate and energy policies, including optimization models, simulation models; the use of simulation models in the modeling of national climate and energy policies; the use of system dynamics models in the modeling of national climate and energy policies. Besides using the systemic thinking approach in the process of creating national climate and energy policies, the user experience in building simulation tools was used. Based on the literature analysis, improvements to the interface of the national climate and energy simulation tool developed by Riga Technical University, Institute of Energy Systems and Environment were made and developed training material for the use of the simulation tool, based on explaining the causal relationships built into the model.

Modelling the behaviour in socio-technical systems for policy assessment - A comparison of modelling approaches using the example of Sponge City concept implementation

How are policies affecting the social behaviour of actors and thus the dynamics of the corresponding socio-technical systems? This question is of central relevance to understand what policies might be effective for sustainable transitions and transformation where social changes are immanent. Exploratory modelling approaches such as agent-based modelling (ABM) are suitable and established to analyse how individual, especially heterogeneous behaviour emerges to affect social systems. In contrast to this micro-level perspective of understanding how social behaviour influences system behaviour, System Dynamics models (SDM) focus on the macro-level perspective by analysing how system interactions drive complex system behaviour. Qualitative modelling approaches such as Causal Loop Diagrams belonging to the Systems Thinking Modelling (STM) toolbox remain descriptive but need much less effort for model development. In this article, we compare how the different approaches ABM, SDM and STM can model social behaviour in socio-technical systems and what different kind of insights can be gained to assess the efficacy of policies. It discusses the advantages and limitations of the different approaches for their application in behavioural public policy modelling for sustainability transition. Therefore, three exemplary models (STM, SDM, ABM) are created for the same topic of implementation dynamics of adaptation measures to foster rainwater retention (Sponge City concept) in a multi-residential quarter. As a result, the risk of green gentrification is identified. Three hypothetical policies are implemented to illustrate the differences between the three modelling approaches for their use in policy assessment. Based on that, we discuss how the three modelling approaches conceptualise behaviour in general.

Applying Blockchain, Causal Loop Diagrams, and the Analytical Hierarchy Process to Enhance Fifth-Generation Ceramic Antenna Manufacturing: A Technology–Organization–Environment Framework Approach

This study used a technology–organization–environment (TOE) framework as the primary analytical tool to explore the burgeoning capabilities of blockchain technology in the area of 5G ceramic antenna development. A causal loop diagram (CLD) analysis is used to further clarify the complex dynamics and feedback mechanisms, and the impact of blockchain on the design, production, and deployment phases of ceramic antennas, which play a pivotal role in the development of 5G communications, is studied. We found that blockchain’s unique features, including its immutable ledger and decentralized architecture, have the potential to significantly improve the transparency, security, and efficiency of the ceramic antenna manufacturing process. Technology (T), organization (O), and environment (E) were used as the top factors, and the subfactors of TOE were selected and analyzed using the Analytic Hierarchy Process (AHP) by CLD. The AHP analysis was used to evaluate the relative importance of various internal and external factors affecting the adoption of blockchain technology. The integration of the TOE framework with AHP and CLD provides a comprehensive analytical tool that enhances the understanding of the complex dynamics in the 5G ceramic antenna manufacturing process. This methodological approach not only clarifies the interactions between technological, organizational, and environmental factors but also facilitates strategic decision-making through a structured evaluation of these factors. The AHP analysis showed that technical factors are the most important in the TOE analysis of 5G ceramic antenna manufacturing, with a weight of 0.427, which indicates the important role of technical factors in the development of ceramic antenna production. In addition, environmental and organizational factors were given weights of 0.302 and 0.271, respectively, confirming the importance of technological innovation and internal process optimization. In the subfactor of Technology (T), ‘Blockchain Technology’ has the highest ranking among the subfactors, with a global weight value of 0.129, emphasizing the importance of blockchain technology. This study explored the technical and organizational complexities of introducing blockchain technology into the 5G ceramic antenna manufacturing industry and, through an in-depth investigation of the potential benefits of such integration, it aims to propose new approaches to improve quality control and manufacturing efficiency. The research findings aim to contribute to the sustainable growth of the telecommunications industry by providing strategic recommendations for the application of blockchain technology in the production of 5G ceramic antennas.

Cascading supply chain disruption: impact on water industry during COVID-19

The impact of COVID-19 on the lives of people and businesses across the globe was devastating. While governments across the world had undertaken a slew of measures to control the spread of the COVID-19 virus within their geography, many of these measures had long and unintended consequences. The restrictions imposed by the governments on the movement of people and goods across the world brought supply chains to a grinding halt. This study identifies the cascading effects of supply chain disruptions on the water sector. Since these systems are closely integrated and the impact of COVID-19 needs to be analysed at a much broader level, this study uses a systems-thinking approach to study the effect of supply chain disruptions on water services. The study develops a causal loop model to gain further insight into how supply chain disruptions caused by COVID-19 affected the coping capabilities of society and how critical services were affected.

Development of an Evaluation System for Intelligent Construction Using System Dynamics Modeling

Under the new wave of scientific and technological revolution, the construction industry finds itself with a critical need to alter the traditional and outdated production mode through technological innovation in order to realize industry transformation and move towards a new era characterized by digitalization, informatization, and intelligence. As intelligent construction is the indispensable pathway for the transformation and upgrading of the construction industry, it is of great significance to conduct in-depth research on its evaluation indicators and causality. This paper adopts the system dynamics method, based on the overall structure of intelligent construction, extracts the causality chain and causal feedback loop of intelligent construction, and presents a causality diagram and system dynamics diagram to build a robust system dynamics model for intelligent construction. On this basis, an evaluation index system for intelligent construction is constructed from the five dimensions—investment, design, construction, operation, and environment—for a holistic assessment of the current state of intelligent construction. The research aims to provide a valuable reference for professionals focusing on intelligent construction and the broader development of the industry.

Risk Analysis and Simulation of Large Bridge Construction Based on System Dynamics

To reduce risk incidents in large bridge construction, it is necessary to study the interaction mechanisms and dynamic changes among various risk factors during the construction of large bridges. First, the evolution mechanism of construction risks for large bridges is analyzed, and a risk factor structure system is established. Then, based on system dynamics theory, a causal loop diagram and flow–stock diagram are constructed, and system dynamics equations for each variable in the flow diagram are established to build a system dynamics model. Finally, taking a large bridge construction project as an example, the variable equations are assigned actual values, and the construction risk level is simulated and analyzed. The results show that, ➀ continuous investment in safety funds can effectively reduce the overall risk level of the system, ➁ changes in the management risk subsystem have a significant impact on the overall risk level of large bridge construction, and ➂ increasing the safety investment ratio in both the personnel risk subsystem and the management risk subsystem can effectively reduce the total risk level of the system.