Adaptation Goals Research Articles

Actor-specific adaptation objectives shape perceived roles and responsibilities: lessons from Mumbai’s flood risk reduction and general considerations

Adaptation efforts need to be advanced significantly, involving multiple actors and a diverse portfolio of options. Despite this being well established, there is little understanding of different actors’ perceptions of adaptation goals and their associated expectations regarding roles and responsibilities to achieve them. In this analysis, we seek to address this gap by elucidating the diverging viewpoints held by various actor groups concerning adaptation objectives, target beneficiaries, and the distribution of roles and responsibilities for adaptation. Here, we use the case study of flood risk in Mumbai, drawing upon qualitative interview data collected through key informant interviews with diverse stakeholders including state, civil society, and academic actors. Interviews revealed stark disparities between state and non-state actors, in particular on the objective of efficiency, largely emphasized by state actors for physical infrastructure measures. Other contested objectives included ecosystem protection and fairness for vulnerable populations. The findings showed consensus on the importance of planning. Non-state actors heavily debated the lack of planning and implementation of institutional changes and ecosystem-based measures. They called for a stronger role of the state in caretaking and fairness for vulnerable populations, mainly through deeper institutional changes. Overall, the findings point to the urgent need for understanding how actors navigate competing priorities, make trade-offs, and negotiate conflicting viewpoints on the distribution of roles and responsibilities. This paper makes an empirical and conceptual contribution to the debates on “social contracts” for adaptation, offering an operationalization of the concept and application to a real-world example through an actor lens.

Climate Resilience in Nigerian Construction: A Systematic Review of Strategies and Outcomes

Climate resilience in the construction sector is critical for ensuring the durability and sustainability of infrastructure amidst the increasing impacts of climate change. This study systematically reviews climate resilience strategies in the Nigerian construction sector, evaluating their effectiveness and outcomes. A comprehensive literature search yielded 50 peer-reviewed journal articles, conference papers, and official reports, focusing on design innovations, material selection, policy frameworks, and case studies across Nigeria's diverse climatic zones. Key findings indicate that strategies such as flood barriers, green roofs, and sustainable materials are effective in mitigating climate risks, although challenges such as financial constraints, regulatory gaps, and lack of awareness persist. The study highlights the importance of community involvement, government support, and technological innovation in successfully implementing resilience measures. Comparative analysis with global best practices underscores the need for integrated approaches tailored to Nigeria's unique context. The study concludes with recommendations for future research, emphasizing the need for longitudinal studies, cross-regional comparisons, and the integration of traditional knowledge. Policy implications include the development of comprehensive regulatory frameworks and public-private partnerships to enhance the sector's adaptive capacity. This research provides valuable insights and practical recommendations for enhancing climate resilience in Nigeria's construction industry, contributing to broader goals of sustainable development and climate adaptation.

Meta-learning of human motor adaptation via the dorsal premotor cortex

Meta-learning enables us to learn how to learn the same or similar tasks more efficiently. Decision-making literature theorizes that a prefrontal network, including the orbitofrontal and anterior cingulate cortices, underlies meta-learning of decision making by reinforcement learning. Recently, computationally similar meta-learning has been theorized and empirically demonstrated in motor adaptation. However, it remains unclear whether meta-learning of motor adaptation also relies on a prefrontal network. Considering hierarchical information flow from the prefrontal to motor cortices, this study explores whether meta-learning is processed in the dorsolateral prefrontal cortex (DLPFC) or in the dorsal premotor cortex (PMd), which is situated upstream of the primary motor cortex, but downstream of the DLPFC. Transcranial magnetic stimulation (TMS) was delivered to either PMd or DLPFC during a motor meta-learning task, in which human participants were trained to regulate the rate and retention of motor adaptation to maximize rewards. While motor adaptation itself was intact, TMS to PMd, but not DLPFC, attenuated meta-learning, impairing the ability to regulate motor adaptation to maximize rewards. Further analyses revealed that TMS to PMd attenuated meta-learning of memory retention. These results suggest that meta-learning of motor adaptation relies more on the premotor area than on a prefrontal network. Thus, while PMd is traditionally viewed as crucial for planning motor actions, this study suggests that PMd is also crucial for meta-learning of motor adaptation, processing goal-directed planning of how long motor memory should be retained to fit the long-term goal of motor adaptation.

Climate adaptation in multi‐level governance systems: Security, risk, or normal politics?

AbstractUsing a securitization lens, this article explores the climate adaptation discourse and its impact on the making and implementation of adaptation strategies in Sweden. The main goal is to discern whether climate change is understood and addressed as a security issue within Swedish climate adaptation policy, examining its practical implications from national to local levels. We scrutinize the discourses employed to frame climate adaptation and assess whether these align with threatification, riskification, or normal politics. We explore the actors and tools involved in creating this framing. Our findings reveal examples of threat‐ and risk‐oriented securitizations of climate adaptation strategy; however, most evidence highlights discourses and practices associated with normal politics across governance levels. Nationally, climate adaptation is managed akin to any other policy domain. Prioritization of adaptation goals takes place through centralized decision‐making, then monitored through accountability mechanisms spanning national, subregional, and local levels. The national government maintains financial and monitoring control throughout this chain. Municipalities possess significant autonomy in determining the means and methods to achieve adaptation objectives. This indicates that some securitization, but mainly normal policymaking, describes climate change adaption in Sweden ‐ an outcome strongly influenced by organizational fragmentation, scarce resources, and a pronounced role for experts.

Risk Assessment of Community-Scale High-Temperature and Rainstorm Waterlogging Disasters: A Case Study of the Dongsi Community in Beijing

With the advancement of urbanization and acceleration of global warming, extreme weather and climate events are becoming increasingly frequent and severe, and climate risk continues to rise. Each community is irreplaceable and important in coping with extreme climate risk and improving urban resilience. In this study, the Dongsi Community in the functional core area of Beijing was explored, and the risk assessment of high temperatures and rainstorm waterlogging was implemented at the community scale. Local navigation observations were integrated into a theoretical framework for traditional disaster risk assessment. The risk assessment indicator system for community-scale high-temperature and rainstorm waterlogging disasters was established and improved from a microscopic perspective (a total of 22 indicators were selected from the three dimensions of hazard, exposure, and vulnerability). Geographic Information Systems (GIS) technology was used to integrate geographic information, meteorological, planning, municipal, socioeconomic and other multisource information layers, thus enabling more detailed spatial distribution characteristics of the hazard, exposure, vulnerability, and risk levels of community-scale high temperatures and rainstorm waterlogging to be obtained. The results revealed that the high-risk area and slightly high-risk area of high-temperature disasters accounted for 13.5% and 15.1%, respectively. The high-risk area and slightly high-risk area of rainstorm waterlogging disasters accounted for 9.8% and 31.6%, respectively. The high-risk areas common to high temperatures and waterlogging accounted for 3.9%. In general, the risk of high-temperature and rainstorm waterlogging disasters at the community scale showed obvious spatial imbalances; that is, the risk in the area around the middle section of Dongsi Santiao was the lowest, while a degree of high temperatures or rainstorm waterlogging was found in other areas. In particular, the risk of high-temperature and rainstorm waterlogging disasters along Dongsi North Street, the surrounding areas of Dongsi Liutiao, and some areas along the Dongsi Jiutiao route was relatively high. These spatial differences were affected to a greater extent by land cover (buildings, vegetation, etc.) and population density within the community. This study is a useful exploration of climate risk research for resilient community construction, and provides scientific support for the planning of climate-adaptive communities, as well as the proposal of overall adaptation goals, action frameworks, and specific planning strategies at the community level.

Joint weight optimization for partial domain adaptation via kernel statistical distance estimation

The goal of Partial Domain Adaptation (PDA) is to transfer a neural network from a source domain (joint source distribution) to a distinct target domain (joint target distribution), where the source label space subsumes the target label space. To address the PDA problem, existing works have proposed to learn the marginal source weights to match the weighted marginal source distribution to the marginal target distribution. However, this is sub-optimal, since the neural network’s target performance is concerned with the joint distribution disparity, not the marginal distribution disparity. In this paper, we propose a Joint Weight Optimization (JWO) approach that optimizes the joint source weights to match the weighted joint source distribution to the joint target distribution in the neural network’s feature space. To measure the joint distribution disparity, we exploit two statistical distances: the distribution-difference-based L2-distance and the distribution-ratio-based χ2-divergence. Since these two distances are unknown in practice, we propose a Kernel Statistical Distance Estimation (KSDE) method to estimate them from the weighted source data and the target data. Our KSDE method explicitly expresses the two estimated statistical distances as functions of the joint source weights. Therefore, we can optimize the joint weights to minimize the estimated distance functions and reduce the joint distribution disparity. Finally, we achieve the PDA goal by training the neural network on the weighted source data. Experiments on several popular datasets are conducted to demonstrate the effectiveness of our approach. Intro video and Pytorch code are available at https://github.com/sentaochen/Joint-Weight-Optimation. Interested readers can also visit https://github.com/sentaochen for more source codes of the related domain adaptation, multi-source domain adaptation, and domain generalization approaches.

Kernel Extreme Learning Machine with Discriminative Transfer Feature and Instance Selection for Unsupervised Domain Adaptation

The goal of domain adaptation (DA) is to develop a robust decision model on the source domain effectively generalize to the target domain data. State-of-the-art domain adaptation methods typically focus on finding an optimal inter-domain invariant feature representation or helpful instances from the source domain. In this paper, we propose a kernel extreme learning machine with discriminative transfer features and instance selection (KELM-DTF-IS) for unsupervised domain adaptation tasks, which consists of two steps: discriminative transfer feature extraction and classification with instance selection. At the feature extraction stage, we extend cross domain mean approximation(CDMA) by incorporating a penalty term and develop discriminative cross domain mean approximation (d-CDMA) to optimize the category separability between instances. Subsequently, d-CDMA is integrated into kernel ELM-AutoEncoder(KELM-AE) for extracting inter-domain invariant features. During the classification process, our approach uses CDMA metrics to compute a weights to each source instances based on their impact in reducing distribution differences between domains. Instances with a greater effect receive higher weights and vice versa. These weights are then used to distinguish and select source domain instances before incorporating them into weight KELM for proposing an adaptive classifier. Finally, we apply our approach to conduct classification experiments on publicly available domain adaptation datasets, and the results demonstrate its superiority over KELM and numerous other domain adaptation approaches.

The LTAR Cropland Common Experiment at Central Mississippi River Basin.

The Central Mississippi River Basin (CMRB) Common Experiment, with its marginal soils and southern Corn Belt climate, is an ideal location for evaluating progress toward environmental, productivity, and climatic adaptation goals. Sustainable production with conventional row-crop systems is more challenging than in the upper Corn Belt, making evaluation and adoption of alternative farming practices crucial. This Common Experiment has a hydrologically restrictive layer causing reduced plant available water capacity in the root zone. The CMRB site joined the Long-Term Agroecosystem Research Network in 2011 with the Cropland Common Experiment established in 2015. The Common Experiment contrasts prevailing and alternative practices at plot and field scale. Improvement of the soil ecosystem is key, as it underpins other objectives, including reduced nutrient losses, increased soil water holding capacity, and yield stability.

Tree selection for a virtual urban park: Comparing aided and unaided decision-making to support public engagement in greenspace design

To meet urban resilience goals and the needs of growing populations, cities aim to develop multifunctional greenspaces and urban forests. Urban greening is seen to improve the quality of life for residents, contribute significantly to biodiversity conservation and socio-ecological resilience, and meet climate mitigation and adaptation goals. There is also a growing recognition of the importance of involving individuals and communities in the design and planning of greenspace in cities, particularly in the configuration of parks and in identifying the types of vegetation found there. In these contexts, it is increasingly common to engage the public with virtual 3D landscapes, with the ultimate goals of crowdsourcing preferences, knowledge, and patterns of use. There have been few studies to systematically examine how the public interacts with these virtual spaces, and their decision-making needs. Experts have a fluency with a broad range of ecosystem services that flow from urban greenspaces, as well as a familiarity with trees and other landscape elements. This is not the case for the public, who may instead rely on familiar and visually salient landscape attributes. This is in keeping with the concept of constructed preferences, where judgements are formed as they are elicited and are heavily influenced by available information. This study thus compares aided and unaided decision-making by the public in a virtual 3D urban park. Participants were invited to plant trees in park; some participants were provided with a brief description of a key ecosystem function of each tree (along with an illustration of that tree), other participants were only provided the illustration. Three key insights emerge from this research: (i) public tree preferences are sensitive to whether information is provided or withheld, (ii) in the absence of information, easy to evaluate characteristics (i.e., visually salient characteristics) played a large role in tree selection within the virtual urban parks, and (iii) for most participants, and consistent with other studies, aesthetics was the most important attribute guiding tree choice. These insights can support improved public engagement in landscape design and planning, particularly in crowdsourced and virtual settings.

'There was nothing, just absolute darkness': Understanding the needs of those caring for children and young people with complex neurodisability in a diverse UK context: A qualitative exploration in the ENCOMPASS study.

Children and young people (CYP) with complex neurodisability experience multiple physical, communication, educational and social challenges, which require complex packages of multidisciplinary care. Part of the holistic care required includes supporting the families and parents/caregivers. The aim of the wider study was to introduce a new programme ('Ubuntu') to parents/caregivers and healthcare professionals (HCPs) in order to test the feasibility and acceptability of the concept and content, with the goal of potential adaptation for the UK in mind. Data collection and analysis uncovered rich data on caregiving journeys, navigation of health services, and perceived service gaps. This paper focuses solely on these topics. Further papers will report on the feasibility and adaptation data. Two rounds of semi-structured interviews were conducted with 12 caregivers of CYP with complex neurodisability and six HCPs from a variety of disciplines, recruited from a community child health service in London Borough of Newham, UK in 2020. The interviews included open-ended questions to explore caregiving journeys, experiences of navigating health services and perceived service gaps. Transcripts were analysed using a data-driven inductive thematic analysis. Three themes were identified that related to the aim of understanding caregivers' experiences and unmet needs relating to current service provision. These were (1) Caregiver Mental Health, (2) The Information Gap and (3) The Need for Holistic Support. Mental health difficulties were reported, particularly around the period of diagnosis. Priority needs included the provision of clear information about the diagnosis and services offered, opportunities to forge peer support networks and for services across the community to collaborate. The delivery of health services for CYP with neurodisability should encompass the broad needs of the family as well as meeting the clinical needs of the CYP.

Public preferences for coastal adaptation: Economic evidence from a discrete choice experiment for hard structures and nature-based solutions in Miami, Florida

Coastal communities are under pressure to adapt to the increasing impacts of climate change and environmental degradation. One strategy to achieve adaptation goals is to deploy hard structures and nature-based solutions that can help reduce damages from flooding, storm surge, and sea level rise in coastal areas. Understanding how local communities perceive and value these measures helps local authorities follow strategies that not only increase efficiency, but also garner public support. In this paper, we used Miami-Dade County as a case study to establish public preferences and trade-offs across hard structures and nature-based solutions and their physical characteristics to address coastal risk reduction: specifically, seawalls and mangroves. By applying a discrete choice experiment, we showed that respondents are generally in favor of deploying any coastal measures, with a higher willingness-to-pay for nature-based solutions over hard structures. We also found that the same respondents identify the length of protected coastline as a valuable feature of these coastal measures. Our findings inform ongoing plans and policies for coastal adaptation, particularly the deployment of nature-based solutions, and also illustrate the importance of including public input in the decision-making process of coastal management policy.

Climate concepts for supporting political goals of mitigation and adaptation: The case for “climate crisis”

AbstractClimate concepts are crucial to understand the effects of human activity on the climate system scientifically, and to formulate and pursue policies to mitigate and adapt to these effects. Yet, scientists, policymakers, and activists often use different terms such as “global warming,” “climate change,” “climate crisis,” or “climate emergency.” This advanced review investigates which climate concept is most suitable when we pursue mitigation and adaptation goals in a scientifically informed manner. It first discusses how survey experiments and social science reviews on climate frames draw normative recommendations about which terms to use for public climate communication. It is suggested that such normative claims can be refined by including the scientific alongside lay uses of a climate concept, and by using explicit assessment conditions to evaluate how suitable a concept is for formulating mitigation and adaptation goals. Drawing on philosophical theories of conceptual change in science and conceptual engineering, a novel framework with two assessment conditions is introduced and then applied to “global warming,” “climate change,” “climate emergency,” and “climate crisis.” The assessment suggests that currently, “climate crisis” is most suitable to formulate and pursue climate mitigation and adaptation goals. Using this concept promotes the epistemic goals of climate science to a high degree, bridges scientific, political, and activist discourse, and fosters for democratic participation when articulating climate policies.This article is categorized under: The Social Status of Climate Change Knowledge > Climate Science and Decision Making The Social Status of Climate Change Knowledge > Knowledge and Practice Perceptions, Behavior, and Communication of Climate Change > Communication

Passing patients’ tests and following patients’ coaching communications in psychotherapy: an empirical study

ABSTRACT According to Control-Mastery Theory, patients attend therapy with an unconscious plan to achieve adaptive goals and disprove their pathogenic beliefs. One important way patients work to disconfirm these beliefs is by testing them. Moreover, patients coach their therapists on what they need at different times during their treatment. The aim of this study is to expand the results of previous studies suggesting that therapists’ responses that support patients’ plan predict progress. We investigated whether therapists' ability to pass patients’ tests correlated with patients' improvement in the following session and whether their ability to follow patients' coaches correlated with immediate improvement. Transcriptions of 98 sessions from six brief psychodynamic psychotherapies were assessed by 12 independent raters. The patients’ plans were formulated, and tests and coaching communications were identified. The accuracy of the therapist’s responses to these tests and coaches was rated, and the impact of the therapist’s interventions on the patient’s following communication was measured. The results showed that when a therapist’s intervention passes a patient's tests, patients show signs of improvement both immediately and in the following session, and when therapists follow patients’ coaches, patients show signs of immediate improvement. The clinical implications and limitations of the findings are discussed.

Adapting goals and motivational messages on smartphones for motivation to walk

Physical activity is one of the most recognised means of disease prevention. While several studies investigated different techniques for motivating people to walk (e.g. adaptive goal setting), only few studies considered user profiles to personalise interactions. In this article, we propose a new interaction model which adapts walking goals and motivational messages in a static way (using the initial physical activity level) and in a dynamic way (using previous days’ performance). We explain how we implemented this model in a mobile application counting and displaying in real time the number of steps, an adapted daily goal and personalised motivational messages throughout the day. We describe two field studies conducted with 32 and 50 users over four weeks. We compare the impacts of adapted daily goals and personalised motivational messages on users’ step counts and motivation to walk. Participants using the adapted version of the mobile application displayed an increase in their motivation to walk after the intervention and were more physically active than participants using non adapted versions of the mobile application.

Examining sociodemographic and health-related characteristics as moderators of an mHealth intervention on physical activity outcomes in young adult cancer survivors.

This study explored whether sociodemographic and health-related characteristics moderated mHealth PA intervention effects on total and moderate-to-vigorous physical activity (MVPA) at 6 months, relative to a self-help condition among young adult cancer survivors (YACS). We conducted exploratory secondary analyses of data from a randomized controlled trial among 280 YACS. All participants received digital tools; intervention participants also received lessons, adaptive goals, tailored feedback, text messages, and Facebook prompts. Potential moderators were assessed in baseline questionnaires. PA was measured at baseline and 6 months with accelerometers. Linear model repeated measures analyses examined within- and between-group PA changes stratified by levels of potential moderator variables. Over 6 months, the intervention produced MVPA increases that were ≥ 30min/week compared with the self-help among participants who were males (28.1 vs. -7.7, p = .0243), identified with racial/ethnic minority groups (35.2 vs. -8.0, p = .0006), had baseline BMI of 25-30 (25.4 vs. -7.2, p = .0034), or stage III/IV cancer diagnosis (26.0 vs. -6.8, p = .0041). Intervention participants who were ages 26-35, college graduates, married/living with a partner, had a solid tumor, or no baseline comorbidities had modest MVPA increases over 6 months compared to the self-help (ps = .0163-.0492). Baseline characteristics did not moderate intervention effects on total PA. The mHealth intervention was more effective than a self-help group at improving MVPA among subgroups of YACS defined by characteristics (sex, race, BMI, cancer stage) that may be useful for tailoring PA interventions. These potential moderators can guide future optimization of PA interventions for YACS. NCT03569605.

Prescribed fire placement matters more than increasing frequency and extent in a simulated Pacific Northwest landscape

AbstractPrescribed fire has been increasingly promoted to reduce wildfire risk and restore fire‐adapted ecosystems. Yet, the complexities of forest ecosystem dynamics in response to disturbances, climate change, and drought stress, combined with myriad social and policy barriers, have inhibited widespread implementation. Using the forest succession model LANDIS‐II, we investigated the likely impacts of increasing prescribed fire frequency and extent on wildfire severity and forest carbon storage at local and landscape scales. Specifically, we ask how much prescribed fire is required to maintain carbon storage and reduce the severity and extent of wildfires under divergent climate change scenarios? We simulated four prescribed fire scenarios (no prescribed fire, business‐as‐usual, moderate increase, and large increase) in the Siskiyou Mountains of northwest California and southwest Oregon. At the local site scale, prescribed fires lowered the severity of projected wildfires and maintained approximately the same level of ecosystem carbon storage when reapplied at a ~15‐year return interval for 50‐year simulations. Increased frequency and extent of prescribed fire decreased the likelihood of aboveground carbon combustion during wildfire events. However, at the landscape scale, prescribed fire did not decrease the projected severity and extent of wildfire, even when large increases (up to 10× the current levels) of prescribed fire were simulated. Prescribed fire was most effective at reducing wildfire severity under a climate change scenario with increased temperature and precipitation and on sites with north‐facing aspects and slopes greater than 30°. Our findings suggest that placement matters more than frequency and extent to estimate the effects of prescribed fire, and that prescribed fire alone would not be sufficient to reduce the risk of wildfire and promote carbon sequestration at regional scales in the Siskiyou Mountains. To improve feasibility, we propose targeting areas of high concern or value to decrease the risk of high‐severity fire and contribute to meeting climate mitigation and adaptation goals. Our results support strategic and targeted landscape prioritization of fire treatments to reduce wildfire severity and increase the pace and scale of forest restoration in areas of social and ecological importance, highlighting the challenges of using prescribed fire to lower wildfire risk.

State Responsibility for Environmental Damage from Climate Change under the No-Harm Principle

This research delves into the critical role of states in addressing environmental damage stemming from climate change, underpinned by the fundamental principle of “no harm”. Climate change represents a profound threat to both the global ecosystem and human welfare, necessitating concerted action from all nations. The no harm principle places an obligation on states to undertake effective measures to mitigate greenhouse gas emissions, facilitate adaptation to climate change impacts, and mitigate adverse effects on vulnerable communities. This research uses normative legal research methods with a regulatory and contextual approach. By examining both theoretical frameworks and legal instruments, the study aims to elucidate the responsibilities of states and the mechanisms through which they can fulfill these obligations. A central focus of this research is the examination of how nations collaborate internationally to achieve climate change mitigation and adaptation goals through global agreements. These agreements serve as frameworks for collective action, guiding states in their efforts to combat climate change and minimize its harmful effects. By emphasizing the no harm principle, this study seeks to deepen our understanding of state responsibility for climate change and associated environmental damage. It underscores the imperative for robust collaborative endeavors aimed at safeguarding the global environment and ensuring a sustainable future for all. Through enhanced awareness and collective action, states can fulfill their duty to protect the planet and mitigate the adverse impacts of climate change on present and future generations.

Diagnosing Complex Organisations with Diverse Cultures—Part 2: Application to ASEAN

In this paper, the second part of a two-part series, we explore the cultural stability of the Association of Southeast Asian Nations (ASEAN). The analytical framework adopted, formulated on a background of social cybernetics, uses Mindset Agency Theory (MAT) within a metacybernetic framework. Our exploration involves a thorough investigation of signs pointing to cultural instability, identification of potential pathologies, and the provision of insights into the underlying dynamics within ASEAN. Expanding on the theoretical foundation established in the first part, we explore the notion that regional organisations (ROs) like ASEAN can be viewed as complex adaptive systems with agency. Heterogeneity of RO membership can be both beneficial and detrimental, especially when this delivers cultural diversity. If detrimental, pathologies can arise that affect both ROs’ institutional dynamics and their affiliated regional organisations, a significant interest of this paper. In response to certain cybernetic aspects introduced in part 1 of the research, MAT is shown to be a specialised framework imbued with systemic and reflexive elements. Through this, the analysis sheds light on how an agency’s mindset connects with its behaviour and performance. ROs exhibit coherence in their operations when they successfully achieve adaptive goals. ROs, as agencies defined through a population of state agents, have mutual relationships and are encouraged to pursue shared regional objectives, such as economic growth, social welfare, security, and democracy. However, in highly diverse cultural environments, this poses unique challenges to achieving and maintaining cultural stability. The analysis scrutinises ASEAN’s behaviour, relating it to manifestations of cultural instability, and deduces conditions that encompass an inability to undertake collective action, covert narcissism, and a lack of authority. Employing MAT as a diagnostic tool to comprehend ASEAN’s intricate nature, the paper concludes with practical recommendations aimed at enhancing ASEAN’s cultural health and sustainability. The ultimate vision is to foster a more integrated and proactive regional entity.

A Proactive Self-Adaptation Approach Based on Ensemble Prediction for Service-Based Systems

Service-based systems (SBSs) are a unique category of software systems that dynamically combine various third-party services at runtime to deliver complex and adaptive functionality. This dynamic composition introduces a high level of unpredictability and uncertainty, creating potential anomalies and exerting significant pressure on system maintenance. To tackle this challenge, the conventional approach involves employing prediction-based proactive self-adaptation. Despite the prevalence of existing approaches emphasizing prediction accuracy, the critical aspect of “earliness” in predictions is often overlooked. Striking a balance between early and accurate predictions is paramount in practice. In response, we propose Proactive Self-Adaptation based on Ensemble Prediction (PSA-EP) to effectively balance the trade-off between the prediction earliness and accuracy of in SBSs. At the heart of PSA-EP lies an ensemble prediction model built upon a deep neural network and an enhanced long short-term memory (DNN-ELSTM) architecture. PSA-EP is crafted to empower SBSs to adapt to the inherent unpredictability and instability, facilitating the achievement of their adaptation goals. This adaptation mechanism not only enables effective prediction and analysis of adaptation goal violations but also addresses the reliability and performance of service level agreements (SLAs) governing quality of service (QoS). We evaluated the performance of PSA-EP in a decentralized tele-assistance system using four key metrics, and the experimental results, examined from various perspectives, underscore its exceptional performance.

Adaptive goal recognition using process mining techniques

Goal Recognition (GR) is a research problem that studies ways to infer the goal of an intelligent agent based on its observed behavior and knowledge of the environment in which the agent operates. A common assumption of GR is that the environment is static. However, in many real-world scenarios, for example, recognizing customers’ preferences, it is necessary to recognize the goals of multiple agents or multiple goals of a single agent over an extended period. Therefore, it is reasonable to expect the environment to change throughout a series of goal recognition tasks. This paper presents three process mining-based solutions to the problem of adaptive GR in a changing environment implemented as different control strategies of a system for solving standard GR problems. As a standard GR system that gets controlled, we use the system grounded in process mining techniques, as it can adjust its internal GR mechanisms based on data collected while observing the operating agents. We evaluated our control strategies over synthetic and real-world datasets. The synthetic datasets were generated using the extended version of the Goal Recognition Amidst Changing Environments (GRACE) tool. The datasets account for different types of changes and drifts in the environment. The evaluation results demonstrate a trade-off between the GR performance over time and the effort invested in adaptations of the GR mechanisms of the system, showing that few well-planned adaptations can lead to a consistently high GR performance.