Complexity-based Approach Research Articles

Efficacy of Complexity-Based Target Selection for Treating Morphosyntactic Deficits in Children With Developmental Language Disorder and Children With Down Syndrome: A Single-Case Experimental Design.

Selecting targets for morphosyntactic intervention is a critical component of treatment planning. The complexity approach suggests that, by treating a complex morphosyntactic target, improvements will occur for the treated structure and for related, simpler structures. This study evaluated the efficacy of the complexity approach for treating morphosyntactic deficits by targeting a complex BE verb question structure for children with developmental language disorder (DLD) and children with Down syndrome (DS) and observing its impact on treated and untreated BE verb structures. We also explored whether etiology impacted our participants' treatment responses. Three participants with DLD and three with DS received treatment for the BE verb question structure in the context of a single-case multiple-baseline design across participants. Accuracy of production for the treated structure and untreated BE verb structures was measured across baseline, treatment, and posttreatment phases. Treatment of the complex BE verb question structure resulted in change on the treated structure for three participants (i.e., two with DLD and one with DS). Generalization of treatment to untreated, related BE verb structures occurred for all six participants. Outcomes indicated participants from both etiologies benefited from treatment. This study provides evidence supporting the use of a complexity-based approach for selecting morphosyntactic treatment targets for children with DLD and children with DS. Additional research is needed to identify specific characteristics that may influence individual treatment responses. https://doi.org/10.23641/asha.27018124.

Lifelong longitudinal assessment of the contribution of multi-fractal fluctuations to heart rate and heart rate variability in aging mice: role of the sinoatrial node and autonomic nervous system.

Aging is a major risk factor for sinoatrial node (SAN) dysfunction, which can impair heart rate (HR) control and heart rate variability (HRV). HR and HRV are determined by intrinsic SAN function and its regulation by the autonomic nervous system (ANS). The purpose of this study wasto use multi-scale multi-fractal detrended fluctuation analysis (MSMFDFA; a complexity-based approach to analyze multi-fractal dynamics) to longitudinally assess changes in multi-fractal HRV properties and SAN function in ECG time series recorded repeatedly across the full adult lifespan in mice. ECGs were recorded in anesthetized mice in baseline conditions and after autonomic nervous system blockade every three months beginning at 6months of age until the end of life. MSMFDFA was used to assess HRV and SAN function every three months between 6 and 27months of age. Intrinsic HR (i.e. HR during ANS blockade) remained relatively stable until 15months of age, and then progressively declined until study endpoint at 27months of age. MSMFDFA revealed sudden and rapid changes in multi-fractal properties of the ECG RR interval time series in aging mice. In particular, multi-fractal spectrum width (MFSW, a measure of multi-fractality) was relatively stable between 6months and 15months of age and then progressively increased at 27months of age. These changes in MFSW were evident in baseline conditions and during ANS blockade. Thus,intrinsic SAN function declines progressively during aging and is manifested by age-associated changes in multi-fractal HRV across the lifespan in mice, which can be accurately quantified by MSMFDFA.

An integrated gear tooth crack analysis of coupled electromechanical model: A complexity-based approach

This paper presents a novel approach by conducting a complexity-based study of an electromechanical (EM) gearbox system with tooth root cracks, utilizing an analytical model and motor current signals for the first time, in contrast to existing studies that primarily use experimental vibration data for complexity-based analyses. The coupled EM model is developed using the modified Lagrangian approach and by incorporating improved time-varying mesh stiffness and gear crack models. Refined composite multiscale dispersion entropy (RCMDE) is employed to evaluate system complexity at various crack depths. The analysis focuses on residual current signals and uncovers an inverse correlation between RCMDE values and the severity of cracks. To demonstrate the efficacy of the proposed approach, a comparison study is conducted between RCMDE and multiscale dispersion entropy (MDE) on both residual current and vibration signals. The results show that RCMDE provides superior stability and effectiveness compared to MDE. Additionally, it has been found that residual current signals offer more robust insights into system complexity than residual vibration signals. This study contributes valuable insights into the behavior of gearbox systems experiencing crack-induced faults, especially concerning short-time series data, highlighting the advantages of using motor current signals and RCMDE for complexity-based analyses.

A system-thinking approach for migration studies: an introduction

ABSTRACT Migration studies first took up a systems perspective in the 1970s to explain migration flows and their dynamics over time. Over the last decades, the dominant discourse and analysis in migration studies have remained constrained within the limits of the ‘migration system’. While the influence of the ‘wider environment’ on the migration system has been recognized, what the elements of the wider environment are and their mechanisms of influence remain poorly articulated. Through eight innovative contributions, this Special Issue seeks to contribute firstly, to unpacking the elements (i.e. the other systems) that constitute the ‘wider environment’ with which the components of the migration system (e.g. migrants, sending and receiving communities, institutions, policies, etc.) interact, and secondly, to disentangling the mutual influences between the migration system and this wider environment. This Special Issue as a whole suggests that the growing complexity of migration governance demands a complexity-based approach that acknowledges the multiple relations among systems. In this respect, the wider environment and its linkages with the migration system need to be better captured through an analytical approach based on system thinking.

Arctic weather variability and connectivity

The Arctic’s rapid sea ice decline may influence global weather patterns, making the understanding of Arctic weather variability (WV) vital for accurate weather forecasting and analyzing extreme weather events. Quantifying this WV and its impacts under human-induced climate change remains a challenge. Here we develop a complexity-based approach and discover a strong statistical correlation between intraseasonal WV in the Arctic and the Arctic Oscillation. Our findings highlight an increased variability in daily Arctic sea ice, attributed to its decline accelerated by global warming. This weather instability can influence broader regional patterns via atmospheric teleconnections, elevating risks to human activities and weather forecast predictability. Our analyses reveal these teleconnections and a positive feedback loop between Arctic and global weather instabilities, offering insights into how Arctic changes affect global weather. This framework bridges complexity science, Arctic WV, and its widespread implications.

Towards Optimal Moment Estimation in Streaming and Distributed Models

One of the oldest problems in the data stream model is to approximate the p th moment \(\Vert \mathbf {X}\Vert _p^p = \sum _{i=1}^n \mathbf {X}_i^p\) of an underlying non-negative vector \(\mathbf {X}\in \mathbb {R}^n\) , which is presented as a sequence of \(\mathrm{poly}(n)\) updates to its coordinates. Of particular interest is when \(p \in (0,2]\) . Although a tight space bound of \(\Theta (\epsilon ^{-2} \log n)\) bits is known for this problem when both positive and negative updates are allowed, surprisingly, there is still a gap in the space complexity of this problem when all updates are positive. Specifically, the upper bound is \(O(\epsilon ^{-2} \log n)\) bits, while the lower bound is only \(\Omega (\epsilon ^{-2} + \log n)\) bits. Recently, an upper bound of \(\tilde{O}(\epsilon ^{-2} + \log n)\) bits was obtained under the assumption that the updates arrive in a random order . We show that for \(p \in (0, 1]\) , the random order assumption is not needed. Namely, we give an upper bound for worst-case streams of \(\tilde{O}(\epsilon ^{-2} + \log n)\) bits for estimating \(\Vert \mathbf {X}\Vert _p^p\) . Our techniques also give new upper bounds for estimating the empirical entropy in a stream. However, we show that for \(p \in (1,2]\) , in the natural coordinator and blackboard distributed communication topologies, there is an \(\tilde{O}(\epsilon ^{-2})\) bit max-communication upper bound based on a randomized rounding scheme. Our protocols also give rise to protocols for heavy hitters and approximate matrix product. We generalize our results to arbitrary communication topologies G , obtaining an \(\tilde{O}(\epsilon ^{2} \log d)\) max-communication upper bound, where d is the diameter of G . Interestingly, our upper bound rules out natural communication complexity-based approaches for proving an \(\Omega (\epsilon ^{-2} \log n)\) bit lower bound for \(p \in (1,2]\) for streaming algorithms. In particular, any such lower bound must come from a topology with large diameter.

Conceptualizing distributive justice in education: a complexity theory perspective

AbstractOver the last two decades, complexity theory, which is designed to deal with systems of multiple interdependent variables, has been increasingly applied to analyse and shed light on various aspects of education. So far, however, complexity theory has rarely been used, if at all, to examine questions related to educational justice. This article offers a theoretical examination of some possible links between complexity theory and distributive justice in education. It asks how accepting the premise that education is a complex dynamic system should shape the way distributive justice in education is conceptualized and approached. It is argued that complexity theory challenges many assumptions on which the dominant approach for dealing with distributive justice in education rests. The article focuses on three subjects that stem from complexity theory but have implications for dealing with distributive justice in education: system diversity, reductionism, and change. Each of these subjects is examined separately, and some possible contributions of complexity theory to illuminating these subjects in relation to distributive justice in education are discussed. The article ends with a brief illustration of where a complexity-based approach to educational justice leads that focuses on school choice and contrasts it with Brighouse's views on this issue.

Identifying anomalous radio sources in the Evolutionary Map of the Universe Pilot Survey using a complexity-based approach

ABSTRACTThe Evolutionary Map of the Universe (EMU) large-area radio continuum survey will detect tens of millions of radio galaxies, giving an opportunity for the detection of previously unknown classes of objects. To maximize the scientific value and make new discoveries, the analysis of these data will need to go beyond simple visual inspection. We propose the coarse-grained complexity, a simple scalar quantity relating to the minimum description length of an image that can be used to identify unusual structures. The complexity can be computed without reference to the broader sample or existing catalogue data, making the computation efficient on new surveys at very large scales (such as the full EMU survey). We apply our coarse-grained complexity measure to data from the EMU Pilot Survey to detect and confirm anomalous objects in this data set and produce an anomaly catalogue. Rather than work with existing catalogue data using a specific source detection algorithm, we perform a blind scan of the area, computing the complexity using a sliding square aperture. The effectiveness of the complexity measure for identifying anomalous objects is evaluated using crowd-sourced labels generated via the Zooniverse.org platform. We find that the complexity scan identifies unusual sources, such as odd radio circles, by partitioning on complexity. We achieve partitions where 5 per cent of the data is estimated to be 86 per cent complete, and 0.5 per cent is estimated to be 94 per cent pure, with respect to anomalies and use this to produce an anomaly catalogue.

Which complexity? A review of typologies and a framework proposal for characterizing complexity-based approaches

PurposeThe purpose of this paper is twofold: (1) to map and analyze existing complexity typologies and (2) to develop a framework for characterizing complexity-based approaches.Design/methodology/approachThis study was conducted in three stages: (1) initial identification of typologies related to complexity following a structured procedure based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol; (2) backward and forward review to identify additional relevant typologies and (3) content analysis of the selected typologies, categorization and framework development.FindingsBased on 17 selected typologies, a comprehensive overview of complexity studies is provided. Each typology is described considering key concepts, contributions and convergences and differences between them. The epistemological, theoretical and methodological diversity of complexity studies was explored, allowing the identification of the main schools of thought and authors. A framework for characterizing complexity-based approaches was proposed including the following perspectives: ontology of complexity, epistemology of complexity, purpose and object of interest, methodology and methods and theoretical pillars.Originality/valueThis study examines the main typologies of complexity from an integrated and multidisciplinary perspective and, based on that, proposes a novel framework to understanding and characterizing complexity-based approaches.

Editorial: Complexity-based approaches in health communication

Editorial: Complexity-based approaches in health communication

Meta-Systemic Model of Transformation: A General Complexity-Based Approach in Political Science and International Relations

System-based research remains an important yet usually outdated and internally contradictory approach in political science and international relations. Based on concepts borrowed from physiology, cybernetics, and general system theory, the system-based approach popularised in the 1960s was cast away as outdated and ill-focused. Despite those systems, the theory was developed in natural sciences, eventually creating a paradigm more applicable to domestic and international politics. The weakest element of past systems (like the one proposed by D. Easton) was that they did not allow for a sudden and catastrophic transformation and lacked emergence. This paper aims to present a model that would allow for the system’s ordinary and catastrophic transformation. The complex adaptive system features were defined using relevant literature on a paradigm of complexity. Connecting it with the propositions of D. Easton, R. Axelrod, and M. Cohen, as well as R. Jervis, such a model was constructed. The theoretical introduction is supplanted with a general case study of the early phases of the Arab Spring in Tunisia. The model mirrors the complex systems’ dynamics, considering the agent-structure problem.

Nature-based Solutions for climate adaptation and mitigation in Deltas and coastal areas.

Coastal areas, especially deltas, are some of the most urbanized territories in the world. Historically, the natural richness and strategic location of these areas resulted attractive for human settlements worldwide. However, these areas are also highly vulnerable due to rapid and unplanned urbanization, land use changes, and extreme climate events. There is an international consensus on the need for new strategies for sustainable development to help cities to mitigate the effects of climate change, as well as adapt to new changing conditions in a context of increasing uncertainty. This paper will explore the specific aspects of urban deltas and coastal areas, from a complexity-based approach, and analyse Nature-based Solutions as alternatives towards sustainable development in these areas.

Eat Walk Engage: Enabling acute care teams to deliver consistent fundamentals of care for older people.

Fundamentals of care are particularly important for older people in acute inpatient settings, who are at increased risk of serious hospital-associated complications like delirium and functional decline. These complications occur due to interactions between clinical complexity and the complex processes and context of hospital care and can be reduced by consistent attention to the fundamentals of care. This paper aims to illustrate of how multi-level nursing leadership of fundamentals of care can be supported to emerge within complex multidisciplinary delivery systems in acute care. Discussion paper informed by clinical and organizational experience of a multidisciplinary leadership team and complexity leadership theory. We provide a series of vignettes as practical illustrations of a successful multidisciplinary improvement program called Eat Walk Engage which supports the delivery of better care for older inpatients, significantly reducing delirium. We argue that taking a broader complexity-based approach including collaborative multidisciplinary engagement, iterative and integrated interventions and appropriate knowledge translation frameworks can enable emergent leadership by nurses at all levels. This promising approach to improving care for older patients requires organizational support for facilitation and reflective practice, and for meaningful data to support change. Our discussion challenges nursing leaders to support the time, agency and connections their nursing staff need in order to emerge as local leaders in fundamental care. The debate around scope and responsibilities for fundamentals of care in hospital care has important practical implications for conceptualizing leadership and accountability for improvement. Our discussion illustrates how a structured multidisciplinary approach that acknowledges and navigates complexity can empower nurses to lead and improve outcomes of older patients in acute care.

Blue uncertainty: Warding off systemic risks in the Anthropocene—Lessons from COVID-19

COVID-19 has made evident the complex interdependence of social and ecological systems and that to reduce the risk of future zoonotic pandemics we must safeguard nature. Approaches based on complexity science taking into account that interdependence and its associated systemic risks must be mainstreamed in current policy making, in general. However, at present, that could result in failure for three main reasons: (1) those approaches might be too sophisticated for current policy making pursuing sustainable development; (2) the reductionist views from conventional economics still deeply influence economic and environmental policy making; (3) it is unlikely that far-reaching policies aimed at stimulating post-pandemic economic development can be steered through radically innovative approaches that remain untested. Here, using COVID-19 as an example, I suggest that the use of innovative complexity-based approaches could be enabled through intermediary approaches equipped to resonate with the mindset pervading current policy making.

Navigating Governance Tensions to Enhance the Impact of Partnerships with the Private Sector for the SDGs

Partnering with the private sector is a key modality in development cooperation to achieve the Sustainable Development Goals (SDGs). Despite their increasing importance, such Public-Private Partnerships for Development (PPPD) experience major challenges in defining, assessing and reporting on their actual impact. This paper explores why, and how this can be improved. We engage in a qualitative synthesis review of academic, gray literature and evaluation reports of public-private programs of development agencies. We identify challenges, tensions and contradictions that affect a proper understanding and assessment of the impacts of such partnerships. The analysis shows that the main challenge in understanding and assessing impacts is the double governance logic that emerges in PPPD monitoring and evaluation (M&E). While M&E functions as an accountability and risk mitigation approach, it should also support collaborative characteristics of PPPDs such as trust and power-sharing, in order to enhance impactful PPPDs. Enhancing the impact of PPPDs for the SDGs requires bridging the divide between (a) result-based, upward accountability monitoring and evaluation approaches and (b) emerging learning, participatory and complexity-based approaches. The paper provides suggestions on how to navigate these governance tensions by using a paradoxical lens.

A Signal Complexity-Based Approach for AM–FM Signal Modes Counting

Frequency modulated signals appear in many applied disciplines, including geology, communication, biology and acoustics. They are naturally multicomponent, i.e., they consist of multiple waveforms, with specific time-dependent frequency (instantaneous frequency). In most practical applications, the number of modes—which is unknown—is needed for correctly analyzing a signal; for instance for separating each individual component and for estimating its instantaneous frequency. Detecting the number of components is a challenging problem, especially in the case of interfering modes. The Rényi Entropy-based approach has proven to be suitable for signal modes counting, but it is limited to well separated components. This paper addresses this issue by introducing a new notion of signal complexity. Specifically, the spectrogram of a multicomponent signal is seen as a non-stationary process where interference alternates with non-interference. Complexity concerning the transition between consecutive spectrogram sections is evaluated by means of a modified Run Length Encoding. Based on a spectrogram time-frequency evolution law, complexity variations are studied for accurately estimating the number of components. The presented method is suitable for multicomponent signals with non-separable modes, as well as time-varying amplitudes, showing robustness to noise.

Explicating the social constructionist perspective on crisis communication and crisis management research: a review of communication and business journals

ABSTRACT There is a growing body of literature concerning the social construction of crisis. This study aimed to clarify inconsistencies regarding the social constructionist perspective by examining how the key terms of social constructionism are applied in crisis communication and crisis management studies. Through an analysis of 65 scholarly works in both communication and business journals, this study proposes a four-dimensional model of social constructionist crisis research (SCCR): (1) cause – the objective facticity and subjective meaning of crisis; (2) text – a constitutive view of language; (3) meaning – multiple actors and multiple realities; and (4) context – societal context awareness. Next, three approaches to SCCR are identified: a terminological approach, a framing approach, and a complexity-based approach. Lastly, the implications of social constructionism for theory and methodology development in crisis communication are discussed.

Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier

The El Niño Southern Oscillation (ENSO) is one of the most prominent interannual climate phenomena. Early and reliable ENSO forecasting remains a crucial goal, due to its serious implications for economy, society, and ecosystem. Despite the development of various dynamical and statistical prediction models in the recent decades, the "spring predictability barrier" remains a great challenge for long-lead-time (over 6 mo) forecasting. To overcome this barrier, here we develop an analysis tool, System Sample Entropy (SysSampEn), to measure the complexity (disorder) of the system composed of temperature anomaly time series in the Niño 3.4 region. When applying this tool to several near-surface air temperature and sea surface temperature datasets, we find that in all datasets a strong positive correlation exists between the magnitude of El Niño and the previous calendar year's SysSampEn (complexity). We show that this correlation allows us to forecast the magnitude of an El Niño with a prediction horizon of 1 y and high accuracy (i.e., root-mean-square error = 0.23° C for the average of the individual datasets forecasts). For the 2018 El Niño event, our method forecasted a weak El Niño with a magnitude of 1.11±0.23° C. Our framework presented here not only facilitates long-term forecasting of the El Niño magnitude but can potentially also be used as a measure for the complexity of other natural or engineering complex systems.

Being in Uncertainties: An Inquiry-based Model Leveraging Complexity in Teaching-Learning

Education is traditionally structured as a closed system, privileging result-driven methods that offer control and predictability. In recent decades this reductionist approach has been effectively challenged by interdisciplinary work in complex systems theory, revealing myriad levels of orderly disorder that make either-or, linear instruction an inadequate norm. Narrowing the broad implications of a complexity lens on education, this paper focuses on generative uncertainty in teaching-learning, a paradoxical state of epistemological and creative growth described by English poet John Keats as "the negative capability of being in uncertainties, mysteries, doubts." Opportunities to advance this potentiating capacity are especially abundant in constructivist curricula, for example the Methods of Inquiry (MoI) program discussed herein. MoI's open, complexity-based approach foregrounds uncertainty-tolerance and other interactive dispositions, providing a fluid structure for the emergent, often turbulent nature of meaning production. Such dynamic attitudes and strategies are seen as essential for any classroom practice that seeks to transform as well as inform, to guide and also empower.

The Emergence of Integrative Peacebuilding: A Complexity-Based Approach to Professional Leadership Development

Integrative Peacebuilding draws on integral theory, complexity theory, and mimetic theory to develop an integrative orientation that leads to integrative actions leading to peacebuilding actors mutually contributing to each other’s goals. The priority is to contribute to the well-being of those from Fragile or Conflict Affected States (FCAS); in particular, it is imperative to enhance the conditions under which they might take independent action to address their own challenges. All of this entails building relationships of mutual trust amongst and between intervening actors and their counterparts within FCAS.