Swift development of technology for monitoring complex structures demands major attention on the precision of damage detection methods. The early detection of any type of deterioration or degradation of structures is of paramount importance to avoid sudden catastrophic failure. It warns users about the impending state of the system. At the initiation of a crack or some other system faults, the system may generate a time-varying state of crack under ambient vibration. It represents the nonlinear breathing phenomena of crack. An assessment of this degree of nonlinearity can be utilized for the detection, localization, and quantification of breathing cracks. Appropriate modeling of such cracks is thus necessary to capture distinctive nonlinear features. Recognizing this importance, various methods of modeling and nonlinear system identification which have been employed in the past for the detection of breathing crack are reviewed. The present study also explores some of the available vibration as well as acoustic-based damage identification techniques, chronologically connecting their evolutions. It summarizes the advantages and limitations of the methods to inspect potential future applications. The future scopes drawn from this review are highlighted to pave the path of wide-spread applications of nonlinear features of crack.

UNASSIGNED: Dynamic systems theory and complexity theory (DST/CT) is a framework explaining how complex systems change and adapt over time. In psychotherapy, DST/CT can be used to understand how a person\'s mental and emotional state changes during therapy incorporating higher levels of complexity. This study aimed to systematically review the variability of DST/CT methods applied in psychotherapy research.
UNASSIGNED: A primary studies search was conducted in the EBSCO and Web of Knowledge databases, extracting information about the analyzed DST/CT phenomena, employed mathematical methods to investigate these phenomena, descriptions of specified dynamic models, psychotherapy phenomena, and other information regarding studies with empirical data (e.g., measurement granularity).
UNASSIGNED: After screening 38,216 abstracts and 4,194 full texts, N = 41 studies published from 1990 to 2021 were identified. The employed methods typically included measures of dynamic complexity or chaoticity. Computational and simulation studies most often employed first-order ordinary differential equations and typically focused on describing the time evolution of client-therapist dyadic influences. Eligible studies with empirical data were usually based on case studies and focused on data with high time intensity of within-session dynamics.
UNASSIGNED: This review provides a descriptive synthesis of the current state of the proliferation of DST/CT methods in the psychotherapy research field.

Quality in education is one of the 17 goals in the United Nations\' sustainable agenda for 2030, presupposing careful planning and assessment of learning. Traditional planning in sports education (either in training or school settings) largely adopts pre-determined learning sequences and temporal milestones that, in theory, enhance the learning process. However, learning is a context-dependent, non-linear process with considerable intra- and interindividual variability, whereby planning and assessment should also be non-linear. In this narrative review, the main findings suggest that the specific teaching or training contents and their relative (i.e., ordering or sequencing) and absolute timing (i.e., the specific time point where certain learning or adaptations are expected) should vary depending on the learners and the context. In a process-oriented perspective, this requires flexible planning and the establishment of ongoing bidirectional links between planning and assessment. In this framework, assessment should be a flexible, evolving, and daily pedagogical tool instead of a set of formal checkpoints. We further explored how planning and assessment could be linked to provide an ongoing feedback loop that respects the individuality of each learner and its context, and therefore hope this review helps bring about a change in current planning and assessment paradigms in sports education.

The assessment of time irreversibility, i.e., of the lack of invariance of the statistical properties of a system under the operation of time reversal, is a topic steadily gaining attention within the research community. Irreversible dynamics have been found in many real-world systems, with alterations being connected to, for instance, pathologies in the human brain, heart and gait, or to inefficiencies in financial markets. Assessing irreversibility in time series is not an easy task, due to its many aetiologies and to the different ways it manifests in data. It is thus not surprising that several numerical methods have been proposed in the last decades, based on different principles and with different applications in mind. In this contribution we review the most important algorithmic solutions that have been proposed to test the irreversibility of time series, their underlying hypotheses, computational and practical limitations, and their comparative performance. We further provide an open-source software library that includes all tests here considered. As a final point, we show that \"one size does not fit all\", as tests yield complementary, and sometimes conflicting views to the problem; and discuss some future research avenues.

Lake water-level fluctuation is a complex and dynamic process, characterized by high stochasticity and nonlinearity, and difficult to model and forecast. In recent years, applications of machine learning (ML) models have yielded substantial progress in forecasting lake water-level fluctuations. This paper presents a comprehensive review of the applications of ML models for modeling water-level dynamics in lakes. Among the many existing ML models, seven popular ML model types are reviewed: (1) artificial neural network (ANN); (2) support vector machine (SVM); (3) artificial neuro-fuzzy inference system (ANFIS); (4) hybrid models, such as hybrid wavelet-artificial neural network (WA-ANN) model, hybrid wavelet-artificial neuro-fuzzy inference system (WA-ANFIS) model, and hybrid wavelet-support vector machine (WA-SVM) model; (5) evolutionary models, such as gene expression programming (GEP) and genetic programming (GP); (6) extreme learning machine (ELM); and (7) deep learning (DL). Model inputs, data split, model performance criteria, and model inter-comparison as well as the associated issues are discussed. The advantages and limitations of the established ML models are also discussed. Some specific directions for future research are also offered. This review provides a new vision for hydrologists and water resources planners for sustainable management of lakes.

The relationship between BMI and mortality has been extensively investigated in the general population; however, it is less clear in people with type 2 diabetes. We aimed to assess the association of BMI with all-cause and cardiovascular mortality in individuals with type 2 diabetes mellitus.
We searched electronic databases up to 1 March 2016 for prospective studies reporting associations for three or more BMI groups with all-cause and cardiovascular mortality in individuals with type 2 diabetes mellitus. Study-specific associations between BMI and the most-adjusted RR were estimated using restricted cubic splines and a generalised least squares method before pooling study estimates with a multivariate random-effects meta-analysis.
We included 21 studies including 24 cohorts, 414,587 participants, 61,889 all-cause and 4470 cardiovascular incident deaths; follow-up ranged from 2.7 to 15.9 years. There was a strong nonlinear relationship between BMI and all-cause mortality in both men and women, with the lowest estimated risk from 31-35 kg/m2 and 28-31 kg/m2 (p value for nonlinearity <0.001) respectively. The risk of mortality at higher BMI values increased significantly only in women, whilst lower values were associated with higher mortality in both sexes. Limited data for cardiovascular mortality were available, with a possible inverse linear association with BMI (higher risk for BMI <27 kg/m2).
In type 2 diabetes, BMI is nonlinearly associated with all-cause mortality with lowest risk in the overweight group in both men and women. Further research is needed to clarify the relationship with cardiovascular mortality and assess causality and sex differences.