Analyzing and identifying predictable time range for stress prediction based on chaos theory and deep learning.

Propose: Stress is a common problem globally. Prediction of stress in advance could help people take effective measures to manage stress before bad consequences occur. Considering the chaotic features of human psychological states, in this study, we integrate deep learning and chaos theory to address the stress prediction problem. Methods: Based on chaos theory, we embed one's seemingly disordered stress sequence into a high dimensional phase space so as to reveal the underlying dynamics and patterns of the stress system, and meanwhile are able to identify the stress predictable time range. We then conduct deep learning with a two-layer (dimension and temporal) attention mechanism to simulate the nonlinear state of the embedded stress sequence for stress prediction. Results: We validate the effectiveness of the proposed method on the public available Tesserae dataset. The experimental results show that the proposed method outperforms the pure deep learning method and Chaos method in both 2-label and 3-label stress prediction. Conclusion: Integrating deep learning and chaos theory for stress prediction is effective, and can improve the prediction accuracy over 2% and 8% more than those of the deep learning and the Chaos method respectively. Implications and further possible improvements are also discussed at the end of the paper.

Chaotic RIME optimization algorithm with adaptive mutualism for feature selection problems.

The RIME optimization algorithm is a newly developed physics-based optimization algorithm used for solving optimization problems. The RIME algorithm proved high-performing in various fields and domains, providing a high-performance solution. Nevertheless, like many swarm-based optimization algorithms, RIME suffers from many limitations, including the exploration-exploitation balance not being well balanced. In addition, the likelihood of falling into local optimal solutions is high, and the convergence speed still needs some work. Hence, there is room for enhancement in the search mechanism so that various search agents can discover new solutions. The authors suggest an adaptive chaotic version of the RIME algorithm named ACRIME, which incorporates four main improvements, including an intelligent population initialization using chaotic maps, a novel adaptive modified Symbiotic Organism Search (SOS) mutualism phase, a novel mixed mutation strategy, and the utilization of restart strategy. The main goal of these improvements is to improve the variety of the population, achieve a better balance between exploration and exploitation, and improve RIME's local and global search abilities. The study assesses the effectiveness of ACRIME by using the standard benchmark functions of the CEC2005 and CEC2019 benchmarks. The proposed ACRIME is also applied as a feature selection to fourteen various datasets to test its applicability to real-world problems. Besides, the ACRIME algorithm is applied to the COVID-19 classification real problem to test its applicability and performance further. The suggested algorithm is compared to other sophisticated classical and advanced metaheuristics, and its performance is assessed using statistical tests such as Wilcoxon rank-sum and Friedman rank tests. The study demonstrates that ACRIME exhibits a high level of competitiveness and often outperforms competing algorithms. It discovers the optimal subset of features, enhancing the accuracy of classification and minimizing the number of features employed. This study primarily focuses on enhancing the equilibrium between exploration and exploitation, extending the scope of local search.

A two-phase spatiotemporal chaos-based protocol for data integrity in IoT.

One of the biggest problems with Internet of Things (IoT) applications in the real world is ensuring data integrity. This problem becomes increasingly significant as IoT expands quickly across a variety of industries. This study presents a brand-new data integrity methodology for Internet of Things applications. The "sequence sharing" and "data exchange" stages of the suggested protocol are divided into two parts. During the first phase, each pair of nodes uses a new chaotic model for securely exchanging their identity information to generate a common sequence. This phase's objectives include user authentication and timing calculations for the second phase of the recommended method's packet validation phase. The recommended approach was tested in numerous settings, and various analyses were taken into account to guarantee its effectiveness. Also, the results were compared with the conventional data integrity control protocol of IoT. According to the results, the proposed method is an efficient and cost-effective integrity-ensuring mechanism with eliminates the need for third-party auditors and leads to reducing energy consumption and packet overhead. The results also show that the suggested approach is safe against a variety of threats and may be used as a successful integrity control mechanism in practical applications.

Chaos theory in the understanding of COVID-19 pandemic dynamics.

The chaos theory, a field of study in mathematics and physics, offers a unique lens through which to understand the dynamics of the COVID-19 pandemic. This theory, which deals with complex systems whose behavior is highly sensitive to initial conditions, can provide insights into the unpredictable and seemingly random nature of the pandemic's spread. In this review, we will discuss some literature data with the aim of showing how chaos theory could provide valuable perspectives in understanding the complex and dynamic nature of the COVID-19 pandemic. In particular, we will emphasize how the chaos theory can help in dissecting the unpredictable, non- linear progression of the disease, the importance of initial conditions, and the complex interactions between various factors influencing its spread. These insights are crucial for developing effective strategies to manage and mitigate the impact of the pandemic.

Astrovirology and terrestrial life survival.

Microbial organisms have been implicated in several mass extinction events throughout Earth's planetary history. Concurrently, it can be reasoned from recent viral pandemics that viruses likely exacerbated the decline of life during these periods of mass extinction. The fields of exovirology and exobiology have evolved significantly since the 20th century, with early investigations into the varied atmospheric compositions of exoplanets revealing complex interactions between metallic and non-metallic elements. This diversity in exoplanetary and stellar environments suggests that life could manifest in forms previously unanticipated by earlier, more simplistic models of the 20th century. Non-linear theories of complexity, catastrophe, and chaos (CCC) will be important in understanding the dynamics and evolution of viruses.

Optimum placement of distributed generation resources, capacitors and charging stations with a developed competitive algorithm.

This study presents a novel approach for the optimal placement of distributed generation (DG) resources, electric vehicle (EV) charging stations, and shunt capacitors (SC) in power distribution systems. The primary objective is to improve power efficiency and voltage profiles while considering practical and nonlinear constraints. The proposed model combines competitive search optimization (CSO) with fuzzy and chaotic theory to develop an efficient and effective solution. The use of fuzzy theory in the model enables the identification of optimal locations for DG sources and SCs, leading to significant enhancements in power index, generation, power losses, and system voltage. Moreover, the proposed fuzzy method is employed to determine the best locations for EV charging stations, further optimizing the overall system performance. The theoretical analysis demonstrates substantial improvements in both accuracy and convergence speed, highlighting the robustness of the proposed approach. In addition, the utilization of chaos theory enhances the local search optimization process, making the proposed method more efficient in finding high-quality solutions. To validate the performance of the model, extensive simulations are conducted on a 69-bus distribution system and various test functions. The results consistently reveal the superiority of the proposed method compared to other conventional optimization techniques. The key contribution of this study lies in its development of a comprehensive and efficient approach for the optimal placement of DG, EV charging stations, and SCs in power distribution systems. The integration of CSO, fuzzy theory, and chaotic theory enables the simultaneous consideration of multiple objectives and constraints, resulting in enhanced power dissipation reduction and voltage profile improvement. The obtained results demonstrate the practical applicability and superiority of the proposed method, which can significantly benefit power system planners and operators in real-world scenarios.

A study on the dynamic impact of carbon emission trading on green and high-quality development.

As a pivotal element in market mechanisms, carbon trading is increasingly recognized as crucial for achieving China's Carbon Peaking and Carbon Neutrality Goals. This study introduces a comprehensive dynamic model, integrating carbon trading, emissions, economic growth, and green technology innovation, to offer a holistic understanding of the interplay between these domains. Utilizing principles from nonlinear dynamics and chaos theory, the model is adept at simulating various scenarios and assessing the effectiveness of government policies in stabilizing these complex systems. In-depth analysis provided by this research sheds light on the nuanced impact of carbon trading policies on sustainable development. Key findings highlight (1) Carbon trading's essential role as a catalyst in propelling sustainable and high-quality growth. (2) A strong positive relationship is observed between the sophistication of the carbon trading mechanism and its effectiveness in stimulating green technology innovation and fostering high-quality green development. Notably, carbon trading's influence on green technology innovation markedly enhances the efficacy of carbon emission reduction strategies. (3) Government regulations are instrumental in augmenting carbon prices, thus incentivizing increased corporate participation in emission reduction and enhancing the overall impact of carbon emission reduction. Nevertheless, the study identifies a critical threshold in regulatory intensity, beyond which there is a risk of system destabilization ( a 3 ≥ 0.032 ). These findings underscore the imperative for developing an integrated national carbon emission trading market, prioritizing sustainable growth strategies and diligently pursuing China's environmental objectives.

Safety assessment of sand casting explosion accidents by testing cavity pressure of the sand mold to protect employee health.

Excessive cavity pressure may result in a sand casting explosion, and corresponding measures should be adopted to prevent these consequences. In this study, the pressure variations in the cavity were first investigated based upon on-site testing by taking the resin contents into consideration, and then the evolution characteristics of sand casting explosion accidents were analyzed in depth by system dynamics, chaos theory, and the bow-tie model. When the resin contents are 1.3 wt%, 1.4 wt%, and 1.5 wt%, the pressures of the gas vent increase by 27.0 Pa, 32.8 Pa, and 35.6 Pa, respectively. To reduce the pressure of the cavity, the resin content should be reduced. The evolutionary process of sand casting explosion accidents has a noticeable butterfly effect and randomness, whose occurrence is comprehensively affected by human, object, environment, management and emergency subsystems. The leading causes of sand casting explosion accidents mainly include the extensive gas evolution characteristics of foundry sand, cavity exhaust blockage, and inadequate safety monitoring. The leading consequences of sand casting explosion accidents mainly include casualties, secondary disasters, and social panic. The implications of these findings concerning sand casting explosion accidents can be regarded as the foundation for accident prevention in practice.

"Impending illness" Concept Research and Modern Interpretation / 中医杂志

This paper elucidates the connotation and extension of the concept of "impending illness" from the perspective of logic. The connotation of impending illness refers to the intermediate state between health （not yet sick） and disease （already sick）， characterized by the initial instability of yin and yang or a low-level balance of yin and yang as its internal mechanism， and mild dysfunctions in physiological， psychological， and social functions as its manifestations. The extension of impending illness includes latent disease status and pre-disease status. By adopting a creative hermeneutic approach to explore the deep-seated connotations of impending illness， this paper systematically analyzes the concept of impending illness from five levels： the existential level， the intentional level， the implicational level， the situational level， and the necessitational level， arguing that impending illness represents the unity of ambiguity and precision， the deviation of the human body system from homeostasis within a certain range， and an important component of the improvement of the traditional Chinese medicine health management system. Thoroughly explicating and clarifying the concept of "impending illness" can provide theoretical references for the study of transitional states between health and disease and the improvement of the traditional Chinese medicine health management system.

A Catastrophic Biodiversity Loss in the Environment Is Being Replicated on the Skin Microbiome: Is This a Major Contributor to the Chronic Disease Epidemic?

There has been a catastrophic loss of biodiversity in ecosystems across the world. A similar crisis has been observed in the human gut microbiome, which has been linked to "all human diseases affecting westernized countries". This is of great importance because chronic diseases are the leading cause of death worldwide and make up 90% of America's healthcare costs. Disease development is complex and multifactorial, but there is one part of the body's interlinked ecosystem that is often overlooked in discussions about whole-body health, and that is the skin microbiome. This is despite it being a crucial part of the immune, endocrine, and nervous systems and being continuously exposed to environmental stressors. Here we show that a parallel biodiversity loss of 30-84% has occurred on the skin of people in the developed world compared to our ancestors. Research has shown that dysbiosis of the skin microbiome has been linked to many common skin diseases and, more recently, that it could even play an active role in the development of a growing number of whole-body health problems, such as food allergies, asthma, cardiovascular diseases, and Parkinson's, traditionally thought unrelated to the skin. Damaged skin is now known to induce systemic inflammation, which is involved in many chronic diseases. We highlight that biodiversity loss is not only a common finding in dysbiotic ecosystems but also a type of dysbiosis. As a result, we make the case that biodiversity loss in the skin microbiome is a major contributor to the chronic disease epidemic. The link between biodiversity loss and dysbiosis forms the basis of this paper's focus on the subject. The key to understanding why biodiversity loss creates an unhealthy system could be highlighted by complex physics. We introduce entropy to help understand why biodiversity has been linked with ecosystem health and stability. Meanwhile, we also introduce ecosystems as being governed by "non-linear physics" principles-including chaos theory-which suggests that every individual part of any system is intrinsically linked and implies any disruption to a small part of the system (skin) could have a significant and unknown effect on overall system health (whole-body health). Recognizing the link between ecosystem health and human health allows us to understand how crucial it could be to maintain biodiversity across systems everywhere, from the macro-environment we inhabit right down to our body's microbiome. Further, in-depth research is needed so we can aid in the treatment of chronic diseases and potentially change how we think about our health. With millions of people currently suffering, research to help mitigate the crisis is of vital importance.

Fractal-Based Hybrid Cryptosystem: Enhancing Image Encryption with RSA, Homomorphic Encryption, and Chaotic Maps.

Protecting digital data, especially digital images, from unauthorized access and malicious activities is crucial in today's digital era. This paper introduces a novel approach to enhance image encryption by combining the strengths of the RSA algorithm, homomorphic encryption, and chaotic maps, specifically the sine and logistic map, alongside the self-similar properties of the fractal Sierpinski triangle. The proposed fractal-based hybrid cryptosystem leverages Paillier encryption for maintaining security and privacy, while the chaotic maps introduce randomness, periodicity, and robustness. Simultaneously, the fractal Sierpinski triangle generates intricate shapes at different scales, resulting in a substantially expanded key space and heightened sensitivity through randomly selected initial points. The secret keys derived from the chaotic maps and Sierpinski triangle are employed for image encryption. The proposed scheme offers simplicity, efficiency, and robust security, effectively safeguarding against statistical, differential, and brute-force attacks. Through comprehensive experimental evaluations, we demonstrate the superior performance of the proposed scheme compared to existing methods in terms of both security and efficiency. This paper makes a significant contribution to the field of digital image encryption, paving the way for further exploration and optimization in the future.

Darwin's feathers: Eco-evolutionary biology, predictions and policy.

The scientific community is often asked to predict the future state of the environment and, to do so, the structure (biodiversity) and the functions (ecosystem functioning) of the investigated systems must be described and understood. In his "handful of feathers" metaphor, Charles Darwin explained the difference between simple and predictable systems, obeying definite laws, and complex (and unpredictable) systems, featured by innumerable components and interactions among them. In order not to waste efforts in impossible enterprises, it is crucial to ascertain if accurate predictions are possible in a given domain, and to what extent they might be reliable. Since ecology and evolution (together forming "natural history") deal with complex historical systems that are extremely sensitive to initial conditions and to contingencies or 'black swans', it is inherently impossible to accurately predict their future states. Notwithstanding this impossibility, policy makers are asking the community of ecological and evolutionary biologists to predict the future. The struggle for funding induces many supposed naturalists to do so, also because other types of scientists (from engineers to modellers) are keen to sell predictions (usually in form of solutions) to policy makers that are willing to pay for them. This paper is a plea for bio-ecological realism. The "mission" of ecologists and evolutionary biologists (natural historians) is not to predict the future state of inherently unpredictable systems, but to convince policy makers that we must live with uncertainties. Natural history, however, can provide knowledge-based wisdom to face the uncertainties about the future. Natural historians produce scenarios that are of great help in figuring out how to manage our relationship with the rest of nature.

Applying dynamic systems theory and complexity theory methods in psychotherapy research: A systematic literature review.

OBJECTIVE: 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. METHODS: 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). RESULTS: 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. CONCLUSION: This review provides a descriptive synthesis of the current state of the proliferation of DST/CT methods in the psychotherapy research field.

Eight Weeks of Vitamin C Supplementation Restores the Lost Correlation between Serum Leptin and C-reactive Protein (CRP) in Patients with Type 2 Diabetes; A Randomized, Double-blind, Parallel-group, Placebo-controlled Clinical Trial.

OBJECTIVE: Inflammation is a well-described factor in the pathophysiology of type 2 diabetes mellitus (DM), which has been a suspect in the alteration of correlations between CRP and leptin in patients with type 2 DM. AIM: This study aimed to show the effect of vitamin C as an antioxidant on the correlation of the serum levels of C-reactive protein (CRP) and leptin in patients with type 2 DM. METHODS: We recruited 70 patients with longstanding T2DM and randomly assigned them into two groups; one received 500 mg/day of vitamin C, and the other received a placebo for eight weeks. Both groups were matched regarding baseline characteristics such as age, gender, weight, and diabetic medications. RESULTS: Out of 70 individuals, 57 participants were left in the study. After eight weeks of follow-up, leptin level was significantly increased in the Vitamin C group (MD = 3.48 change = 24%, p-value = 0.001) but did not change in the placebo group. Other markers such as Fasting plasma glucose, HbA1c, Creatinine, uric acid, Urea, cholesterol, HDL, LDL, TG, AST, ALT, insulin, and CRP did not significantly change in both groups (p value > 0.05). The significant changes in the leptin level among the vitamin C group also remained after controlling for age, BMI, Blood pressure (BP), Triglyceride (TG), and cholesterol. Also, the correlation between serum CRP and leptin became significant in the vitamin C group after eight weeks of follow-up but not in the placebo group. (rs = 0.730, p < 0.001 vs. rs = 0.286, p-value = 0.266 in placebo group). CONCLUSION: This study shows vitamin C can restore CRP-leptin correlation in patients with type 2 diabetes and increase serum leptin levels. More studies are needed to clarify the mechanism of this restoration. CLINICAL TRIAL REGISTRATION NUMBER: IRCT20160811029306N1.

Predicting chromosomal compartments directly from the nucleotide sequence with DNA-DDA.

Three-dimensional (3D) genome architecture is characterized by multi-scale patterns and plays an essential role in gene regulation. Chromatin conformation capturing experiments have revealed many properties underlying 3D genome architecture, such as the compartmentalization of chromatin based on transcriptional states. However, they are complex, costly and time consuming, and therefore only a limited number of cell types have been examined using these techniques. Increasing effort is being directed towards deriving computational methods that can predict chromatin conformation and associated structures. Here we present DNA-delay differential analysis (DDA), a purely sequence-based method based on chaos theory to predict genome-wide A and B compartments. We show that DNA-DDA models derived from a 20 Mb sequence are sufficient to predict genome wide compartmentalization at the scale of 100 kb in four different cell types. Although this is a proof-of-concept study, our method shows promise in elucidating the mechanisms responsible for genome folding as well as modeling the impact of genetic variation on 3D genome architecture and the processes regulated thereby.

Evidence of Chaos in Electroencephalogram Signatures of Human Performance: A Systematic Review.

(1) Background: Chaos, a feature of nonlinear dynamical systems, is well suited for exploring biological time series, such as heart rates, respiratory records, and particularly electroencephalograms. The primary purpose of this article is to review recent studies using chaos theory and nonlinear dynamical methods to analyze human performance in different brain processes. (2) Methods: Several studies have examined chaos theory and related analytical tools for describing brain dynamics. The present study provides an in-depth analysis of the computational methods that have been proposed to uncover brain dynamics. (3) Results: The evidence from 55 articles suggests that cognitive function is more frequently assessed than other brain functions in studies using chaos theory. The most frequently used techniques for analyzing chaos include the correlation dimension and fractal analysis. Approximate, Kolmogorov and sample entropy account for the largest proportion of entropy algorithms in the reviewed studies. (4) Conclusions: This review provides insights into the notion of the brain as a chaotic system and the successful use of nonlinear methods in neuroscience studies. Additional studies of brain dynamics would aid in improving our understanding of human cognitive performance.

How complex is COVID-19: Spatiotemporal variation of chaotic nature of the virus in Indian states.

Like other pandemics, COVID-19 also created a huge socioeconomic imbalance and distress in people. Often, every pandemic is characterized as chaotic and complex. Hence, the nature of the virus spread and deaths should be analyzed to prepare for the next similar pandemic. In this analysis, the popular and well-known time series in chaos theory is implemented, and the results are deduced for the states of India. The phase space reconstruction algorithm is implemented, and false nearest neighbor (FNN) method is applied to determine the dimensionality, and also Lyapunov exponent of the time series is estimated. The chaotic nature of COVID-19 cases showed a less severe and low complexity, with the FNN dimension range of 3-5, whereas the COVID-19 deaths showed moderate complexity with FNN dimensions 2-7. Policymakers should take action on medical availability in rural states and control people's movement in highly populated areas.

Agritourism resilience during the COVID-19 crisis.

Resilience is critical to the sustainability of the tourism industry, which was made particularly evident during the COVID-19 crisis. COVID-19 impacted all sectors of the tourism industry revealing previously unknown strengths and weaknesses. Through a longitudinal qualitative approach, we identified the evolving challenges and coping strategies of agritourism operations under the COVID-19 crisis in North Carolina, USA. The results indicate that agritourism operations not only withstood the health crisis but also advanced the management of their operation and customer satisfaction through diversification and reorganization strategies. We use chaos theory to show how agritourism operations took advantage of the context of uncertainty to employ practices that ultimately showcased their resilience.

Extracting Nonlinear Dynamics from Psychological and Behavioral Time Series Through HAVOK Analysis.

Analytical methods derived from nonlinear dynamical systems, complexity, and chaos theories offer researchers a framework for in-depth analysis of time series data. However, relatively few studies involving time series data obtained from psychological and behavioral research employ such methods. This paucity of application is due to a lack of general analysis frameworks for modeling time series data with strong nonlinear components. In this article, we describe the potential of Hankel alternative view of Koopman (HAVOK) analysis for solving this issue. HAVOK analysis is a unified framework for nonlinear dynamical systems analysis of time series data. By utilizing HAVOK analysis, researchers may model nonlinear time series data in a linear framework while simultaneously reconstructing attractor manifolds and obtaining a secondary time series representing the amount of nonlinear forcing occurring in a system at any given time. We begin by showing the mathematical underpinnings of HAVOK analysis and then show example applications of HAVOK analysis for modeling time series data derived from real psychological and behavioral studies.