Systems Theory Research Articles

Near miss prediction in commercial aviation through a combined model of grey neural network

Owing to unprecedented level of safety management, there have been few commercial aviation accidents recently, obstructing accurate prediction of safety trends. The innovative approach of proactive safety management was employed to replace reactive safety management for predicting commercial aviation near misses. In light of positive correlation between flight time and commercial aviation near-miss with different severity levels, this study aims to predict total / serious / non-serious near-miss per million flight hours. Considering grey system theory is good at prediction without adequate data, and artificial neural networks can handle nonlinear data well, the combined models of grey neural networks were developed for forecasting three data sequences over time respectively. Based on the empirical results of commercial aviation near-miss forecasting, BP neural network had the potential to enhance accuracy of grey prediction model. The three statistical measures of MAE/MSE/MAPE denoted the combined model of grey neural network outperformed the single model of GM (1, 1) or BP neural network. Predictions with a high degree of accuracy is beneficial to determining actual trends of commercial aviation near-misses at present or in future. Quantitative data will be offered for making specific decisions on maintaining preventive measures or promoting commercial aviation safety performance by introducing additional management actions

Human Versus Machine Intelligence: Assessing Natural Language Generation Models Through Complex Systems Theory.

The introduction of Transformer architectures - with the self-attention mechanism - in automatic Natural Language Generation (NLG) is a breakthrough in solving general task-oriented problems, such as the simple production of long text excerpts that resemble ones written by humans. While the performance of GPT-X architectures is there for all to see, many efforts are underway to penetrate the secrets of these black-boxes in terms of intelligent information processing whose output statistical distributions resemble that of natural language. In this work, through the complexity science framework, a comparative study of the stochastic processes underlying the texts produced by the English version of GPT-2 with respect to texts produced by human beings, notably novels in English and programming codes, is offered. The investigation, of a methodological nature, consists first of all of an analysis phase in which the Multifractal Detrended Fluctuation Analysis and the Recurrence Quantification Analysis - together with Zipf's law and approximate entropy - are adopted to characterize long-term correlations, regularities and recurrences in human and machine-produced texts. Results show several peculiarities and trends in terms of long-range correlations and recurrences in the last case. The synthesis phase, on the other hand, uses the complexity measures to build synthetic text descriptors - hence a suitable text embedding - which serve to constitute the features for feeding a machine learning system designed to operate feature selection through an evolutionary technique. Using multivariate analysis, it is then shown the grouping tendency of the three analyzed text types, allowing to place GTP-2 texts in between natural language texts and computer codes. Similarly, the classification task demonstrates that, given the high accuracy obtained in the automatic discrimination of text classes, the proposed set of complexity measures is highly informative. These interesting results allow us to add another piece to the theoretical understanding of the surprising results obtained by NLG systems based on deep learning and let us to improve the design of new informetrics or text mining systems for text classification, fake news detection, or even plagiarism detection.

Energy-informed pathways: A fundamental approach to designing ballistic lunar transfers

Ballistic Lunar Transfers (BLTs) offer propellant-efficient paths to the Moon. The nature of such transfers relies on the combined gravitational influence of the Earth, Moon, and Sun to reach the lunar vicinity. These transfers are often constructed as point designs to deliver results for a specific application. This investigation focuses on a broader, more foundational approach to BLTs seeking general design characteristics. This investigation introduces dynamics-based approaches for characterizing low-energy cislunar transfers. Principles from dynamical systems theory are applied in the Earth-Moon-Sun Bicircular Restricted Four-body Problem to enable the construction of such paths. Structures such as the instantaneous equilibrium solutions and the evolution of the Hamiltonian offer a dynamical foundation for the mechanisms that govern low-energy transfers. A targeting framework enables the methodical construction of BLT families by applying dynamically informed initial conditions from perigee maps and equilibrium solutions. With solutions generated in the four-body model, several strategies are incorporated to transition the solutions into a higher-fidelity force model governed by the ephemerides of the celestial bodies. Incorporating transfer geometries from the Earth-Moon and Sun-B1 rotating frame offers an effective transition strategy for ballistic lunar transfers to such a higher-fidelity force model. Connections between the underlying dynamics that govern ballistic lunar transfers and the practical results in the mission design process are highlighted.

On the higher-order smallest ring-star network of Chialvo neurons under diffusive couplings.

Network dynamical systems with higher-order interactions are a current trending topic, pervasive in many applied fields. However, our focus in this work is neurodynamics. We numerically study the dynamics of the smallest higher-order network of neurons arranged in a ring-star topology. The dynamics of each node in this network is governed by the Chialvo neuron map, and they interact via linear diffusive couplings. This model is perceived to imitate the nonlinear dynamical properties exhibited by a realistic nervous system where the neurons transfer information through multi-body interactions. We deploy the higher-order coupling strength as the primary bifurcation parameter. We start by analyzing our model using standard tools from dynamical systems theory: fixed point analysis, Jacobian matrix, and bifurcation patterns. We observe the coexistence of disparate chaotic attractors. We also observe an interesting route to chaos from a fixed point via period-doubling and the appearance of cyclic quasiperiodic closed invariant curves. Furthermore, we numerically observe the existence of codimension-1 bifurcation points: saddle-node, period-doubling, and Neimark-Sacker. We also qualitatively study the typical phase portraits of the system, and numerically quantify chaos and complexity using the 0-1 test and sample entropy measure, respectively. Finally, we study the synchronization behavior among the neurons using the cross correlation coefficient and the Kuramoto order parameter. We conjecture that unfolding these patterns and behaviors of the network model will help us identify different states of the nervous system, further aiding us in dealing with various neural diseases and nervous disorders.

Model adaptive phase space reconstruction.

Phase space reconstruction (PSR) methods allow for the analysis of low-dimensional data with methods from dynamical systems theory, but their application to prediction models, such as those from machine learning (ML), is limited. Therefore, we here present a model adaptive phase space reconstruction (MAPSR) method that unifies the process of PSR with the modeling of the dynamical system. MAPSR is a differentiable PSR based on time-delay embedding and enables ML methods for modeling. The quality of the reconstruction is evaluated by the prediction loss. The discrete-time signal is converted into a continuous-time signal to achieve a loss function, which is differentiable with respect to the embedding delays. The delay vector, which stores all potential embedding delays, is updated along with the trainable parameters of the model to minimize prediction loss. Thus, MAPSR does not rely on any threshold or statistical criterion for determining the dimension and the set of delay values for the embedding process. We apply the MAPSR method to uni- and multivariate time series stemming from chaotic dynamical systems and a turbulent combustor. We find that for the Lorenz system, the model trained with the MAPSR method is able to predict chaotic time series for nearly seven to eight Lyapunov time scales, which is found to be much better compared to other PSR methods [AMI-FNN (average mutual information-false nearest neighbor) and PECUZAL (Pecora-Uzal) methods]. For the univariate time series from the turbulent combustor, the long-term cumulative prediction error of the MAPSR method for the regime of chaos stays between other methods, and for the regime of intermittency, MAPSR outperforms other PSR methods.

Near miss prediction in commercial aviation through a combined model of grey neural network

Owing to unprecedented level of safety management, there have been few commercial aviation accidents recently, obstructing accurate prediction of safety trends. The innovative approach of proactive safety management was employed to replace reactive safety management for predicting commercial aviation near misses. In light of positive correlation between flight time and commercial aviation near-miss with different severity levels, this study aims to predict total / serious / non-serious near-miss per million flight hours. Considering grey system theory is good at prediction without adequate data, and artificial neural networks can handle nonlinear data well, the combined models of grey neural networks were developed for forecasting three data sequences over time respectively. Based on the empirical results of commercial aviation near-miss forecasting, BP neural network had the potential to enhance accuracy of grey prediction model. The three statistical measures of MAE/MSE/MAPE denoted the combined model of grey neural network outperformed the single model of GM (1, 1) or BP neural network. Predictions with a high degree of accuracy is beneficial to determining actual trends of commercial aviation near-misses at present or in future. Quantitative data will be offered for making specific decisions on maintaining preventive measures or promoting commercial aviation safety performance by introducing additional management actions

The Effectiveness of Implementing Local Taxpayer Status Confirmation for Investors in the City of Blitar

This article discusses the effectiveness of confirming the status of regional taxpayers in Blitar City based on the Regulation of the Minister of Home Affairs Number 112 of 2016. This research uses Lawrence Meir Friedman's theory of the legal system and Soerjono Soekanto's theory of Law Enforcement. The results of the legal structure analysis show the success of the Regional Finance and Asset Management Agency (BPKAD) and the One-Stop Investment and Integrated Services Office (DPMPTSP) in coordinating and accessing tax applications, which significantly improves service efficiency. The legal substance of Regulation Number 112 of 2016 has proven effective as the basis for the Regulation of the Mayor of Blitar Number 73 of 2019. Legal culture analysis reveals effective public compliance with the taxpayer's status confirmation procedure before receiving services, without any protests. Thus, implementing the regulation in Blitar City can be considered successful, creating effective cooperation between related agencies, ensuring taxpayer understanding and compliance, and improving the quality of public services.

The plurality of norms that factor into Canadians’ decisions to engage in transnational egg transactions

Egg donation is regulated differently in countries around the world. In Canada, federal law – the Assisted Human Reproduction Act – prohibits paying egg providers. As a result, many Canadians are engaging in a grey market for eggs or are pursuing transnational egg transactions by traveling to countries with more permissive laws, or having eggs shipped to Canada. In this paper I rely on interview data with 18 Canadian intended parents and 16 Canadian egg providers – many of whom had traveled abroad for egg transactions – to understand how intended parents and egg providers decide how they will pursue egg transactions; specifically, why so many Canadians choose to engage in transnational egg transactions. I use Brian Z. Tamanaha’s theory of systems of normative ordering, combined with Paul Schiff Berman’s cosmopolitan pluralism, as a framework to reveal the plurality of norms and other factors that weigh into this decision-making and that help reveal the answer to the “why.” Ultimately, I illustrate how intended parents and egg providers are impacted by sometimes clashing norms from official legal systems, economic/capitalist systems, and customary/cultural systems. These norms, along with moral and practical concerns, shape the decisions of intended parents and egg providers.

Економічна динаміка: сучасний зміст, інноваційний базис та настанови для політики розвитку

The article substantiates the relevance of updating the categorical thesaurus of the theory of economic dynamics on the basis of modern methodology, which combines the cognitive potentials and tools of global evolutionism, the general theory of systems and synergy. In the context of the evolutionary approach, the authors provide the content and characteristics of the complex forms of economic system’ movement (self-organization, development / degradation, evolution / involution) and the accompanying qualitative changes (modifications, transformations, metamorphoses, architectural shifts) of economic reality. There are revealed the hierarchy of dynamic processes according to the degree of complexity and their interrelationships and principles of mutual transitions. Attention is focused on innovations and innovative changes, which, depending on the level (depth) of the implemented novelty, and the speed and scale of its diffusion, determine the trajectory of the economic system’ movement over time. Taking into account the time criterion, the authors systematize the signs of progressive and regressive economic dynamics. Noted the significant role of organizational and regulatory influences, primarily that of the innovation policy, for designing and constructing the expected future. A view is proposed to consider innovation policy as a system of measures aimed at the strategic management of economic genetics. The content of these measures consists in selective changes in inherited production and technological structures and reproductive cycles, as well as inherited models of the behavior of economic actors. The direction of selection, which represents a choice of priorities for innovative development is determined by the imperatives of self-preservation, effective self-realization and self-development of the economic system in a highly competitive environment.

THE APPRAISAL SYSTEM OF TOURISM EDITORIAL TEXTS ON SINDONEWS.COM

This research aimed to identify appraisal system in tourism editorial texts in SINDOnews.com news portal, particularly to identify attitude, graduation, and engagement as well as to know how the writer positions himself in his writing and public response on the issue. This research applied Systemic Functional Linguistics (SFL) theories and is designed with an embedded case study. The data were taken from a tourism editorial text in SINDOnews.com with the total number of 70 out of 30 sentences. The data were then analyzed with appraisal system theory proposed by Martin & White (2005). The result shows the existence of attitude (affect positive/negative), judgement (social esteem positive/negative: normality, capacity, and tenacity; social sanctions positive/negative: veracity and propriety), appreciation (reaction, composition, and valuation positive/negative), graduation (force: Intensifier, attitudinal lexis, metaphor and focus: sharpen and soften) and engagement (monogloss and heterogloss). The data mostly found are graduation which consists of force (8 intensifiers, 6 metaphor, and 2 qttitudinal lexes) and focus (6 sharpen and 8 soften). The texts themselves emphasize values, public opinions, author’s attitude, and show the beautiful and the selling point of tourist destinations in Bali. It means that the writer shows the positive perception subjectively whereas the society shows the positive and negative perception objectively in the texts.

Impact of Research Methodology on Data Quality and Research Findings

The study examined the intrigueing impact of research methodology as the foundation of any research, impacting the accuracy of data collected, the scope of data collected, and the findings. It focused on the interface between research methodology, data quality, and research findings and elucidated on the relationship between research methodology and the reliability, validity, and generalizability of research findings. Focus was also on the identifying the challenges and limitations associated with different research methodologies in ensuring data quality and reliability. Multidisciplinary discipline of systems theory helped to rationalized on how crucial it is to comprehend the dynamic interconnections, feedback loops, and process structure of research. Finding from the study shows that indeed research methodology determines the quality, reliability, validity, and generalizability of research findings while indicating as well that significant differences exist regarding challenges limiting the applicability of findings in different research methods. As a recommendation, faculties and academic programs should ensure they offer numerous research methodology courses that will lay a foundation for students' research.

Family and job microsystems as mediators between social integration and depression among rural-to-urban migrant workers in China: does having sons make a difference?

Rural-to-urban migrant workers are a vulnerable group at risk of developing depression. Based on the social-ecological systems theory, this study investigates the impact of the lack of social integration on depression, considering the mediating roles of migrant workers' microsystems (family happiness and job burnout). Additionally, the study explores whether having sons influences these associations. The sample of 4,618 rural-to-urban migrant workers was obtained from the 2018 wave of the China Labor Force Dynamics Survey (CLDS). All the measures in the survey exhibited good reliability, including the Center for Epidemiological Research Depression Scale (CES-D), family happiness, job burnout, and social integration. The data were primarily analyzed using a structural equation model. Social integration had a direct impact on depression among migrant workers. Additionally, it indirectly affected depression through the mediating roles of family happiness not job burnout. The moderating effect of having sons mainly occurred on the path from social integration to family happiness. The cross-sectional design impeded the ability to draw causal inferences. This finding highlights the potential benefits of social integration and family happiness in promoting early prevention of depression among migrant workers. It indicates that the inclination toward having sons among migrant workers continues to impact their mental health.

Chaotic Phenomena, Sensitivity Analysis, Bifurcation Analysis, and New Abundant Solitary Wave Structures of The Two Nonlinear Dynamical Models in Industrial Optimization

In this research, we discussed the different chaotic phenomena, sensitivity analysis, and bifurcation analysis of the planer dynamical system by considering the Galilean transformation to the Lonngren wave equation (LWE) and the (2 + 1)-dimensional stochastic Nizhnik–Novikov–Veselov System (SNNVS). These two important equations have huge applications in the fields of modern physics, especially in the electric signal in data communication for LWE and the mechanical signal in a tunnel diode for SNNVS. A different chaotic nature with an additional perturbed term was also highlighted. Concerning the theory of the planer dynamical system, the bifurcation analysis incorporating phase portraits of the dynamical systems of the declared equations was performed. Additionally, a sensitivity analysis was used to monitor the sensitivity of the mentioned equations. Also, we extracted new, abundant solitary wave structures with the graphical phenomena of the mentioned nonlinear mathematical models. By conducting an expansion method on the abovementioned equations, we generated three types of soliton structures, which are rational function, trigonometric function, and hyperbolic function. By simulating the 3D, contour, and 2D graphs of these obtained solitons, we scrutinized the behavior of the waves affecting the nonlinear terms. The figures show that the solitary waves obtained from LWE are efficient in analyzing electromagnetic wave signals in the cable lines, and the solitary waves from SNNVS are essential in any stochastic system like a sound wave. Moreover, by taking some values of the parameters, we found some interesting soliton shapes, such as compaction soliton, singular periodic solution, bell-shaped soliton, anti-kink-shaped soliton, one-sided kink-shaped soliton, and some flat kink-shaped solitons, etc. This article will have a great impact on nonlinear science due to the new solitary wave structures with different complex phenomena, sensitivity analysis, and bifurcation analysis.

Measurement insights and error analysis of electronic parameters for ultrasonic transducers

Abstract Piezoelectric ultrasonic transducers with the function of energy conversation, as well as their numerous advantages in high-power density, quick response, flexible design, and service reliability, are involved in wide applications of industrial processing, precision driving, smart sensing, and medical services. The electromechanical equivalent circuit and Kirchhoff’s law indicate that mechatronics parameters are essential for performance evaluation, reliability analysis, and fault diagnosis of ultrasonic transducers. Importantly, the ultrasonic transducer is a time-variant system, data of one single parameter collected from a certain test cannot match with the data of another single parameter acquired from a different test. So, a synchronous and precise online measurement of electronic parameters is encouraged for performance evaluation and optimization design of ultrasonic transducers. With the combination of virtual instrument technology, an asynchronous measurement system of electrical excitation parameters for the ultrasonic transducers of linear driving motors was established in this study. Furthermore, the systematic measurement methods and error theory were illustrated, including the calculation methods and measuring circuits of electric signals. Experimental results proved that the proposed system and methods for measuring the input electronic power of ultrasonic transducer (e.g. effective value method for voltage and current, energy moment method for frequency, and spectrum analysis method for phase difference) are highly precise, quickly responsive, robust, and reliable for ultrasonic transducers. The findings of this study provide valuable references and suggestions for efficient, accurate, and online performance evaluation of ultrasonic transducers, particularly for piezoelectric transducers utilizing ultrasonic high-voltage exciting signals.

Active Engagement in Retirement: Factors That Influence the Participation of Older People in Community Volunteering

Although older adults participating in volunteering promotes “active ageing” and reduces social service burden, not many retied people in China participate in volunteering. Utilizing data from semi-structured interviews with 15 retired individuals in Beijing, this research explores the factors influencing their involvement in community volunteer services. Informed by resource theory and social ecological system theory, a hybrid three-level social ecological systems model is developed following thematic analysis. At the micro-level, foundational factors for volunteering are their human capital, including good health status, ample free time, and satisfaction with pensions. At the meso-level, social capital from family and peers provides essential information and support, while a cohesive community environment fostered through community and volunteer management enhances the willingness to volunteer. At the macro-level, CPC ideologies and governmental policies nurture a culture of volunteerism that facilitates community involvement. The study also investigates interactions across these levels: the microsystem and mesosystem mutually enhance human and social capital, whereas the macrosystem and mesosystem reflect the reciprocal influence of cultural on social capital. Recommendations are offered to increase retired people’s participation in community volunteering, emphasizing community management and governmental policy adjustments.

Impact of horticulture on the sustainable livelihood development of smallholders: a SPAR-4-SLR & future research agenda

Purpose. The perishable nature of the horticulture (fruits, vegetables, and flowers) industry makes it more complex to study. The higher return and nutritional benefits compared to traditional agricultural products expand the research scope for the stakeholders’ sustainable benefit. Nevertheless, despite limited research, this study explicitly examines additional facets of smallholder development beyond the traditional economic, social, and environmental dimensions. Methodology / approach. To fill this knowledge gap, we comprehensively reviewed 73 articles published between 2013 and 2022 in international scientific journals with an SJR (Scimago Journal & Country Rank) Q1 ranking. We followed the “Scientific Procedures and Rationales for Systematic Literature Reviews” (SPAR-4-SLR) protocol to identify different aspects of smallholders’ development. Literature from Web of Science and Scopus databases was analysed and organised using the TCCM (Theory, Context, Characteristics, and Methodology) framework. Results. The study identified important factors like infrastructure, commercialisation, market access, marketing, credit, policies, information, intervention, training, technology, and collaboration that could uncover crucial economic, political, social, psychological, ecological, cultural, physical and nutritional aspects of development in general. The study also suggests that implementation Agriculture 4.0 through advanced technologies like IoT, AI, and vertical and bio-fortification practices can help in sustainable livelihood development in horticulture. Originality / scientific novelty. The limited or lack of comprehensive studies on smallholders’ sustainable livelihood development with different dimensions makes this research a bridge to identify other dimensions instead of only focusing on the horticultural sector’s economic, social, and ecological aspects. It uses the Agricultural Innovation System and Farmer First theories, stating that the farmer is the first priority and must collaborate and interact to implement technology and innovations for smallholder livelihood development. Practical value / implications. This research will help in decision-making regarding smallholders’ livelihoods in all eight dimensions at all levels of government, private and other stakeholders, suggesting a triple-helix model.

Renewable Energy Adoption and Its Effect on Rural Development in United States

Purpose: The aim of the study was to analyze the renewable energy adoption and its effect on rural development in United States. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Renewable energy adoption in rural areas of the United States has shown promising effects on rural development. Studies indicate that the deployment of renewable energy projects, such as wind and solar farms, has led to job creation, increased local tax revenues, and economic diversification in rural communities. Additionally, renewable energy projects have provided opportunities for landowners to earn additional income through leasing their land for energy production Unique Contribution to Theory, Practice and Policy: Diffusion of innovations theory, social-ecological systems theory & technology acceptance model (TAM) may be used to anchor future studies on renewable energy adoption and its effect on rural development. Strengthening practical interventions to facilitate the adoption of renewable energy technologies in rural communities is essential. Developing supportive policy frameworks is critical to unlocking the full potential of renewable energy for rural development.

Co to jest chaosmotechnika?

This article considers the problem of the scale of locality as a matter of placing technology in an (alter)cosmological perspective. It engages in a dialogue with two Simondon-inspired projects: on the one hand, Yuk Hui’s concept of cosmotechnics, and on the other, the transductive theory of systems outlined in the Deleuzoguattarian Thousand Plateaus. A new reading of a marginal thread of the latter, i.e., the universe of technical objects associated with the nomadism of the Great Steppe of the Bronze and Iron Ages, leads to the crystallization of the concept of chaosmotechnics, according to which the substrate of technical activities is the contingency of the cosmos. Answering the question of locality, the chaosmotechnic perspective allows for the untimely emergence of an alternative cosmology of the contemporary digital image from nomadic metal technical objects. Key-words: cosmos, technology, art, digital image, Witold Gombrowicz, Yuk Hui, Gilbert Simondon, Gilles Deleuze, Félix Guattari, Laura U. Marks

Microscopic Derivation of Transition-state Theory for Complex Quantum Systems

Microscopic Derivation of Transition-state Theory for Complex Quantum Systems

Darboux transformation-based LPNN generating novel localized wave solutions

Darboux transformation method is one of the most essential and important methods for solving localized wave solutions of integrable systems. In this work, we introduce the core idea of Darboux transformation of integrable systems into the Lax pairs informed neural networks (LPNNs), which we proposed earlier. By fully utilizing the data-driven solutions, spectral parameter and spectral function obtained from LPNNs, we present the novel Darboux transformation-based LPNN (DT-LPNN). The notable feature of DT-LPNN lies in its ability to solve data-driven localized wave solutions and spectral problems with high precision, and it also can employ Darboux transformation formulas of integrable systems and non-trivial seed solutions to discover novel localized wave solutions that were previously unobserved and unreported. The numerical results indicate that, by utilizing the single-soliton solutions as the non-trivial seed solutions, we obtain novel localized wave solutions for the Kraenkel–Manna–Merle (KMM) system by employing DT-LPNN, in which solution u changes from original bright single-soliton on zero background wave to new dark single-soliton dynamic behavior on a variable non-zero background wave. Moreover, by treating the two-soliton solutions as the non-trivial seed solutions, DT-LPNN generates novel localized wave solutions for the KMM system that exhibit completely different dynamic behaviors from prior two-soliton solutions. DT-LPNN combines the Darboux transformation theory of integrable systems with deep neural networks, offering a new approach for generating novel localized wave solutions using non-trivial seed solutions.