Components Of Complex System Research Articles

P-978. Internal Medicine Resident Perceptions of the Barriers to and Facilitators of Optimal Inpatient Care of Persons who Inject Drugs: A Mixed Methods Study

Abstract Background Harm reduction counseling and HIV pre- and post-exposure prophylaxis (PrEP and PEP) can prevent infectious complications among persons who inject drugs (PWID). Hospitalizations are opportunities to provide these services, though these interventions occur infrequently for PWID admitted to our hospital. Little is known about internal medicine resident (IMR) perceptions of these interventions in the inpatient setting despite their frontline role in patient care in our institution. Methods In our mixed methods study we describe IMR perceptions of HIV prevention interventions and harm reduction counseling among PWID admitted to the University of Wisconsin Hospital. We conducted a survey examining current practices and perceptions. We explored the barriers and facilitators to optimizing these practices through semi-structured interviews. Transcripts were analyzed via consensus coding using the Systems Engineering Initiative for Patient Safety (SEIPS) model as a guiding conceptual framework. This framework describes the interactions of five components of complex work systems and their individual and overlapping influence on patient outcomes: 1) organization, 2) person, 3) physical environment, 4) task, and 5) technology and tools. Results 59 IMRs responded to the survey (58% response rate). Residents report offering HIV-PrEP or HIV-PEP to PWID at low frequency; 56% never offer PrEP and 64% never offer PEP. 20% or fewer IMRs report offering PrEP or PEP at least some of the time. IMRs report a high degree of importance and appropriateness of HIV prevention interventions; however, the majority lack comfort, familiarity, or competence in how to counsel patients or prescribe PrEP or PEP (Table 1). 15 residents participated in interviews, all of whom identified barriers and facilitators to these interventions relevant to each component of the work system (Figure 1). Conclusion Despite recognizing the importance of HIV prevention services, including PrEP, PEP and harm reduction counseling, IMRs lack knowledge and comfort to do so. Barriers to optimal care span all components of the work system. Facilitators to optimal care and next steps include educational initiatives and EMR-based interventions with assessment of impact. Disclosures Jessica S. Tischendorf, MD, MS, Merck: Grant/Research Support

A review of the evolution and concepts of deep learning and AI in the textile industry

Machine learning (ML) and deep learning (DL) are transforming the textile industry by integrating advanced technologies into various processes. Textiles, once seen as passive materials, are now essential components of complex systems due to automation and innovative materials. This review focuses on articles that utilized AI, ML, or DL in textile research and industry. The review presents bibliometric analysis of AI methods in textiles. Later, the review is structured into sections that examine the effect of ML and DL across the textile sector. We outline key ML and DL methods applied in textiles, discussing their main uses and potential applications. This overview aims to clarify the working principles behind these methods, which are explored in greater detail. The methods analyzed range from basic linear regression to ensemble techniques such as XGBoost. DL techniques include convolutional neural networks for image analysis and long short-term memory networks for time-series analysis. In addition, a bibliometric review identifies trends and gaps in the literature, highlighting areas for future research. We also provide a detailed examination of how these methods are implemented in textiles.

Resilience evaluation of train control on-board system considering component failure correlations: Based on Apriori-Multi Layer-Copula Bayesian Network model

The failure of complex system components is an important factor affecting system resilience, and it is not only affected by their own basic life parameters, but may also be affected by the failure of other components. In order to investigate the impact of component failure correlations on the resilience evaluation of Train Control on Board System (TCOBS), we propose the Apriori-Multi Layer-Copula Bayesian Network (AMLCBN) model. Firstly, the definition and evaluation function of TCOBS component resilience are provided. Then, build a TCOBS Bayesian Network and perform hierarchical processing on the network to clarify the position of Copula functions in the Bayesian Network. The Copula function is used to evaluate the correlations among component failures, and the Copula Bayesian Network is used to infer TCOBS resilience. We use Apriori to calculate the correlation coefficient matrix in the Copula function. Finally, a case study is conducted by taking CTCS-3OBS as an example, the results show that among the components of TCOBS, BTM Ant has low resilience and high importance. Considering the correlation among component failures, the TCOBS resilience evaluation results will increase, and those components with higher importance will become more important, while those with lower importance will become less important.

Analysis of Avoided Level Crossing Muon Spin Resonance Spectra of Muoniated Radicals in Anisotropic Environments: Estimation of Muon Dipolar Hyperfine Parameters for Lorentzian-like Δ1 Resonances

Avoided level crossing muon spin resonance (ALC-μSR) is used to characterize muoniated free radicals. These radicals are used as probes of the local environment and reorientational motion of specific components in complex systems. The parameter that provides information about the anisotropic motion is the motionally-averaged muon dipolar-hyperfine coupling constant (Dμ‖). The ALC-μSR spectra of muoniated radicals in anisotropic environments frequently have Lorentzian-like Δ1 resonances, which makes it challenging to extract Dμ‖. In this paper, we derive a means to estimate|Dμ‖| from ALC-μSR spectra with Lorentzian-like resonances by measuring the amplitude, width, and position of the Δ1 resonance and the amplitude, width, and position of a Δ0 resonance. Numerical simulations were used to test this relationship for radicals with a wide range of muon and proton hyperfine parameters. We use this methodology to determine |Dμ‖| for the Mu adducts of the cosurfactant 2-phenylethanol in C12E4 bilayers. From this we determined the amplitude of the anisotropic reorientational motion of the cosurfactant.

Health Assessment for Multi-component Systems Based on Proportional Hazards Model and Dynamic Bayesian Network

As a key task of prognostics and health management, the health assessment of systems depends on both age and covariate processes. Cox’s proportional hazards model is an effective tool for system health assessment, capable of capturing both the failure rate and the effect of the covariate process. However, most existing literature assumes the system failure as a whole, which exhibits certain limitations when dealing with the failure interactions among components in complex systems. This paper develops a method that combines a dynamic Bayesian network with a proportional hazards model, where a dynamic Bayesian network is utilized to characterize the failure dependency among components failure, and the joint effect of age and covariate processes on component failure is quantified by the proportional hazards model. The integration of two models facilitates the health assessment of complex multi-component systems. Finally, the effectiveness of the proposed model is showcased through a numerical case study.

Predicting multiple observations in complex systems through low-dimensional embeddings

Forecasting all components in complex systems is an open and challenging task, possibly due to high dimensionality and undesirable predictors. We bridge this gap by proposing a data-driven and model-free framework, namely, feature-and-reconstructed manifold mapping (FRMM), which is a combination of feature embedding and delay embedding. For a high-dimensional dynamical system, FRMM finds its topologically equivalent manifolds with low dimensions from feature embedding and delay embedding and then sets the low-dimensional feature manifold as a generalized predictor to achieve predictions of all components. The substantial potential of FRMM is shown for both representative models and real-world data involving Indian monsoon, electroencephalogram (EEG) signals, foreign exchange market, and traffic speed in Los Angeles Country. FRMM overcomes the curse of dimensionality and finds a generalized predictor, and thus has potential for applications in many other real-world systems.

Equipment Fault Propagation Path Identification Based on Unstable Points Detection

To prevent industrial fault propagation, it is important to clarify the relationship between industrial system components and identify the fault propagation path efficiently and timely, aiming at the problems in fault propagation path identification, this paper presents an equipment fault propagation analysis approach based on unstable points identification to solve such issues. First, the fault propagation diagram is created by analyzing the industrial complex system components. Second, to address the issues of traditional Interpretative Structural Modeling (ISM), the Bilateral Rotation Interpretative Structural Modeling (BRISM) method is proposed to stratify the components and detect the unstable points. Finally, the fault propagation graph is analyzed using the PageRank algorithm to update the unstable points and edge weights between different nodes to identify the fault propagation paths. The results indicate that the proposed method can effectively identify fault propagation paths between components and can be applied to various industrial systems.

Intelligent decision support system for assessing the risk of failures of complex technical systems

A decision support system has been developed that allows assessing the risk of failure of elements and components of complex technical systems using artificial intelligence. The proposed algorithm for the functioning of a decision support system implements the task of automating the process of assessing the technical condition of complex systems. The use of the proposed decision support system for assessing the technical condition of complex systems will improve the reliability of operating systems with insufficient information about their technical condition.

Insight into the interaction mechanism between mulberry polyphenols and β-lactoglobulin

The interaction mechanism of components in complex food systems is one of the hot topics in food science. Both protein and polyphenols co-exists in many foods, but their interaction mechanism is very difficult to elucidate. In this paper, the interaction between mulberry polyphenols (MP) and β-lactoglobulin (β-LG) was investigated based on individual phenolic ratios in MP to explore impact of MP on β-LG. The results showed that the effects of MP on β-LG could be reproduced by simulated MP (sMP) which was composed of the main individual phenolics in MP. MP, sMP and individual phenolics interacted with β-LG through static quenching, in which MP, sMP, cyanindin-3-rutinoside (C3R) and epicatechin (EC) bound to β-LG via hydrogen bonds and van der Waals force; cyanindin-3-glucose (C3G) and p-coumaric acid (PCA) bound to β-LG via hydrogen bonds and hydrophobic interactions; but quercetin (Q), rutin (R), protocatechuic acid (PA) and pelargonidin-3-glucosid (Pg3G) bound to β-LG mainly through hydrophobic interaction. Addition of MP, sMP or most individual phenolics of MP significantly increased the particle size of β-LG. C3G, C3R, PCA, Q and R, with varying degrees of influence, markedly changed secondary structure of β-LG by affecting the microenvironment of amino acids residues. Furthermore, R, PCA, EC and Pg3G induced the transition from β-fold to α-helix of β-LG, making the protein more stable. The contribution of each individual phenolic to the interaction between MP and β-LG was mainly dose-dependent and also influenced by their chemical structures.

Mapping a Set of Tools to Ensure Cloud and Distributed Computing, Virtualization Tools and Data Storage Systems in the Work of the Transport and Logistics Center

The existing gaps in approaches to the deployment of transport and logistics centers (TLC) within the edges of the backbone network lead to errors in the implementation of the spatial development strategy. Information support solutions for the implementation of terminal, transportation, and warehousing technologies are the least elaborated. As a result, errors have to be corrected in the process of operating the information architecture. There is a need to complement the existing TLC deployment management system with new tools that enhance the validity of TLC location assessment and eliminate the randomness factor in the choice of information architecture for TLC backbone network objects. This research aims to develop a flexible solution for network architecture design using cloud, fog, and edge layers. The main requirement for a flexible solution is that it can be rapidly deployed when the technology architecture changes. The proposed tool visualizes the structure of the network architecture and allows the analysis of information flows by capturing data on the movement of material cargo within the center and between TLC network facilities. The mapping tool considers the network computational load evaluation factor for the cloud, fog, and edge layers. The scientific novelty of the research results is achieved by the principle of system management of the components of complex systems. The practical significance of the results of the study lies in the possibility of using the mapping tool in the process of information architecture design at the stage of making decisions about the deployment of TLC network objects.

Efficiency Optimization of an Annular-Nozzle Air Ejector under the Influence of Structural and Operating Parameters

The efficiency of annular-nozzle ejectors serving as components of complex technical systems interacting with high-temperature media in engines and in the field of energy technologies is not linearly related to the gas-dynamic characteristics of the flows formed in the device. In this paper, we have analyzed the results of numerical and experimental studies of gas jets in an annular-nozzle air ejector. The regression equations built according to the circumscribed central composite design described the relationship between a pressure drop and the structural parameters of the nozzle with the speed and mass flow rates of the airflows, including error rates of no more than 15 percent. A two-factor optimization based on Harington’s generalized desirability function was performed to obtain a relatively accurate estimate of the ejector efficiency under the influence of the structural and operating parameters. An optimization method based on the combination of response surface methodology and the desirability function approach, allowing simultaneous consideration of all responses, made it possible to simultaneously optimize multiple conflicting objectives.

Study of Component Solubility Polytherm in the System Ca(ClO3)2–[21% ClCH2CH2PO(OH)2⋅NH3 + 11% ClCH2CH2PO(OH)2⋅2NH3 + 12% NH4H2PO4 + 56% H2O]–H2O

Mutual effect of components in complex aqueous system comprising calcium chlorate, ammonium dihydrogen phosphate, and ammonium chloroethylphosphonates has been studied. Polythermal diagram of system solubility has been built in the temperature range from –40.3 to 68.0°C. Phase diagram of system solubility demarcates crystallization fields for ice, calcium chlorate hexa-, tetra-, and dihydrate, and compound of composition NH4ClO3·ClCH2CH2HPO3NH4. The compound has been isolated from supposed crystallization field and has been identified by chemical and physicochemical analysis. To provide foundation for the process of preparation of efficient stimulant and mild defoliant based on calcium chlorate and Nazhot preparation, rheological properties of components in the system Ca(ClO3)2–[21% ClCH2CH2PO(OH)2⋅NH3 + 11% ClCH2CH2PO(OH)2⋅2NH3 + 12% NH4H2PO4 + 56% H2O]–H2O have been studied. Composition–property diagram has been built for the system.

A fault location method based on ensemble complex spatio-temporal attention network for complex systems under fluctuating operating conditions

Recently, traditional fault diagnosis methods achieve fault identification by establishing a sample set covering all fault degrees of different fault components in complex systems. However, the uncertainties including environmental stresses and own physical and chemical variations lead to an infinite number of fault degrees of fault components, and the fault identification of complex systems in practical engineering applications faces the challenge of fluctuating operating conditions due to variations of rotational speed and load. To address the above problem, a fault location method based on ensemble complex spatio-temporal attention network (ECSAN) is proposed in this paper, which can identify the critical fault components of complex systems by combining an ensemble learning mechanism with excellent basic estimators. A basic estimator consists of a feature extraction module, a feature enhancement module, and a classification module. In the first module, a complex spatio-temporal backbone network with strong generalization is developed to provide spatio-temporal features containing the inherent information of the sample data for complex systems. In the feature enhancement module, a lightweight complex attention layer is constructed to enhance the effective structural information of the features and reduce the interference of their redundant information. The classification module then adopts a Softmax layer to perform fault classification. Finally, an ensemble learning mechanism is designed to integrate the basic estimators. By constructing sample weights and introducing a knowledge transfer strategy, the generalization is further improved while saving training expenses. Two datasets from different experimental platforms are concerned to verify the effectiveness and superiority of this method under various operating conditions and fault degrees. The experimental results indicate that this method achieves 99.81% accuracy on a standard dataset of the mechanical system and 98.88% accuracy on a real dataset of the closed-loop control system of the water jet propulsion device, which is superior to comparison approaches.

Pandemiakriisin johtamisesta syndeemiseen kriisien hallintaan – teoreettis-käsitteellinen syndemioiden uudelleenkehystys pandemiakontekstissa

From Managing the Pandemic Crisis to Syndemic Governance: A Conceptual Reframing of Syndemics in the Context of Influenza Pandemics As a result of the COVID-19 pandemic, the research literature has highlighted the significance of syndemic thinking in managing epidemics (e.g. Horton 2020; Irons 2020; Fronteira et al. 2021). The syndemic nature of COVID-19 has posed a challenge to traditional models of management that promote path dependence, which in turn fuels more emergent effects. As a result, many causal relationships cannot be fully mapped in real time, making syndemic interventions challenging to implement. This article introduces new conceptualizations of syndemics by applying theories of emergent governance in order to develop a model of syndemic governance. In this context, emergence emphasizes the forms of interaction between components of complex systems that generate problems that cannot be traced back to these components and their functioning alone. These forms of interaction arise through self-organization...

Do You Need a Distributed Ledger Technology Interoperability Solution?

Entrepreneurs, enterprises, and governments are using distributed ledger technology (DLT) as a component of complex information systems, and therefore interoperability capabilities are required. Interoperating DLTs enable network effects and synergies, and similarly to the rise of the Internet, it unlocks the full potential of the technology. However, due to the novelty of the area, interoperability mechanisms (IMs) are still not well understood, as interoperability is studied in silos. Consequently, choosing the proper IM for a use case is challenging. Our article has three contributions: first, we systematically study the research area of DLT interoperability by dissecting and analyzing previous work. We study the logical separation of interoperability layers, how a DLT can connect to others (connection mode), the object of interoperation (interoperation mode), and propose a new categorization for IMs. Second, we propose the first interoperability assessment for DLTs that systematically evaluates the interoperability degree of an IM. This framework allows comparing the potentiality, compatibility, and performance among solutions. Finally, we propose two decision models to assist in choosing an IM, considering different requirements. The first decision model assists in choosing the infrastructure of an IM, while the second decision model assists in choosing its functionality.

Implications of Dynamic Systems for Future Methodology in Developmental and Learning Science

Children develop and learn within dynamic contexts, yet the simplifying assumptions of common statistical methods often relegate such complexity to unexplained error. This chapter discusses ideas from the dynamic systems literature, which focuses on the interplay within and between components of complex systems, such as individuals and their multitiered contexts. This chapter presents three scenarios highlighting knowledge from the dynamic systems literature. These scenarios have implications requiring reconsidering common approaches to explaining the variance associated with change, the conceptualization of effect sizes, and the use of between-person data and analyses to make within-person inferences. The final section provides resources for moving beyond dynamic metaphors and principles, so theories can be translated into testable hypotheses.

Yoga and pain: A mind-body complex system.

The human body's response to pain is indicative of a complex adaptive system. Therapeutic yoga potentially represents a similar complex adaptive system that could interact with the pain response system with unique benefits. To determine the viability of yoga as a therapy for pain and whether pain responses and/or yoga practice should be considered complex adaptive systems. Examination through 3 different approaches, including a narrative overview of the evidence on pain responses, yoga, and complex system, followed by a network analysis of associated keywords, followed by a mapping of the functional components of complex systems, pain response, and yoga. The narrative overview provided extensive evidence of the unique efficacy of yoga as a pain therapy, as well as articulating the relevance of applying complex systems perspectives to pain and yoga interventions. The network analysis demonstrated patterns connecting pain and yoga, while complex systems topics were the most extensively connected to the studies as a whole. All three approaches support considering yoga a complex adaptive system that exhibits unique benefits as a pain management system. These findings have implications for treating chronic, pervasive pain with behavioral medicine as a systemic intervention. Approaching yoga as complex system suggests the need for research of mind-body topics that focuses on long-term systemic changes rather than short-term isolated effects.

Normal cyber accidents

ABSTRACT Several of the most serious cyber incidents affecting critical infrastructure to date have been the result of collateral damage, indirect effects, malware that ‘escaped’ their intended target and/or incontrollable malware proliferation. This tendency has so far been under-explored in the International Relations (IR) literature, and its potential implications largely overlooked. By focusing on the role of socio-technical system dynamics, this article aims to contribute to advancing our understanding of collateral (incidental) damage and unexpected consequences connected to offensive cyber operations. More specifically, it introduces an analytical framework based on Normal Accidents (NA) theory. The framework highlights dynamics which make complex systems more difficult to analyse and more prone to cascading failures. Its application is explored using in-depth interviews and empirical case examples of large-scale cyber incidents. The results highlight the difficulty of achieving controlled and precise effects when disrupting components in complex systems. The article concludes with a discussion on the need for renewed attention to escalatory risks connected to destructive offensive cyber.

Beyond the Genome: Genetically Modified Crops in Africa and the Implications for Genome Editing

ABSTRACTGenome editing — a plant‐breeding technology that facilitates the manipulation of genetic traits within living organisms — has captured the imagination of scholars and professionals working on agricultural development in Africa. Echoing the arrival of genetically modified (GM) crops decades ago, genome editing is being heralded as a technology with the potential to revolutionize breeding based on enhanced precision, reduced cost and increased speed. This article makes two interventions. First, it identifies the discursive continuity linking genome editing and the earlier technology of genetic modification. Second, it offers a suite of recommendations regarding how lessons learned from GM crops might be integrated into future breeding programmes focused on genome editing. Ultimately, the authors argue that donors, policy makers and scientists should move beyond the genome towards systems‐level thinking by prioritizing the co‐development of technologies with farmers; using plant material that is unencumbered by intellectual property restrictions and therefore accessible to resource‐poor farmers; and acknowledging that seeds are components of complex and dynamic agroecological production systems. If these lessons are not heeded, genome‐editing projects are in danger of repeating mistakes of the past.

Метод та засоби тестування спеціалізованих компонентів мобільної робототехнічної платформи на робочих тактових частотах

Processes of development of test scenarios, selection of technological means of testing hardware and software of specialized components of mobile robotic platform are analyzed. The methods and means of testing specialized components of the mobile robotic platform when operating at working clock frequencies are studied. It is improved the method of testing the hardware and software of the specialized components of the mobile robotics platform. This improvement is made due to the development of specialized scenarios and adaptation of the technological environment to the requirements of a specific application, ensures an increase in the quality of testing in real time. It is shown that the main stages of testing specialized hardware at working clock frequencies are: the development of a test plan, setting of a working clock frequency, creation of a test environment, execution of tests, comparison of test results with reference results, analysis of comparison results. For testing, two environments and two test scenarios are developed: testing of means of encryption and masking of control commands of the mobile robotics platform; testing means of unmasking and deciphering control commands of a mobile robotics platform. It is developed a neurofuzzy control system for the autonomous control of the movement of a wheeled mobile robotic platform, the main components of which are intelligent remote navigation sensors, a rule base, fuzzification, decision-making and defuzzification blocks. The structure of means and scenarios for testing blocks of neuro-like encryption/decryption and masking/unmasking of mobile robotic platform control commands are developed, which provide joint testing of both software and hardware at working clock frequencies. It is developed the structure of tools and scenarios for testing the fuzzy mobile robotic platform control system, which are focused on sequential testing of fuzzification, decision-making, and defuzzification blocks and provide joint testing of both software and hardware tools at working clock frequencies in real time. Using the improved method, testing of the mobile robotic platform control system is performed, which confirmed the feasibility of the chosen approach. The implementation of the latest methods and means of testing specialized components of complex systems saves time and financial costs and contributes to the long-term operation of systems as a whole as a result of identifying problems in the process of their creation.