Complex Hierarchical Structure Research Articles

The place of comprehensive environmental and analytical assessment of the impact of the "CRE" system on environmental components in the EIA procedure

Problem. The operation of highways has a wide and multi-component impact on all components of the environment. Environmental Impact Assessment of roads is crucial for assessing the potential environmental consequences of road construction and operation. It is advisable to conduct this assessment based on a systemic analysis using a hierarchical structure for a comprehensive evaluation of the impact of the "CRE" system on the environment. Goal. The goal is to present the results of a comprehensive ecological-analytical assessment of the impact of roads on environment, which can be effectively used in the EIA procedure. Methodology. The 'CRE' system was analyzed using system analysis. Prioritization and weight coefficients for each element of the hierarchy were determined using expert-analytical assessments with the use of the MAI software complex. For a rapid assessment of the impact of road operation on the state of environment and the accumulation of heavy metals (HM) in vegetation and roadside soils, natural measurements and atomic absorption spectrometry methods were employed. Results. The expert-analytical research results revealed the contributions of each component within the complex hierarchical structure. Accumulation of HM in the roadside areas, including pollution of vegetation and soil, exceeding the permissible levels, was confirmed. Rank series were constructed: accumulation of HM in vegetation and soil samples, vegetation according to the property of accumulation of different HM in them. Originality. The study proposed a comprehensive assessment of the impact of road operation on the components of environment through an ecological-analytical evaluation of the developed hierarchical 'CRE' structure, resulting in the determination of weight coefficients and priorities of each element in the hierarchy's contribution to the environmental impact through expert-analytical assessment using the T. Saaty AНР method. Practical value. Using the proposed systematic approach in the EIA procedure for roads at every stage of their life cycle can improve control quality and the effectiveness of environmental protection measures, reducing environmental impact.

Elastic Network of Droplets for Underwater Adhesives.

Functionality in biological materials arises from complex hierarchical structures formed through self-assembly processes. Here, we report a kinetically trapped self-assembly of an elastic network of liquid droplets and its utility for tough and fast-acting underwater adhesives. This complex structure was made from a one-pot mixture of scalable small-molecule precursors. Liquid-liquid phase separation accompanied by silanol hydrolysis, condensation, and zwitterionic self-association yields a viscoelastic solid with interconnected liquid droplets. These hierarchical microstructures increase toughness and enable underwater adhesion for a range of substrates, offering a platform for robust adhesives for rapid underwater repair or emergency wound care.

Evolution of the Deformation‐ and Flow‐Induced Crystallization and Characterization of the Microstructure of a Single Spherulite, Lamella, and Chain of Isotactic Polypropylene

AbstractIsotactic polypropylene (iPP) is a frequently used polymer in industrial processing and scientific research, used as a model material. This review highlights some current and forefront issues in polymer science and engineering. This review alludes to classical deformation schemes to interpret the complicated deformation in isotactic polypropylene as a semicrystalline polymer. Also, due to the complex hierarchical structures of crystallized polymers, the progress on the multiscale and multistage microstructural evolution covering each deformation stage from the initiation of plasticity until failure is mainly concerned, particularly in terms of crystalline morphology changes. Specifically, the characterization of the microstructure evolution of a single spherulite, lamella, and chain of isotactic polypropylene will be emphasized and mentioned. Here, the authors are most interested in the results obtained from stretching (strain‐induced) orientation and crystallization studies accompanied by the in situ scattering and spectroscopy techniques offering a detection window from nano to micrometer scale, which should be able to reflect the microstructural changes at different length scales of interest. During polymer processing, the melts are frequently subjected to shear or/and elongation flow fields, producing flow‐induced molecular chain orientation in the melt. The oriented molecular chains crystallize differently than those encountered under quiescent conditions. Thus, orientation‐induced crystallization has long been an object of intense interest. A vast body of research is reported about the effects of preorientation on the crystallization of various polymers. Among them, iPP is most frequently studied since its diversified structure and morphology are very sensitive to changes in processing conditions and molecular parameters.

Ensemble model for accuracy prediction of protein secondary structure

<span>Predicting a protein’s secondary structure is crucial for understanding the working of proteins. Despite advancements over the years, the top predictors have achieved only 80% Q8 accuracy when sequence profile information is the sole input. An ensemble approach is proposed using convolutional neural network (CNN) and a classifier known as support vector machine (SVM) on both the partial and the whole CullPDB datasets. The protein secondary structure (PSS) has a complex hierarchical structure, as well as the ability to take into account the reliance between neighbouring labels. A detailed experiment yielding high levels of Q8 accuracy with scores of 97.91%, 85.13%, and 78.02% using 20%, 80%, and 100% respectively of the protein residues on the new predicted dataset CullPDB6133 which is better than the accuracies predicted by similar models. The proposed methodology highlights the use of CNN as a general framework, for efficiently predicting eight-state (Q8) accuracy of secondary protein structures with a low time and space complexity.</span>

Research of a Multi-Level Organization Human Resource Network Optimization Model and an Improved Late Acceptance Hill Climbing Algorithm

Complex hierarchical structures and diverse personnel mobility pose challenges for many multi-level organizations. The difficulty of reasonable human resource planning in multi-level organizations is mainly caused by ignoring the hierarchical structure. To address the above problems, firstly, a multi-level organization human resource network optimization model is constructed by representing the turnover situation of multi-level organizations in a dimensional manner as a multi-level network. Secondly, we propose an improved late acceptance hill climbing based on tabu and retrieval strategy (TR-LAHC) and designed two intelligent optimization operators. Finally, the TR-LAHC algorithm is compared with other classical algorithms to prove that the algorithm provides the best solution and can effectively solve the personnel mobility planning problem in multi-level organizations.

Deep feature representation with online convolutional adversarial autoencoder for nonlinear process monitoring

BackgroundThe significant nonlinearity between the monitoring variables introduces challenges in the task of features extraction when implementing fault detection for an industrial process. Recently, neural network with complex hierarchical structure and layer-by-layer nonlinear transformation, especially autoencoder (AE), have attracted considerable attention from the process monitoring community. However, the latent features of AE cannot fully reflect process information, and there is redundancy between features. MethodsThis study introduces an online convolutional adversarial autoencoder (AAE) model to learn nonlinear features with representative information of industrial processes. The structure of generative adversarial networks (GAN) in AAE aims to extract features that can reflect the manifold information and subject to the Gaussian distribution. Given the advantages of convolutional kernels in weight sharing and local perception, convolutional kernels are embedded in AAE to capture the spatial structure information of process data. On the basis of this model, the fault-relevant features selection strategy is designed to remove redundant information online and improve the accuracy of fault detection. Significant findingsThe results show that the average fault detection rate of the penicillin fermentation process can be improved to 94% using the proposed algorithm comparing with the current fault detection methods.

Strong and ductile medium-entropy alloy via coupling partial recrystallization and hierarchical precipitation

Developing alloys with high strength and large ductility has always been a relentless pursuit. Employing hierarchical microstructures ushers in a crescendo of achieving this goal. Partial recrystallization and hierarchical precipitation are two commonly used approaches to producing hierarchical microstructures. However, employing one of them often embrittles the material and reduces work-hardening capability. Herein, we report that via coupling partial recrystallization and hierarchical precipitation, a strong and ductile medium-entropy alloy (MEA) can be successfully developed. In the model (Ni3CrV)100-xAlx (x = 0, 3, 6 and 9 at.%) MEAs, increasing the Al content not only retards recrystallization due to the resultant solute drag and Zener pinning, but also promotes hierarchical precipitation through elemental partitioning, which produces hierarchical precipitates embedded in the partially recrystallized matrix with large chemical complexity. As a result, the dual-hierarchical structure endows (Ni3CrV)91Al9 alloy with a tensile yield strength of 1151 ± 33 MPa and an ultimate tensile strength of 1448 ± 17 MPa, as well as a uniform elongation of 17% ± 1%. The complex hierarchical structure brings multiple strengthening mechanisms, i.e., hetero-deformation-induced (HDI) strengthening, precipitation strengthening and dislocation strengthening, which lead to the observed high strength. Also, such complex structure heterogeneities facilitate multiple deformation behaviors, i.e., planar slip, wavy slip, stacking faults, dislocation networks and twinning deformation at different loading stages, which give rise to the large ductility and pronounced work-hardening capability.

3D-Printed Materials for Wastewater Treatment.

The increasing levels of water pollution pose an imminent threat to human health and the environment. Current modalities of wastewater treatment necessitate expensive instrumentation and generate large amounts of waste, thus failing to provide ecofriendly and sustainable solutions for water purification. Over the years, novel additive manufacturing technology, also known as three-dimensional (3D) printing, has propelled remarkable innovation in different disciplines owing to its capability to fabricate customized geometric objects rapidly and cost-effectively with minimal byproducts and hence undoubtedly emerged as a promising alternative for wastewater treatment. Especially in membrane technology, 3D printing enables the designing of ultrathin membranes and membrane modules layer-by-layer with different morphologies, complex hierarchical structures, and a wide variety of materials otherwise unmet using conventional fabrication strategies. Extensive research has been dedicated to preparing membrane spacers with excellent surface properties, potentially improving the membrane filtration performance for water remediation. The revolutionary developments in membrane module fabrication have driven the utilization of 3D printing approaches toward manufacturing advanced membrane components, including biocarriers, sorbents, catalysts, and even whole membranes. This perspective highlights recent advances and essential outcomes in 3D printing technologies for wastewater treatment. First, different 3D printing techniques, such as material extrusion, selective laser sintering (SLS), and vat photopolymerization, emphasizing membrane fabrication, are briefly discussed. Importantly, in this Perspective, we focus on the unique 3D-printed membrane modules, namely, feed spacers, biocarriers, sorbents, and so on. The unparalleled advantages of 3D printed membrane components in surface area, geometry, and thickness and their influence on antifouling, removal efficiency, and overall membrane performance are underlined. Moreover, the salient applications of 3D printing technologies for water desalination, oil-water separation, heavy metal and organic pollutant removal, and nuclear decontamination are also outlined. This Perspective summarizes the recent works, current limitations, and future outlook of 3D-printed membrane technologies for wastewater treatment.

Electronic Proves as One of the Modern Independent Means of Evidence

Objective: the purpose of this study is a determination of the procedural status of electronic documents as electronic evidence and identification of gaps in legislation regarding their legal regulation.
 
 Theoretical framework: theoretical materials were based on international scientific publications, reports, and scientific papers. And also for a more complete and objective presentation of the problem being studied, also practical materials from criminal cases were used.
 
 Method: is a dialectical method of understanding general patterns and particular manifestations of the essence of phenomena of objective reality. The comparative legal method made it possible to qualitatively study foreign legislation from the point of view of the legal regulation of electronic evidence and their application in practice. The method of mathematical analysis and logic made it possible to analyze everything that could relate to electronic evidence, identifying their characteristics.
 
 Results and conclusion: electronic evidence is information in electronic digital form, suitable for communication, interpretation or processing. In the electronic digital environment, the main interacting objects are various kinds of information objects (data sets, files, programs, etc.) with a complex hierarchical structure. Data in electronic digital form are intended to transmit information through media, information systems and telecommunication networks, and in the event of a crime being committed against them, their protection is of primary interest, and the nature and content of this data fades into the background.
 
 Originality/value: the value of the study lies in the fact that, based on a comprehensive analysis, electronic evidence has a unique structure and ensuring their safety is one of the criteria for their use, as well as the possibility of introducing a copying function will expand the process of processing evidence. The concept of “computer information” is the author’s definition, which will allow us to consolidate electronic evidence as an independent type.

Bone Hierarchical Structure: Heterogeneity and Uniformity

AbstractBone has a complex hierarchical structure with structural integration from nm to cm. The understanding of bone structure is developing rapidly due to improvements in available methodologies that allow unravelling structures across several length scales. These methods include advances in electron microscopy, in particular, focused ion beam scanning electron microscopy (FIB‐SEM), confocal laser scanning microscopy techniques, X‐ray imaging, X‐ray diffraction tomography (XRD‐CT), and tensor tomography (small angle X‐ray scattering tensor tomgraphy, SAXS‐TT and wide angle X‐ray scattering tensor tomgraphy, WAXS‐TT). Special emphasis is placed on the latter X‐ray techniques that are emerging into powerful tools. Through a review of selected recent results on the structure of the bone matrix as well as the lacuno‐canalicular network housing the osteocyte cells of bone, it is proposed that bone is more heterogeneous than typically described and that local variation in composition and crystallography may play a significant role in bone biology in health and disease.

Correlative study of liquid in human bone by 3D neutron microscopy and lab-based X-ray μCT.

Liquid plays an important role in bone that has a complex 3D hierarchical pore structure. However, liquid (water) is difficult to discern from e.g. an organic matrix by X-ray imaging. Therefore, we use a correlative approach using both high resolution X-ray and neutron imaging. Human femoral bone with liquid adsorbed into some of the pores was imaged with both the Neutron Microscope at the ICON beamline, SINQ at PSI, and by lab-based μCT using 2.7μm voxel size. Segmentation of the two datasets showed that, even though the liquid was clearly distinguishable in the neutron data and not in the X-ray data, it remained challenging to segment it from bone due to overlaps of peaks in the gray level histograms. In consequence, segmentations from X-ray and neutron data varied significantly. To address this issue, the segmented X-ray porosities was overlaid on the neutron data, making it possible to localize the liquid in the vascular porosities of the bone sample and use the neutron attenuation to identify it as H2O. The contrast in the neutron images was lowered slightly between the bone and the liquid compared to the bone and the air. This correlative study shows that the complementary use of X-rays and neutrons is very favorable, since H2O is very distinct in the neutron data, while D2O, H2O, and organic matter can barely be distinguished from air in the X-ray data.

E-Justice Texts: Factors of Borrowing Terms and Motivation

The research is based on the articles, overviews and reports of the participants of the academic research “Transformation of Legal Regulation of Relations, Connected with Application of Digital Technologies in Legal System and within the System of Enforcement of Court Rulings” from which there have been extracted and analyzed terms, recently borrowed from English into Russian, which occur in the texts of e-justice. There has been done an overview of the opinions of Russian and foreign linguists connected with the issue of acceptance of borrowings, that resulted in such features as expedience, common sense, and correspondence to the target language standard. There have been generalized extralinguistic and linguistic factors of borrowings.
 There have been stated that transmitted terms are borrowed in a ready-made form. They are motivated lexemes in both languages. Being included in the complex hierarchical structure of micro- and macro-fields, the transmitted terms become integral elements of the term system, acquiring the semantic properties of the basic system-forming terms. Their Greek and Latin term elements support the process of internationalization of vocabulary and the ways of its formation.
 Some unmotivated and partially motivated basic terms of the computer branch of knowledge are frequency terms for digital texts. These interdisciplinary terms meet the requirements of brevity, unambiguity, and lack of synonymity, which indicates their ideal compliance with the terminological nomination.

Chemical Structure and Side Reactions in Polyurea Synthesized via the Water-Diisocyanate Synthesis Pathway.

Industrial polyureas are typically synthesized using diisocyanates via two possible alternative pathways: the extremely quick and highly exothermal diamine-diisocyanate pathway and the relatively slow and mild water-diisocyanate pathway. Although polyurea synthesis via the water-diisocyanate pathway is known and has been industrially applied for many decades, there is surprisingly very little analytical information in the literature in relation to the type and extent of the occurring side reactions and the resulting chemical structures following this synthesis pathway. The synthesis of polyureas exhibiting very high concentrations of carbonyl-containing groups resulted in strong and accurate diagnostic analytical signals of combined FTIR and solid-state 13C NMR analysis. Despite the strictly linear theoretical chemical structure designed, the syntheses resulted in highly nonlinear and crosslinked polymers. It was analytically found that the water-diisocyanate pathway preferentially produced highly dominant and almost equal contents of both biuret structures and tertiary oligo-uret structures, with a very small occurrence of urea groups. This is in strong contrast with the chemical structures previously obtained via the diamine-diisocyanate polyurea synthesis pathway, which almost exclusively resulted in biuret structures. The much slower reaction and crosslinking rate of the water-diisocyanate synthesis pathway enabled the further access of isocyanate groups to the already-formed secondary nitrogens, thus facilitating the formation of complex hierarchical tertiary oligo-uret structures.

The development of a method for visualizing the states of the national security system

The scientific task, which is solved in the research, is the cognitive display of the state of the national security system with a complex hierarchical structure. As a rule, images are created individually taking into account a specific application field and interpreted by an expert (a group of experts) based on accumulated knowledge. Cognitive mapping is designed to support decision making by an expert (group of experts), monitoring and managing in real time. The object of research is the system of ensuring national security. The subject of the research is the functioning of the national security system. The research developed a method of visualization of the states of the national security system. An overview of the methods of visual graphic presentation of information about the state of multidimensional objects and systems was carried out. The novelties of the proposed method are: ‒ creation of a visual, multi-level and interconnected description of the national security system; ‒ increasing the efficiency of decision making while assessing the state of the national security system; ‒ solving the problem of falling into global and local extremes while assessing the state of the national security system; ‒ combination of graphic and numerical display of controlled state parameters of the national security system; ‒ avoiding the problem of loops while visualizing the state of the national security system in real time. The specified method should be implemented in specialized software, which is used to analyze the state of the national security system and make management decisions.

Predictors of Clinical Nurse Leader implementation success across a national sample of settings: A Bayesian multilevel modeling analysis.

The Clinical Nurse Leader (CNL) care model is a different way of organizing frontline nursing care delivery in contrast to the traditional "staff nurse" model and is increasingly being adopted by health systems across the United States and abroad. However, variability in implementation and outcomes has been noted across health settings. A recently validated CNL Practice Model provides an explanatory pathway for CNL model integration into practice. The purpose of this study was to identify and compare patterns of empirical correspondence to the CNL Practice Model and predict their influence on implementation success. We conducted a secondary analysis of a 2015 national-level study with clinicians and administrators involved with CNL initiatives in their health system. A psychometrically validated CNL Practice Survey was used to collect data measuring the presence (0%-100%) of the five domains of the CNL Practice Model (organizational readiness, CNL structuring, CNL practices, outcomes, and value) and one measure of CNL implementation success. We modeled the complex hierarchical structure of the data using a Bayesian multilevel regression mixed modeling approach. A zero-one-inflated beta distribution, a mixture of Bernoulli distributions for the minimum and maximum responses and a beta distribution for the responses between the minimum and maximum, was used to fit success ratings in the model. A total of 920 participants responded, 540 (59%) provided success scores. The model captured ratings skewed toward upper bound, while also adequately modeling data between the minimum and maximum values. The Bayesian model converged and gave estimates for all hierarchical parameters, which would likely have failed to converge in a pure maximum likelihood framework. The variability around success score across CNL Practice Model element ratings was greatest at the component level, 0.29 (0.18-0.48), compared to either the domain level, 0.16 (0.01-0.54), or the item level, 0.09 (0.01-0.17). The components most predictive of implementation success were (a) consensus CNL model can close gaps, (b) organization level implementation strategy, and (c) alignment of empirical CNL microsystem level structuring to the model's conceptualization. Findings provide further empirical evidence to support the explanatory pathway proposed by the CNL Practice Model and identified specific organizational readiness and CNL workflow structures that are critical antecedents predictive of CNL practice manifestation and production of expected outcomes. Findings indicate actionable implementation evidence that can be successfully adopted across real-world healthcare settings to achieve safer and higher quality patient care. CNL integrated care delivery is a frontline nursing care model that is being increasingly adopted by health systems across the United States and abroad. However, variability in CNL implementation and outcomes has been noted across health settings, limiting its evidence base. Findings of this study contribute a better understanding about the variability of CNL practice and outcomes found in the literature and contribute empirical and conceptual clarity about the relationships between modes of CNL implementation and successful adoption in healthcare settings.

Biomimetic Nacre-like Hydroxyapatite/Polymer Composites for Bone Implants.

One of the most ambitious goals for bone implants is to improve bioactivity, incapability, and mechanical properties; to reduce the need for further surgery; and increase efficiency. Hydroxyapatite (HA), the main inorganic component of bones and teeth, has high biocompatibility but is weak and brittle material. Cortical bone is composed of 70% calcium phosphate (CaP) and 30% collagen and forms a complex hierarchical structure with anisotropic and lamellar microstructure (osteons) which makes bone a light, strong, tough, and durable material that can support large loads. However, imitation of concentric lamellar structure of osteons is difficult to achieve in fabrication. Nacre from mollusk shells with layered structures has now become the archetype of the natural "model" for bio-inspired materials. Incorporating a nacre-like layered structure into bone implants can enhance their mechanical strength, toughness, and durability, reducing the risk of implant catastrophic failure or fracture. The layered structure of nacre-like HA/polymer composites possess high strength, toughness, and tunable stiffness which matches that of bone. The nacre-like HA/polymer composites should also possess excellent biocompatibility and bioactivity which facilitate the bonding of the implant with the surrounding bone, leading to improved implant stability and long-term success. To achieve this, a bi-directional freeze-casting technique was used to produce elongated lamellar HA were further densified and infiltrated with polymer to produce nacre-like HA/polymer composites with high strength and fracture toughness. Mechanical characterization shows that increasing the ceramic fractions in the composite increases the density of the mineral bridges, resulting in higher flexural and compressive strength. The nacre-like HA/(methyl methacrylate (MMA) + 5 wt.% acrylic acid (AA)) composites with a ceramic fraction of 80 vol.% showed a flexural strength of 158 ± 7.02 MPa and a Young's modulus of 24 ± 4.34 GPa, compared with 130 ± 5.82 MPa and 19.75 ± 2.38 GPa, in the composite of HA/PMMA, due to the higher strength of the polymer and the interface of the composite. The fracture toughness in the composition of 5 wt.% PAA to PMMA improves from 3.023 ± 0.98 MPa·m1/2 to 5.27 ± 1.033 MPa·m1/2 by increasing the ceramic fraction from 70 vol.% to 80 vol.%, respectively.

Multiple stakeholders’ perspectives of marine social ecological systems, a case study on the Barents Sea

The Barents Sea ecosystem components and services are under pressure from climate change and other anthropogenic impacts. Following an Ecosystem-based management approach, multiple simultaneous pressures are addressed by using integrative strategies, but regular prioritization of key issues is needed. Identification of such priorities is typically done in a ‘scoping’ phase, where the characterization of the social-ecological system is defined and discussed. We performed a scoping exercise using an open and flexible multi-stakeholder approach to build conceptual models of the Barents Sea social-ecological system. After standardizing vocabulary, a complex hierarchical model structure containing 155 elements was condensed to a simpler model structure containing a maximum of 36 elements. To capture a common understanding across stakeholder groups, inputs from the individual group models were compiled into a collective model. Stakeholders' representation of the Barents Sea social-ecological system is complex and often group specific, emphasizing the need to include social scientific methods to ensure the identification and inclusion of key stakeholders in the process. Any summary or simplification of the stakeholders' representation neglects important information. Some commonalities are highlighted in the collective model, and additional information from the hierarchical model is provided by multicriteria analysis. The collective conceptual stakeholder model provides input to an integrated overview and strengthens prioritization in Ecosystem-based management by supporting the development of qualitative network models. Such models allow for exploration of perturbations and can inform cross-sectoral management trade-offs and priorities.

What’s so hard about hierarchical control? Pinpointing processing constraints within cue-based and serial-order control structures

Most task spaces require a hierarchical structure, where decisions on one level are contingent on previous decisions made on one or more higher levels. While it is a truism that increasing the number of hierarchical levels makes it harder to solve a given task, the exact nature of this “number-of-levels” effect is not clear. On the one hand, processing costs might be strictly “local,” incurred only when higher-level settings need to be updated, while otherwise lower-level decisions are insulated from the presence of higher-level settings (local updating costs with ballistic control). On the other hand, maintaining and integrating more complex hierarchical structures could require overall greater representational resources, negatively affecting each individual decision within the represented task space (global integration/maintenance costs). Further, navigation through hierarchical structures can be guided either through prompts in the environment (cue-based), or through sequential plans (serial-order based), with potentially distinct maintenance and updating demands. In two experiments, we assessed performance as a function of hierarchical level and format (serial-order vs. cue-based). Model comparisons showed that the pattern of costs in the serial-order format was consistent with a global maintenance/integration account. In contrast, in the cue-based format, costs arose at updating points and when one additional relevant level beyond the current decision was relevant, while additional levels produced no further costs. This overall constellation of costs can be explained by assuming that each decision requires checking the immediately relevant higher-level context for that decision. For cue-based control, this context involves the “next-level-up” rule, whereas in the serial-order format, each trial requires updating of the current position within the sequence, which in turn requires integration across all relevant hierarchical levels.

Research on the assessment of the risk situations emergence for automated control systems of the metallurgical industry companies

The article presents the assessment method of the risk situations emergence based on the analysis of the probability and frequency of threats emergence for automated control systems of the metallurgical industry companies (ACSMIC). The analysis of the risk dependency on vulnerabilities and threats to ACSMIC assets is carried out, and the relationship between risk, vulnerability, and threat is presented according to a three-level risk assessment scale. When identifying and assessing the risk, existing countermeasures against both external and internal attacks are taken into account, which, in turn, reduces the risk level for the protected organization. In the process of any organization functioning, there may be a need to reassess the risk associated with changes in the structure of the control organization or security policy in the system of the metallurgical industry companies. Therefore, the existing countermeasures are closely related to the concept of risk and require constant correction in the assessment process. Risk is related to the reliability of the functioning of the studied control structure of units and aggregates and their ability to function in accordance with the mode and goals of the production structure in various branches and levels of the hierarchy with an acceptable risk assessment. On the basis of the system concepts and assessment methods of the acceptable risk level, the system-oriented method of identifying the reliability of functioning of self-recovering complex hierarchical structures is proposed, the classification of the risk types of unforeseen situations in complex human-oriented systems that can be applied and implemented in hierarchical control systems is carried out.

Exploring and Understanding the Multiscale Mechanical Degradation in Graphene Assemblies via Practical Microstructure Guided Modeling

AbstractExploring and understanding the structure‐mechanical property relations in hierarchical graphene assemblies is crucial for optimizing their mechanical properties and developing new functionalities, as the tensile strength is two orders of magnitude degradation from pristine graphene to graphene assemblies. Yet, quantifying the strength degradation across multiscale is a challenge due to the complex hierarchical structures. Thus, key structures and dominated factors at different lengthscales that affect the mechanical properties of graphene assemblies should be extracted for the reasonable unveiling of this problem. In this study, the multiscale mechanical degradation of graphene assemblies through practical microstructure‐guided multiscale modeling is characterized. Combining with experimental observations, three representative models are developed to study the mechanical behaviors of graphene assemblies at different lengthscales. Then, the dominated factors affecting the strength at these lengthscales are identified, that is the defects in monolayer graphene, tension‐shear load transfer for stacked graphene, and uniformity of graphene assemblies. Based on the simulation results, the structure‐strength relation of graphene assemblies is given, and practical strategies are proposed followed by experimental realization, to significantly improve the mechanical properties of graphene‐based nanocomposites.