Complex Hierarchical Structure Research Articles

The Complex Latent Structure of Attenuated Psychotic Symptoms: Hierarchical and Bifactor Models of SIPS Symptoms Replicated in Two Large Samples at Clinical High Risk for Psychosis.

The Structured Interview for Psychosis-Risk Syndromes (SIPS) and other assessments of psychosis risk define clinical high risk for psychosis (CHR) by the presence of attenuated psychotic symptoms. Despite extensive research on attenuated psychotic symptoms, substantial questions remain about their internal psychometric structure and relationships to comorbid non-psychotic symptoms. Hierarchical and bifactor models were developed for the SIPS in a large CHR sample (NAPLS-3, N = 787) and confirmed through preregistered replication in an independent sample (NAPLS-2, N = 1043). Criterion validity was tested through relationships with CHR status, comorbid symptoms/diagnoses, functional impairment, demographics, neurocognition, and conversion to psychotic disorders. Most variance in SIPS items (75%-77%) was attributable to a general factor. Hierarchical and bifactor models included a general factor and five specific/lower-order factors (positive symptoms, eccentricity, avolition, lack of emotion, and deteriorated thought process). CHR participants were elevated on the general factor and the positive symptoms factor. The general factor was associated with depressive symptoms; functional impairment; and mood, anxiety, and schizotypal personality diagnoses. The general factor was the best predictor of psychotic disorders (d ≥ 0.50). Positive symptoms and eccentricity had specific effects on conversion outcomes. The deteriorated thought process was least meaningful/replicable. Attenuated psychotic symptoms, measured by the SIPS, have a complex hierarchical structure with a strong general factor. The general factor relates to internalizing symptoms and functional impairment, emphasizing the roles of general psychopathological distress/impairment in psychosis risk. Shared symptom variance complicates the interpretation of raw symptom scores. Broad transdiagnostic assessment is warranted to model psychosis risk accurately.

Biological and bio-inspired materials: Multi-scale modeling, artificial intelligence approaches, and experiments

Biological materials often possess remarkable properties and functionalities owing to their complex hierarchical and composite structures. Learning from nature can lead to revolutionary breakthroughs in materials science and innovative new technologies. This Special Issue titled “Biological and Bio-inspired Materials: Multi-scale Modeling and Artificial Intelligence Approaches” is a collection of research articles and comprehensive reviews utilizing multi-scale modeling, artificial intelligence approaches, and experiments to elucidate the characteristics of biological materials and design and optimize bio-inspired materials. The computational approaches of interest include but are not limited to molecular dynamics, lattice spring models, finite element analysis, genetic algorithms, neural networks, generative adversarial networks, and other modeling and artificial intelligence approaches for better understanding the structure-property relationships and underlying mechanisms of biological (natural) materials, and reproducing, designing, and optimizing bio-inspired materials. Novel experimental results, fabrication strategies, and applications of biological, bio-inspired and biomedical materials are also collected in this special issue.

Makespan minimization for workflows with multiple privacy levels

With the advanced development of Metaverse, various service requests are required to be processed as soon as possible which contains a series of tasks with topology structure (workflow) and different privacy constraints. Due to the diversity and complex hierarchical structure of resources, how to rent suitable resources from cloud, edge, and local nodes to minimize the makespan of workflows is currently one of the key issues in service computing scenarios. Due to the limited computing resources of local devices, tasks need to be offloaded to edge or cloud servers. Based on task privacy constraints, it is challenging to determine the appropriate offloading nodes for each task in the workflow. In this paper, for workflows with multi-privacy level tasks, a privacy-preserving workflow scheduling algorithm based on clustering (PWCSA) is proposed to minimize the workflow makespan with limited bandwidth. Multiple tasks of different privacy levels are clustered into one task pipeline to reduce the data transmission time caused by limited bandwidth, thereby reducing workflow makespan. After statistically calibrating algorithm parameters over a comprehensive set of workflow instances, the proposed algorithms are compared with the state-of-the-art algorithms over five workflow-type instances. The results indicate that the proposed PWCSA algorithm outperforms other algorithms with acceptable computational time. Through a number of workflow instances, the system parameters are verified by analysis of variance methods.

Examining the effects of prekindergarten to second grade classroom quality on early elementary achievement

Previous research has investigated the influence of preschool interventions in early elementary environments with some results suggesting that high quality early preschool experiences may have meaningful effects on student academic outcomes not only immediately following the preschool years but also in the long term. This study aimed to investigate this topic further by using a novel cross-classified model to account for the complex hierarchical structure of a large-scale longitudinal and multilevel dataset. First, we examined the carryover classroom effects from prekindergarten (PK), kindergarten (K), and first grade on later grades’ reading and mathematics scores. Second, we explored the effect of PK to second grade classroom quality on students’ K, first, and second grade reading and mathematics scores. Our analyses yielded significant PK carryover classroom effects for reading; these results provide evidence that a student’s PK classroom continues to impact that student for at least three years after. Additionally, one domain of PK classroom quality —classroom organization—was found to be positively related to students’ K, first, and second grade reading outcomes.

Иерархическая модель оптимизации технологических параметров комплекса рабочих переходов для процесса механической обработки

Optimization of the parameters of the product manufacturing process is one of the key tasks of technological preparation of production. The technological process of mechanical processing has a complex hierarchical struc-ture. The effectiveness of optimizing the manufacturing process of a product directly depends on the level of its detail and the optimal choice of targets and control parameters. In this case, the technological process of mechanical processing, as an object of control, can be described in the form of a hierarchical model. Thus, the task of optimizing the technological process of mechanical processing is to determine the optimal values of control parameters for each structural element (intermediate state of the control object) of the hierarchical control model. The aim of the work is to develop a hierarchical model for optimizing the parameters of a complex of working transitions for machining operations. The structure of the hierarchical model of the product manufacturing process on metal-cutting machines is described. Within the framework of the developed model, five control levels are identified, control parameters for individual structural elements of the model are defined, as well as the relationship between them. For the intermediate states of the control object (structural elements), a description of single and vector optimization criteria is presented. The practical implementation of the developed control model is presented using the example of optimizing the technological parameters for the “Bushing” part. The application of the developed control model will increase the efficiency of the technological process of manufacturing products on metal-cutting machines by optimizing technological parameters based on a multi-criteria analysis at the stage of technological preparation of production.

Hierarchical knowledge graph relationship prediction leverage of axiomatic fuzzy set graph structure

Knowledge graph embedding has found widespread application across various fields due to its inherent structured data. However, some non-graph-based models necessitate improvement in accurately describing complex hierarchical structures and connecting intricate relational paths. Graph neural networks embedding methods provide a promising direction for exploring complex hierarchical modeling, facilitating the flow of information from nodes along relational paths. Nevertheless, the deficient interpretable characterization by the saliency estimation in entities, which contributes to the efficient performance of the model, is difficult to comprehend. This paper proposes a novel model to mitigate the described phenomenon by intensifying the original structure of knowledge graphs. Specifically, it integrates the Axiomatic Fuzzy Set entity semantic extraction framework with a heterogeneous relationship self-attention mechanism to bolster node representation. Next, this framework incorporates a convolutional neural network model to increase the interaction capabilities of entities and relations. This interpretable entity-level attention mechanism complements the structural information of the knowledge graphs, enabling more comprehensive neighbor information capture and improving the ability to identify the correct entity location. Experimental validation on two relationally complex datasets demonstrates superior performance compared to current methods, obtaining a new Hit@1 value of 45.6% on WN18RR.

Chiral Transfer and Evolution in Cysteine Induced Cobalt Superstructures.

Chiral organic additives have unveiled the extraordinary capacity to form chiral inorganic superstructures, however, complex hierarchical structures have hindered the understanding of chiral transfer and growth mechanisms. This study introduces a simple hydrothermal synthesis method for constructing chiral cobalt superstructures with cysteine, demonstrating specific recognition of chiral molecules and outstanding electrocatalytic activity. The mild preparation conditions allow in situ tracking of chirality evolution in the chiral cobalt superstructure, offering unprecedented insights into the chiral transfer and amplification mechanism. The resulting superstructures exhibit a universal formation process applicable to other metal oxides, extending the understanding of chiral superstructure evolution. This work contributes not only to the fundamental understanding of chirality in self-assembled structures but also provides a versatile method for designing chiral inorganic nanomaterials with remarkable molecular recognition and electrocatalytic capabilities.

A Hybrid Approach Based on Consensus Decision Making for Green Supplier Selection in Automotive Industry

With increased global commerce, businesses must manage their supply chains while taking into account not only costs but also environmental implications. The decision-making of Green Supplier Selection (GSS) is a strategic priority for companies to survive in challenging market conditions and to effectively and sustainably manage their supply chains in the increasingly polluted and resource-depleted world. Environmental sustainability can be enhanced with the appropriate criteria when choosing green suppliers. Based on these motivations, it is necessary to determine the correct criteria, classify the chosen criteria and employ an effective evaluation method in the GSS process. In particular, evaluating each criterion at its own level is of strategic importance. In this paper, the GSS model, handled by group decision-making, is constructed with multi-sub-criteria to increase the competitive advantage of businesses in challenging market conditions for the purpose of ensuring a sustainable future. A novel hybrid methodology of the Interval Type-2 Fuzzy (IT2F) Analytical Hierarchy Process (AHP) and IT2F Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is presented for the GSS model to deal with uncertainty. This study provides decision-makers with an effective method that performs fuzzy calculations at all steps until a solution is found, especially in areas that may have a complex hierarchical structure, such as the automotive industry. In the proposed method, unlike most studies in the literature, if a criterion has sub-criteria (or multi-sub-criteria) in the hierarchy considered, each criterion is evaluated with other criteria at its own level, without the need for all other criteria to have sub-criteria (or multi-sub-criteria). The effectiveness of the proposed method has been demonstrated by testing it with an application taken from the automotive industry with a complex-structured multi-level hierarchy. Additionally, sensitivity analysis has been conducted to assess the impact of changes in subjective input by means of scenarios.

Cloud-edge-end workflow scheduling with multiple privacy levels

The cloud-edge-end architecture satisfies the execution requirements of various workflow applications. However, owing to the diversity of resources, the complex hierarchical structure, and different privacy requirements for users, determining how to lease suitable cloud-edge-end resources, schedule multi-privacy-level workflow tasks, and optimize leasing costs is currently one of the key challenges in cloud computing. In this paper, we address the scheduling optimization problem of workflow applications containing tasks with multiple privacy levels. To tackle this problem, we propose a heuristic privacy-preserving workflow scheduling algorithm (PWHSA) designed to minimize rental costs which includes time parameter estimation, task sub-deadline division, scheduling sequence generation, task scheduling, and task adjustment, with candidate strategies developed for each component. These candidate strategies in each step undergo statistical calibration across a comprehensive set of workflow instances. We compare the proposed algorithm with modified classical algorithms that target similar problems. The experimental results demonstrate that the PWHSA algorithm outperforms the comparison algorithms while maintaining acceptable execution times.

Hyperbolic Graph Diffusion Model

Diffusion generative models (DMs) have achieved promising results in image and graph generation. However, real-world graphs, such as social networks, molecular graphs, and traffic graphs, generally share non-Euclidean topologies and hidden hierarchies. For example, the degree distributions of graphs are mostly power-law distributions. The current latent diffusion model embeds the hierarchical data in a Euclidean space, which leads to distortions and interferes with modeling the distribution. Instead, hyperbolic space has been found to be more suitable for capturing complex hierarchical structures due to its exponential growth property. In order to simultaneously utilize the data generation capabilities of diffusion models and the ability of hyperbolic embeddings to extract latent hierarchical distributions, we propose a novel graph generation method called, Hyperbolic Graph Diffusion Model (HGDM), which consists of an auto-encoder to encode nodes into successive hyperbolic embeddings, and a DM that operates in the hyperbolic latent space. HGDM captures the crucial graph structure distributions by constructing a hyperbolic potential node space that incorporates edge information. Extensive experiments show that HGDM achieves better performance in generic graph and molecule generation benchmarks, with a 48% improvement in the quality of graph generation with highly hierarchical structures.

Soviet Federal Project and the Collisions of Nation Building in the 1920-1930s

The author examines the collisions of the Soviet nation-building which are actively debated in modern historiography. The source base for the research is the published materials, as well as the archival documents of the Russian State Archive of Socio-Political History and the Archive of the President of the Republic of Kazakhstan. There are analyzed some specific historical subjects and the contribution of modern historiography to the understanding of the current problems and the formulation of new ideas and approaches to the analysis of the designated set of issues including the tools of Soviet socio-political engineering, the role and significance of ethnopolitical elites in the creation of the USSR as a complex hierarchical structure. It is concluded that the contradictions in the implementation of ethnonational policy were directly related to the formation of the Soviet federation, which became a concession to the ethnopolitical movements of the early XX century; and managing diversity was the central task of the state as an alternative to previous political systems for culturally complex societies.

Colloidal study on the texture of sous vide (SV)-processed Tibetan chicken breast with human-machine collaborated electromyography analysis

Sous Vide (SV) is a potential meat thermal processing technology. However, the texture evolution of SV processed meat is not easy to understand due to the complex hierarchical structure of meat. In our research, a two-level meat colloidal model was carried out to analyze the texture of SV-processed Tibetan chicken breast. Moreover, electromyography analysis was used to collaborate the texture results between human sensory evaluation and machine detection. Although the denaturation and dehydration of sub-continuous phase cytoskeletal proteins are unavoidable even under 55 °C at 1 h or 65 °C at 0.5 h heating, the main-continuous phase collagen can achieve slow denaturation and sufficient conversion to gelatin if SV is applied. Different from collagen under high-temperature processing, gelatin does not exert shrinkage stress to the sub-continuous phase upon SV. Moreover, insignificant lateral contraction was revealed by differential scanning calorimetry (DSC) and scanning electron microscopy (SEM). Thus, limited water migration and minimized cooking loss were achieved because of the integrity of the colloid system upon SV. Furthermore, physical properties proved the tenderization effect of SV. Thus, our results showed that the colloidal model combined with an electromyography study was useful in revealing the textual detail of pure meat or imitation meat.

Моделирование работы технологической цепи с вертикальными и горизонтальными взаимосвязями участников и учетом спроса

The subject of the article is the study of production structures consisting of a set of independent firms (enterprises interacting with each other through the supply of products through horizontal and vertical links in a single production and technological chain. It is assumed that firms at different levels of the chain not only supply components up, but also sell them on the market, organizing interaction with wholesale buyers. The mechanism of interaction takes into account economic interests, the demand for the company’s products in the market and reflects the situation of equilibrium. The scientific novelty of the study is the deepening of the methodological basis for describing the interaction of participants in the technological chain using matrix modeling based on the principles of constructing matrices of intersectoral balance. Such characteristics of firms and the system as a whole as: final product, gross product, resource volumes are considered. On the basis of the obtained final formulas, the dependences of the economic characteristics of the activity of firms and the system as a whole on the decrease (increase) in prices for resources, on changes in demand are established. The proposed approach to determining the final product of the system seems to be quite reasonable: firstly, the output volumes of the system are linked through the interaction of the manufacturer and seller, taking into account market demand, and, secondly, the volume of sales reflects the economic interests of the parties. The ratio “the price of products on the market – the volume of supply” becomes justified on the part of the demand function (the end consumer), the seller and the manufacturer of products. Such an optimization approach to determining the results of interaction promptly takes into account changes in the market environment and adjusts the performance indicators of participants in the direction of their consistency, which makes it possible to increase the stability of the system. It is established that when the market situation worsens as a result of rising prices for factors of production or a reduction in effective demand, there is a percentage decrease in final output in value terms, but the rate of its reduction is lower. The case of a decrease in demand has a more negative impact on the economy of the firm and the entire system. Thus, the growth rates of the economy and the well-being of the population are determined by the efficiency of complex hierarchical structures with horizontal and vertical relationships.

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.