Dynamic Hierarchy Research Articles

Multi-objective optimization of automotive seat frames using machine learning

The optimal design of automobile seats plays an important role in passenger safety in high-speed accidents. In order to enhance the accuracy of the prediction of the input variables and output response of the seat, a hybrid machine learning prediction model that combines the improved gray wolf optimizer (IGWO) and back propagation neural network (BPNN) has been proposed, and the prediction effect of the model was validated using the seat simulation data. Initially, based on the experimental data, finite element models were developed for eight typical working conditions of automobile seats and their accuracy was validated. Subsequently, the energy absorption to mass ratio method was employed to screen the design variables, resulting in the selection of 17 thickness variables and 15 material variables. Thereafter, the gray wolf optimizer (GWO) algorithm underwent enhancement through the incorporation of the dynamic leadership hierarchy (DLH) mechanism and the revision of the positional formula, yielding the IGWO algorithm. Following this, the IGWO algorithm was applied to optimize the hyperparameters of BPNN, culminating in the establishment of the IGWO-BPNN model. Ultimately, the seat multi-objective optimization design process was addressed using multi-objective gray wolf optimizer (MOGWO) to achieve the Pareto frontier, while the decision-making was conducted using the combined compromise solution (CoCoSo) method to determine the best trade-off solution. Furthermore, the effectiveness of the proposed optimal design method is evidenced by comparing the baseline design, simulation analysis, and optimal design methods. The results indicate that the optimized automotive seat frame achieves a reduction in cost by 20.7 % and mass by 22.9 %, simultaneously maintaining safety performance. Consequently, the proposed optimization design methodology is demonstrated to be highly effective for the multi-objective optimization design of automotive seat frames.

The temporal asymmetry of cortical dynamics as a signature of brain states

The brain is a complex non-equilibrium system capable of expressing many different dynamics as well as the transitions between them. We hypothesized that the level of non-equilibrium can serve as a signature of a given brain state, which was quantified using the arrow of time (the level of irreversibility). Using this thermodynamic framework, the irreversibility of emergent cortical activity was quantified from local field potential recordings in male Lister-hooded rats at different anesthesia levels and during the sleep-wake cycle. This measure was carried out on five distinct brain states: slow-wave sleep, awake, deep anesthesia–slow waves, light anesthesia–slow waves, and microarousals. Low levels of irreversibility were associated with synchronous activity found both in deep anesthesia and slow-wave sleep states, suggesting that slow waves were the state closest to the thermodynamic equilibrium (maximum symmetry), thus requiring minimum energy. Higher levels of irreversibility were found when brain dynamics became more asynchronous, for example, in wakefulness. These changes were also reflected in the hierarchy of cortical dynamics across different cortical areas. The neural dynamics associated with different brain states were characterized by different degrees of irreversibility and hierarchy, also acting as markers of brain state transitions. This could open new routes to monitoring, controlling, and even changing brain states in health and disease.

Holacracy: redefining organizational ontology and epistemology

Purpose This paper aims to examine Holacracy as a self-management system and its implications for organizational theory. By exploring its distinctive ontology and epistemology, the paper explains how Holacracy redefines organizational structures and contributes to organizational change. Design/methodology/approach Adopting a conceptual approach, this essay draws upon existing literature to explore Holacracy’s departure from conventional organizational theories. Findings The paper discusses how Holacracy challenges traditional organizational concepts, defining its holarchical approach, and emphasizing dynamic hierarchies and role-based organization. It also highlights Holacracy’s focus on emergent intelligence, continuous feedback loops and structured knowledge acquisition through roles and organization spaces. By examining its unique ontology and epistemology, the paper offers insights into how Holacracy aligns with an emergent paradigm known as integral, metamodern or teal. Research limitations/implications Further empirical research is needed to assess its practical implementation and long-term effects on organizational performance and employee well-being. Future studies could also explore challenges and limitations associated with adopting Holacracy in different organizational contexts. Practical implications Organizations considering alternative ways of working should examine Holacracy. Implementing Holacracy requires understanding its principles and may involve significant changes, with potential benefits such as increased agility and employee engagement. Social implications Holacracy’s departure from traditional structures and approach to power can have broader social implications and contribute to more democratic and participatory cultures. Originality/value This paper is a pioneering contribution to the under-researched domain of alternative organizational models. It sheds light on the originality and distinctiveness of Holacracy, highlighting its unique approach to hierarchy, role-based organization and consciousness within organizations.

DASH properties: Estimating atomic and molecular properties from a dynamic attention-based substructure hierarchy.

Recently, we presented a method to assign atomic partial charges based on the DASH (dynamic attention-based substructure hierarchy) tree with high efficiency and quantum mechanical (QM)-like accuracy. In addition, the approach can be considered "rule based"-where the rules are derived from the attention values of a graph neural network-and thus, each assignment is fully explainable by visualizing the underlying molecular substructures. In this work, we demonstrate that these hierarchically sorted substructures capture the key features of the local environment of an atom and allow us to predict different atomic properties with high accuracy without building a new DASH tree for each property. The fast prediction of atomic properties in molecules with the DASH tree can, for example, be used as an efficient way to generate feature vectors for machine learning without the need for expensive QM calculations. The final DASH tree with the different atomic properties as well as the complete dataset with wave functions is made freely available.

Species-specific behavioural responses to environmental variation as a potential species coexistence mechanism in ants.

A fundamental question of ecology is why species coexist in the same habitat. Coexistence can be enabled through niche differentiation, mediated by trait differentiation. Here, behaviour constitutes an often-overlooked set of traits. However, behaviours such as aggression and exploration drive intra- and interspecific competition, especially so in ants, where community structure is usually shaped by aggressive interactions. We studied behavioural variation in three ant species, which often co-occur in close proximity and occupy similar dominance ranks. We analysed how intra- and allospecific aggression, exploration and foraging activity vary under field conditions, namely with temperature and over time. Behaviours were assessed for 12 colonies per species, and four times each during several months. All behavioural traits consistently differed among colonies, but also varied over time and with temperature. These temperature-dependent and seasonal responses were highly species-specific. For example, foraging activity decreased at high temperatures in Formica rufibarbis, but not in Lasius niger; over time, it declined strongly in L. niger but much less in F. rufibarbis. Our results suggest that, owing to these species-specific responses, no species is always competitively superior. Thus, environmental and temporal variation effects a dynamic dominance hierarchy among the species, facilitating coexistence via the storage effect.

Cognitive hierarchies for games in extensive form

In the cognitive hierarchy (CH) framework, players in a game have heterogeneous levels of strategic sophistication. Each player believes that other players in the game are less sophisticated, and these beliefs correspond to the truncated distribution of a “true” distribution of levels. We develop the dynamic cognitive hierarchy (DCH) solution by extending the CH framework to games in extensive form. Initial beliefs are updated as the history of play provides information about players' levels of sophistication. We establish some general properties of DCH and fully characterize the DCH solution for a wide class of centipede games. DCH predicts a strategy-reduction effect: there will be earlier taking if the centipede game is played as an alternating-move sequential game rather than as a simultaneous move game in its reduced normal form. Experimental evidence reported in García-Pola et al. (2020a) supports this prediction. In all three centipede games for which the DCH strategy-reduction effect is predicted, termination occurs earlier when played sequentially rather than simultaneously with reduced strategies. In a fourth centipede game, where this effect is not predicted, it is not observed.

Perceptions about perioperative communication among anesthesiologists and surgeons and the impact of perceived status hierarchies on teamwork in the operating room

ObjectivesThis exploratory study examines anesthesiologists' and surgeons' perceptions regarding perioperative communication and the extent to which any differences or barriers to effective communication are a function of a perceived power dynamic and/or occupational status hierarchies between them. MethodsSemi-structured interviews were conducted with 11 attending anesthesiologists and 10 attending surgeons actively practicing clinically in the United States to explore their perceptions about perioperative communication, status hierarchies in the medical profession, and effective teamwork. The interviews were analyzed using Interpretative Phenomenological Analysis to identify recurrent and overarching themes. ResultsBoth groups acknowledged the role of effective perioperative communication in ensuring optimal surgical outcomes and identified factors such as team inconsistencies and lack of familiarity with the team as major barriers to effective teamwork. Multiple other occupational and institutional barriers were identified. Both clinician groups alluded to a perceived hierarchy in certain contexts, which can impact expectations and communication dynamics within the operating room environment. This appears to be a nuanced and complex relationship which is manifested via several indirect pathways. ConclusionThere are multiple cultural, occupational, and institutional factors that impact the quality of perioperative communication between anesthesiologists and surgeons. The mechanisms by which perceptions around occupational status hierarchies act as a barrier to effective communication appear to be indirect, complex, and warrant further exploration.

A dynamic data access control scheme for hierarchical structures in big data

Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is a promising solution to address the issues of data leakage and sharing in big data. When multiple files are shared, they often have hierarchical access relationships. However, existing hierarchical data CP-ABE schemes can only support simple hierarchical access structures. The limitation arises, necessitating the generation of multiple access structures to meet hierarchical access requirements, resulting in the wastage of computational and storage resources. Therefore, in this paper, we carefully design a dynamic hierarchy access control that allows its level nodes to flexibly participate in the decryption of other nodes, enabling the realization of complex hierarchical data relationships. Building upon this, we construct the Attribute-based Dynamic Hierarchical data Access Control (ADHAC) scheme, achieving efficient and dynamic access control for multiple datasets. Subsequently, we apply our scheme in distributed computing, which enables efficient access control for individuals and institutions over multiple data sets and fine-grained computation. Security analysis indicates that our scheme can resist chosen plaintext attack (CPA). Both theoretical and experimental analyses demonstrate that our scheme has impressive advantages in terms of computational efficiency and storage costs compared to existing schemes.

‘Guaranteed contraband’: a cultural biography of kaçak tea in Gaziantep’s Iranian bazaar

ABSTRACT Every year, 80,000 tons of kaçak (contraband) tea, primarily of Sri Lankan and Iranian origin, makes its way to the markets of Turkey – itself the fifth-largest producer of tea in the world. While most kaçak commodities in Turkey face derision because they are understood as low-quality approximations of their formal counterparts, of dubious origins and lacking any guarantees of quality, many consumers of kaçak tea valorize it as an emblem of superior taste. Rather than being a target of derision, kaçak tea takes shape in its consumers ‘thin-waisted’ glasses as a sign of distinction. In this inverted hierarchy of values, the domestic produce is the commodity that is mocked for its weak flavor and inflated price, while the informal contraband commodity is prized. By tracing the cultural biography of kaçak tea, this essay advances a historically and geographically networked understanding of commoditization across the formal/informal divide. Studying ‘guaranteed contraband’ tea across Turkey and Iran proves productive for understanding how people negotiate and build dynamic hierarchies of taste, while transforming the confines of the formal national economy into new thresholds of conversion that draw upon formalization of informality as well as informalization of formality in market formation.

Dynamic Power Hierarchies: Controlling Isolated Micro-grids with Precision

Dynamic Power Hierarchies: Controlling Isolated Micro-grids with Precision

Functional hierarchies in brain dynamics characterized by signal reversibility in ferret cortex.

Brain signal irreversibility has been shown to be a promising approach to study neural dynamics. Nevertheless, the relation with cortical hierarchy and the influence of different electrophysiological features is not completely understood. In this study, we recorded local field potentials (LFPs) during spontaneous behavior, including awake and sleep periods, using custom micro-electrocorticographic (μECoG) arrays implanted in ferrets. In contrast to humans, ferrets remain less time in each state across the sleep-wake cycle. We deployed a diverse set of metrics in order to measure the levels of complexity of the different behavioral states. In particular, brain irreversibility, which is a signature of non-equilibrium dynamics, captured by the arrow of time of the signal, revealed the hierarchical organization of the ferret's cortex. We found different signatures of irreversibility and functional hierarchy of large-scale dynamics in three different brain states (active awake, quiet awake, and deep sleep), showing a lower level of irreversibility in the deep sleep stage, compared to the other. Irreversibility also allowed us to disentangle the influence of different cortical areas and frequency bands in this process, showing a predominance of the parietal cortex and the theta band. Furthermore, when inspecting the embedded dynamic through a Hidden Markov Model, the deep sleep stage was revealed to have a lower switching rate and lower entropy production. These results suggest functional hierarchies in organization that can be revealed through thermodynamic features and information theory metrics.

Research on evaluation of legal risk prevention education quality based on dynamic variable weight analytic hierarchy process

Research on evaluation of legal risk prevention education quality based on dynamic variable weight analytic hierarchy process

Enhanced Practical Byzantine Fault Tolerance via Dynamic Hierarchy Management and Location-Based Clustering.

Blockchain is a distributed database technology that operates in a P2P network and is used in various domains. Depending on its structure, blockchain can be classified into types such as public and private. A consensus algorithm is essential in blockchain, and various consensus algorithms have been applied. In particular, a non-competitive consensus algorithm called PBFT is mainly used in private blockchains. However, there are limitations to scalability. This paper proposes an enhanced PBFT with dynamic hierarchy management and location-based clustering to overcome these problems. The proposed method clusters nodes based on location information and adjusts the dynamic hierarchy to optimize consensus latency. As a result of the experiment, the proposed PBFT showed significant performance improvement compared to the existing typical PBFT and Dynamic Layer Management PBFT (DLM-PBFT). The proposed PBFT method showed a processing performance improvement rate of approximately 107% to 128% compared to PBFT, and 11% to 99% compared to DLM-PBFT.

Dynamic ε-multilevel hierarchy constraint optimization with adaptive boundary constraint handling technology

Real-world optimization problems are often difficult to solve because of the complexity of the objective function and the large number of constraints that accompany it. To solve such problems, we propose Adaptive Dynamic ε-Multilevel Hierarchy Constraint Optimization (εMHCO). Firstly, we propose the dynamic constraint tolerance factor ε which can change dynamically with the feasible ratio and the number of iterations in the current population. This ensures a reasonable proportion of virtual feasible solutions in the population. Secondly, we propose adaptive boundary constraint handling technology (ABCHT). It can reshape the current individual position adaptively according to the size of constraint violation and increase the diversity of the population. Finally, we propose multi-level hierarchy optimization, whose multiple population structure is beneficial to solve real-world constraint optimization problems (COPs). To validate and analyze the performance of εMHCO, numerical experiments are conducted on the latest real-world test suite CEC’2020, which contains a set of 57 real-world COPs, and compared with four state-of-the-art algorithms. The results show that εMHCO is significantly superior to, or at least comparable to the state-of-the-art algorithms in solving real-world COPs. Meanwhile, the effectiveness and feasibility of εMHCO are verified on the real-world problem of the pipeline inner detector speed control.

Dynamic Adaptive Hierarchical N-Level Hierarchy Optimization for Electric Power System Problems

Many complex problems in the real world that contain a large number of constraints, especially circuit system problems. We design the Dynamic Adaptive Hierarchical N-Level Hierarchy Optimization (DAH-NLHO) algorithm based on the hierarchy affine of populations in nature. Its hierarchy levels can change dynamically and adaptively following the dimensionality of the problem and the number of constraints. Comparative tests were performed on top of the latest CEC2020 benchmark test set. The final experimental results demonstrate the effectiveness and feasibility of dynamic adaptive hierarchy levels.

Development of a virtual reality-based zygomatic implant surgery training system with global collision detection and optimized finite element method model

Background and objectiveZygomatic implant surgery is challenging due to the complex structure of the zygomatic bone, limited visual range during surgery, and lengthy implant path. Moreover, traditional training methods are costly, and experimental subjects are scarce. MethodsTo overcome these challenges, we propose a novel training system that integrates visual, haptic, and auditory feedback to create a more immersive surgical experience. The system uses dynamic bounding volume hierarchy (BVH) and Symplectic Euler to detect global collisions between surgical tools and models, while an optimized finite element method (FEM) model simulates soft tissue and detects collisions. Compared to previous works, our system achieves global rigid-body collisions between surgical tools and patient models, while also providing stable and realistic simulation and collisions of soft tissues. This advancement offers a more realistic simulation for zygomatic implant surgery. ResultsWe conducted three experiments and evaluations. The first experiment measured the axial force generated during the zygomatic implant simulation process and compared it with actual surgery, demonstrating the realistic force rendering feedback of our system. The second evaluation involved 15 novice surgeons who experienced the system and completed a questionnaire survey focusing on five aspects. The results showed satisfactory evaluations. The third experiment involved six surgeons who underwent in-depth training for two hours daily and were tested on the first, third, and fifth days. We collected data and combined it with the doctors' feedback to prove that our system can improve surgeons' proficiency in zygomatic implant surgery and provide a novel training solution for this procedure. ConclusionWe have innovatively integrated global collision detection and optimized soft tissue simulation into our system. Furthermore, we have conducted experimental validation to demonstrate the effectiveness of this implementation. We present a novel solution for zygomatic implant surgery training.

Assessing the Verbal Behavior of a Linguistically Diverse Speaker with Autism

AbstractFor speakers belonging to multiple verbal communities, functional analyses of verbal behavior allow for dynamic control over response topography. The simple practice of allowing the speaker the freedom to select the language of instruction minimizes cultural bias and hegemony. We extended the research on functional analyses of verbal behavior to include a speaker of multiple languages in a quasi-experimental case study. We employed verbal operant experimental (VOX) analyses as a repeated measure of language acquisition with a linguistically diverse, 7-year-old Indian boy with autism. The VOX analyses were conducted as part of the child’s early intensive behavioral intervention, and we observed the impact of an immersive foreign language experience on his verbal repertoire with follow-up VOX analyses conducted in three topographically distinct languages: English, Telugu, and Tamil. The results show a dynamic hierarchy of strength between the three languages, with overarching patterns across the three assessments. The implications for using VOX analyses to assess the functional language skills of multilingual speakers with autism are discussed, and areas of future research are highlighted.

The arrow of time of brain signals in cognition: Potential intriguing role of parts of the default mode network.

A promising idea in human cognitive neuroscience is that the default mode network (DMN) is responsible for coordinating the recruitment and scheduling of networks for computing and solving task-specific cognitive problems. This is supported by evidence showing that the physical and functional distance of DMN regions is maximally removed from sensorimotor regions containing environment-driven neural activity directly linked to perception and action, which would allow the DMN to orchestrate complex cognition from the top of the hierarchy. However, discovering the functional hierarchy of brain dynamics requires finding the best way to measure interactions between brain regions. In contrast to previous methods measuring the hierarchical flow of information using, for example, transfer entropy, here we used a thermodynamics-inspired, deep learning based Temporal Evolution NETwork (TENET) framework to assess the asymmetry in the flow of events, 'arrow of time', in human brain signals. This provides an alternative way of quantifying hierarchy, given that the arrow of time measures the directionality of information flow that leads to a breaking of the balance of the underlying hierarchy. In turn, the arrow of time is a measure of nonreversibility and thus nonequilibrium in brain dynamics. When applied to large-scale Human Connectome Project (HCP) neuroimaging data from close to a thousand participants, the TENET framework suggests that the DMN plays a significant role in orchestrating the hierarchy, that is, levels of nonreversibility, which changes between the resting state and when performing seven different cognitive tasks. Furthermore, this quantification of the hierarchy of the resting state is significantly different in health compared to neuropsychiatric disorders. Overall, the present thermodynamics-based machine-learning framework provides vital new insights into the fundamental tenets of brain dynamics for orchestrating the interactions between cognition and brain in complex environments.

Research on “native” and “non-native” English-speaking teachers: Past developments, current status, and future directions

AbstractRecently, we have been witnessing the emergence of scholarly interest and professional advocacy efforts centering on systemic, intersectional, fluid, and contextualized inequalities and dynamic hierarchies constructed by essentialized and idealized (non)native speakerhood (speakerism/speakering) and its personal and professional implications for English language teaching (ELT) profession(als). This critical literature review aims to portray, examine, and guide the existing scholarship focusing on a myriad of issues related to ELT professionals traditionally conceptualized as “native” and “non-native” English-speaking teachers. We come to a working conclusion that (non)native speaker/teacherhood is an epistemologically hegemonic, historically colonial, contextually enacted (perceived and/or ascribed), and dynamically experienced socio-professional phenomenon intersecting with other categories of identity (e.g., race, ethnicity, country of origin, gender, religion, sexuality/sexual orientation, social class, schooling, passport/visa status, and physical appearance, among others) in making a priori connections and assertions about individuals as language users and teachers and thereby forming discourses and practices of (in)equity, privilege, marginalization, and discrimination in ELT.

DASH: Dynamic Attention-Based Substructure Hierarchy for Partial Charge Assignment.

We present a robust and computationally efficient approach for assigning partial charges of atoms in molecules. The method is based on a hierarchical tree constructed from attention values extracted from a graph neural network (GNN), which was trained to predict atomic partial charges from accurate quantum-mechanical (QM) calculations. The resulting dynamic attention-based substructure hierarchy (DASH) approach provides fast assignment of partial charges with the same accuracy as the GNN itself, is software-independent, and can easily be integrated in existing parametrization pipelines, as shown for the Open force field (OpenFF). The implementation of the DASH workflow, the final DASH tree, and the training set are available as open source/open data from public repositories.