Structure Theory Research Articles

A REMARK ON FUJINO’S WORK ON THE CANONICAL BUNDLE FORMULA VIA PERIOD MAPS

Abstract Fujino gave a proof for the semi-ampleness of the moduli part in the canonical bundle formula in the case when the general fibers are K3 surfaces or abelian varieties. We show a similar statement when the general fibers are primitive symplectic varieties. This answers a question of Fujino raised in the same article. Moreover, using the structure theory of varieties with trivial first Chern class, we reduce the question of semi-ampleness in the case of families of K-trivial varieties to a question when the general fibers satisfy a slightly weaker Calabi–Yau condition.

A prediction method of thermal deformation of near-zero warping sandwich structure under non-uniform temperature field

The thermal deformation of structure in non-uniform thermal environment in space is a critical factor affecting the imaging accuracy of remote sensing satellite. In this study, the thermal deformation theory of near-zero warping sandwich structure under non-uniform temperature field is established. A high-fidelity prediction method based on CT scanning and reconstruction of structural defects is proposed. Thermal deformation experiments show that this method can accurately characterize the ultra-low thermal deformation behavior of the near-zero warping sandwich metastructure, which is significant for the real-time correction of satellite imaging in orbit.

Study on progressive collapse of overall structure based on numerical simulation method and prediction of structural collapse

This study investigated the structural progressive collapse using finite element (FE) and theoretical methods. Firstly, an FE model was established based on the component method and layered shell element method. The reliability of modeling approaches was evaluated by comparing them with the previous experimental tests by the author. The NIST building was taken as an example to investigate the structural collapse behavior and the failure modes after different component failure scenarios including single column, double columns, multiple columns, and horizontal components based on the alternative path method. The results indicated that the failure of gravity columns and joints more easily triggered the progressive collapse of the structure. After the failure of double moment-resisting frame columns, the structure still demonstrated considerable robustness. The impact of floor fragments played a controlling role in the process of collapse propagation. A method for achieving rapid collapse of structures was proposed based on the structural collapse characteristics. The study found that a demolition strategy based on gravity columns can facilitate the rapid collapse of structures. After comparing the different simplified modeling methods, it was revealed that the 2D models underestimated the anti-collapse performance of structures compared to the 3D models. Under the same scenarios, the predicted structural collapse response of the 2D model was several times greater than that of the 3D model (up to 7 times). Meanwhile, the structural collapse occurred earlier in the 2D model. The anti-collapse performance of structures adopting different design codes also was compared. It was revealed that GSA 2016 has put forward requirements for higher collapse resistance of buildings. In this study, a new calculation method was proposed, namely the Collapse Probability Prediction Method Based on the Randomness of Structural Load (CPPM-RSL). In the CPPM-RSL, load reliability theory was employed to ensure that the structural collapse resistance design fell within the realm of structural engineering theory. Meanwhile, the CPPM-RSL offered greater engineering interpretability compared to previous methods. Additionally, a new simplified mechanical model was proposed to predict the collapse resistance of structures. The new simplified model did not depend on the computational results obtained from the finite element method (FEM) unlike the existing model. At the same time, the simplified method also exhibited high reliability as revealed by comparisons with the FEM. To solve highly nonlinear problems, machine learning (ML) algorithms were established to predict the anti-collapse performance and the collapse probability of structures based on the proposed theoretical model. Meanwhile, the applicability and performance of the multiple ML algorithms in estimating the structural collapse behavior were compared and evaluated. The results indicated that the LightGBM model exhibited stronger robustness and predictive ability.

Altered brain network stability in OCD following rTMS intervention: Insights from structural balance theory

Repetitive Transcranial Magnetic Stimulation (rTMS) is a promising intervention for Obsessive-Compulsive Disorder (OCD). However, understanding brain network changes following rTMS remains limited, despite its potential to enhance treatment efficacy. In this retrospective study, we investigated brain network reorganization in OCD patients after rTMS, using structural balance theory as a framework. We hypothesized that rTMS-induced functional plasticity would alter brain network topology, particularly affecting triadic associations, and leading to increased balance energy levels, indicative of a less stable network state. Brain functional networks were constructed from resting-state EEGs of OCD patients, with phase lag indexes calculated both before and after rTMS treatment. These networks were analyzed by comparing global parameters, including positive and negative links, triadic interactions (balanced/unbalanced), hub formation tendencies, and balance energy levels. We observed a significant decrease in weak-balanced triads and an increase in strong-unbalanced triads within the Beta І frequency band (12–15 Hz). Additionally, there was a notable reduction in the tendency of negative links to form hubs across certain frequency bands. These changes led to an increase in the network's balanced energy level, pushing it toward a less stable state. We hope these findings will refine rTMS strategies by facilitating brain network reorganization.

Revolutionizing Knowledge Management in Transportation Agencies: The Role of Generative AI and Scalable Frameworks

The increased interconnectedness of the globe has led to a fast-paced expansion of transportation systems and, consequently, to the creation of effective Knowledge Management Strategies. By simply put, the ways of decision making, operations management, and solving problems related to the complex infrastructure systems have all improved. Knowledge management is made easier by the use of Generative Artificial Intelligence (AI) which enables all the processes of data generation, data synthesis and even gives room for a real-time insight within a transport department. There is also the availability of Structure and Generative Theory which allows agencies to cope with needs that keep changing without losing the quality and the speed of the flow of the knowledge. In this sense, the present contribution focuses on the perspective of generative AI and structural frameworks in transportation agencies understanding knowledge management and its future prospects in terms of technology usefulness (Chen & Li, 2021). In these external environments, transportation agencies manage vast and complex datasets that change rapidly and are often dispersed. For instance, Generative AI has applications in natural language processing, predictive analytics and intelligent reporting, which assist agencies in solving hurdles posed by conventional knowledge management. When these AI systems are given, scalable frameworks guarantee that the knowledge management systems will address the challenges that come with increased complexity in operations. This research examines how generative artificial intelligence can be applied in various knowledge management situations, with particular focus on how it can be used to deal with mundane processes, enhance responses and help to mitigate problems in the field of transportation (Nguyen et al., 2023). The results underscore the possible benefits of using generative AI within efficient KM systems, such as lower costs, better accuracy, and improved accessibility to the users. On the other hand, the concerns such as data quality, costs of implementation, and other factors like the presence of easy to use interface for non-technical personnel, are also brought up by the authors. These perspectives will be useful for transportation agencies that seek to transform their KM activities. Overcoming these obstacles, generative AI and scalable frameworks can promote radical transformation and development of dynamic and sustainable knowledge systems that meet the needs of transport management in the case of Rahman and Smith, 2023.

Representation of Existentialism in Jo Yong's Fairy Tales in the Drama It's Okay to Not Be Okay (A.J. Greimas Semiotic Analysis)

This research examines the fairy tale 진짜 진짜 얼굴을 찾아서 (Finding The Real Face) which is found in the Korean drama "It's Okay to Not Be Okay". This research analyzes the actant schema and functional structure from A.J.'s structural narrative theory. Greimas, after that, identified the representation of existentialism in it. The method used is qualitative with narrative semiotic analysis presented descriptively. The results of this research show that the fairy tale 진짜 진짜 얼굴을 찾아서 (Finding The Real Face) fulfills the six actants in varying amounts. The dominant actant of this tale is the Box Man, namely as subject, helper and recipient. The functional structure of a fairy tale has a sequential plot. Apart from that, this fairy tale reflects the search for identity of the characters, namely the Box Man, the Mask Child, and the Tin Can Princess.

Determinants of depression and anxiety in informal waste pickers: a cross-sectional study of informal waste pickers in Hong Kong

BackgroundThere are growing global concerns about informal waste pickers and their health issues. This cross-sectional study drew on the structural violence theory to examine the mental health situation of informal waste pickers in Hong Kong and identified the determinants of depression and anxiety in them ranging from individual to societal and governmental levels.MethodThe data from the largest territory-wide study of informal waste pickers in 2023 was analysed. Descriptive statistics and logistic regressions were used to investigate the association between mental health and socioeconomic variables, including demographic background, governmental measures against informal waste pickers, other negative experiences related to safety and public discrimination, and supportive resources. The symptoms of depression and anxiety were assessed using the Patient Health Questionnaire (PHQ) and General Anxiety Disorder (GAD).ResultsThe results showed that physical strain/illness and chronic illness caused by long-term scavenging works (for depression adjusted OR 3.33, 95% CI [1.75, 6.31]; for anxiety adjusted OR 5.01, 95% CI [2.45, 10.24]), recycling or personal property stolen (for depression adjusted OR 2.04, 95% CI [1.23, 3.36]; for anxiety adjusted OR 2.72, 95% CI [1.62, 4.56]), being treated rudely by citizens (for depression adjusted OR 2.16, 95% CI [1.32, 3.55]); for anxiety (adjusted OR 2.85, 95% CI [1.74, 4.67]) are the critical risk factors of informal waste pickers’ mental health. While higher intention to continue scavenging work if financial conditions permit is the critical protective factor for depression (adjusted OR 0.46, 95% CI [0.24, 0.88]) and anxiety (adjusted OR 0.43, 95% CI [0.22, 0.86]), compared to lower intention to continue scavenging work if financial conditions permit in the multivariate model.ConclusionsThis study discloses the close relationship between informal waste pickers’ long-term physical strain/illness and mental health and identifies the governmental measures, other negative experiences related to safety issues and public discrimination as risk factors for informal waste pickers’ mental health. Providing outreach, tailored medical services, increasing the unit price of recycling, developing specific recycling zones and establishing membership-based organisations help alleviate the challenges of precarious employment and enhance the well-being of the informal waste pickers.

A detailed analysis of the key steps of the cyclopentene autoignition mechanism from calculated RRKM rate constants associated with ignition delay time simulations

Cyclopentene, a prototype for studying the combustion chemistry of cyclic olefins, appears in the oxidation of cyclic hydrocarbons and can provide key information in the understanding of the formation of polycyclic aromatic hydrocarbons. The ▪ addition to the double-bond is one of the main steps in low-temperature oxidation mechanisms of unsaturated organic compounds. In the case of cyclopentene, addition of ▪ yields a hydroxycyclopentyl radical, that can further react with O2. In this work, we studied the potential energy surface and reaction rates for the subsequent reactions of O2 with the hydroxycyclopentyl radical. The temperature and pressure dependence of the rate constants were determined using master equation simulations, with microcanonical rate coefficients calculated by RRKM theory. The potential energy surface was extracted from high-level electronic structure theory, based on geometries and frequencies obtained using density functional theory. Our results indicate that a Waddington-type mechanism, which produces glutaraldehyde and regenerates ▪ , is the dominant reaction pathway. However, at low-temperatures, a secondary pathway leading to the formation of epoxycyclopentanol and ▪ becomes equally significant. The thermochemistry of all ▪ radicals involved were also evaluated. The kinetic and thermodynamic data were incorporated into a comprehensive mechanism of cyclopentene autoignition, in order to simulate the associated ignition delays. The updated reaction mechanism resulted in shorter ignition delays compared to the non-updated mechanism. Sensitivity analysis was performed to identify the primary contributors.Novelty and Significance StatementCyclopentene is an important intermediate in the oxidation of cyclic olefins and serves as a precursor in the formation of polycyclic aromatic hydrocarbons. Kinetic modeling studies require detailed information on elementary reactions, much of which is typically unavailable from experiments. The novelty and significance of this study lie in the theoretical calculations of rate constants for key reactions in the oxidation of cyclopentene and their evaluation within the comprehensive mechanism proposed by Lokachari et al. The results demonstrate that the studied reactions significantly influence the ignition delays of cyclopentene at low temperatures. Furthermore, the data presented here can be applied in future studies focusing on the oxidation of cyclic olefins.

A reliability-based framework for comparing ultrasonic flowmeter calibration methods

Ultrasonic flowmeters stand as sophisticated instruments employed for the precise measurement of fluid flow, utilizing cutting-edge time-of-flight technique. However, the optimal functioning of these devices is susceptible to the influence of various factors, including fluid properties and installation conditions, thereby introducing uncertainties into the measurement process. This research introduces a significant method based in structural reliability theory for comparing experimental conditions of ultrasonic flowmeters. This approach strives to offer a thorough comprehension of the factors that affect measurement reliability, actively contributing to the continuous endeavors aimed at fine-tuning the accuracy of ultrasonic flowmeters. To validate the effectiveness of the proposed method, it is applied and tested using data derived from measurements of an ultrasonic liquid flowmeter conducted by INMETRO. These results are subsequently compared against the metrological requirements outlined in OIML R 117. The results not only affirm the applicability of the proposed approach but also demonstrate its effectiveness in handling the uncertainties linked to fluid flow measurements. By enhancing the reliability of these devices, the proposed approach paves the way for advancements in the field, fostering greater precision and confidence in fluid flow measurements for various industrial applications.

Human Facts, Collective Subjects and Author's Worldview in Pendekar Cengeng Silat Story by Kho Ping Hoo

This research explains the relations and implications of Kho Ping Hoo's discrimination experiences as a Peranakan Tionghoa group to the emergence of the Pendekar Cengeng silat story . Data were collected using reading and note-taking techniques. Data were analyzed using Lucien Goldmann's genetic structuralism theory which focuses on human facts, collective subjects, and the author's worldview. The research results show that human facts include various activities carried out by warrior figures in an effort to oppose the oppression of the colonial government. The collective subject shows a group of warriors who work together and are united in one group to carry out the warrior's mission collectively. The author's worldview are about politics, religiosity and life philosophy.

Multi-Level High Entropy-Dissipative Structure Enables Efficient Self-Decoupling of Triple Signals.

The theory of high entropy-dissipative structure is confined to high-entropy alloys and their oxide materials under harsh conditions, but it is very difficult to obtain high entropy-dissipative structure for smart sensors based on polymers and metal oxides under mild conditions. Moreover, multiple signal coupling effect heavily hinder the sensor applications, and current multimodal integrated devices can solve two signal-decoupling, but need very complicated process way. In this work, new synthesis concept is the first time to fabricate high entropy-dissipative conductive layer of smart sensors with triple-signal response and self-decoupling ability within poly-pyrrole/zinc oxide (PPy/ZnO) system. The sensor (SPZ20) amplifies pressure (17.54%/kPa) and gas (0.37%/ppm), reduces humidity (0.41%/% RH) and temperature (0.12%/°C) signals, simultaneously achieving the triple self-decoupling effect of pressure and gas in the complex temperature-humidity field because of the enlarged pressure-contact area, enhanced gas-responsive sites, altered vapor path and its own heat insulation function. Additionally, it inherits the strong robustness (500 rubbing, washing, and heating or freezing cycles) and endurance (10000 photo-purification cycles) of traditional high-entropy materials for information transmission and smart alarms in emergencies or harsh environments. This work gives a new insight into the multiple-signal response and smart flexible electronic design from natural fibers.

Physical properties and thermal stability of zirconium platinum nitride thin films

Ternary transition metal nitrides (TMNs) promise to significantly expand the material design space by opening new functionality and enhancing existing properties. However, most systems have only been investigated computationally, and limited understanding of their stabilizing mechanisms restricts translation to experimental synthesis. To better elucidate key factors in designing ternary TMNs, we experimentally fabricate and analyze the physical properties of the ternary Zr–Pt–N system. Structural analysis and density functional theory modeling demonstrate that Pt substitutes nitrogen on the nonmetallic sublattice, which destabilizes the rock salt structure and forms a complex cubic phase. We also show insolubility of Pt in the Zr–Pt–N at 45 at. % with the formation of a secondary Pt-rich phase. The measured reduced plasma frequency, decrease in resistivity, and decrease in hardness reflect a dominance of metallic behavior in bonding. Additionally, we observe the exsolution of Pt nano precipitates from the Zr–Pt–N films upon annealing as well as degradation in the nitridic film's thermal stability. Even at low concentrations (1%), Pt facilitates a solid reaction with the Si substrate that is otherwise inaccessible in ZrN films.

Megamall development in Tehran city: a study of the development process from the perspective of structuration theory

ABSTRACT Tehran, Iran's capital, has witnessed a boom in megamall development, resulting in significant social and environmental challenges. This trend contradicts the anti-consumerism values of Islamic ideology, particularly during a period of severe economic sanctions. Despite these contradictions, developers continue to invest in megamalls, supported by the municipality and popular among citizens. This paper, through the lens of structuration theory, seeks to explore the conditions providing the development of megamalls in Tehran. The constructivist grounded theory approach is adopted, and the data are collected through 61 semi-structured interviews. The findings highlight the dialectical relationship between agents and structures in this process. It reveals that dysfunctional structures, including regulations on capital and urban development (rules), spatial resources and power distribution (resources), as well as identity and lack of critical discourses in society (ideology), have provided conditions for urban agents to be involved in this process, which, in turn, reinforces structures.

A hybrid opinion dynamics model with leaders and followers fusing dynamic social networks in large-scale group decision-making

Objectives:In this study, our goal is to enhance consensus efficiency in complex decision-making scenarios by constructing a large-scale group decision-making (LSGDM) method that integrates dynamic social network (DSN) and opinion dynamics. To this end, we design a model that can effectively cluster experts and dynamically adjust the network structure to more accurately reflect the diversity and complexity of the actual decision-making process. Methods:Specifically, we first design an improved Louvain algorithm based on social influence to effectively cluster participants with similar opinions into the same community. Then, we utilize structural hole theory to distinguish opinion leaders and followers in the community, and construct a DSN updating mechanism based on opinion disagreement and trust relationship. Finally, we combine the advantages of the DeGroot and Hegselmann–Krause (HK) models and propose a hybrid opinion dynamics (HOD) model in the LSGDM framework, referred to as DSN-HOD-LSGDM. Findings:Experimental results demonstrate that the DSN-HOD-LSGDM model significantly enhances consensus-building efficiency across diverse decision-making communities. The model effectively tracks opinion evolution in complex networks, outperforming conventional methods in both adaptability and scalability. Novelty:In this study, we propose an improved Louvain algorithm and dynamic weight allocation mechanism based on influence index, and design a personalized opinion evolution mechanism combined with structural hole theory. By fusing opinion evolution and dynamic trust, we construct a new LSGDM consensus model that realizes the dynamic adjustment of the trust relationship between individuals.

Non-adiabatic Couplings in Surface Hopping with Tight Binding Density Functional Theory: The Case of Molecular Motors.

Nonadiabatic molecular dynamics (NAMD) has become an essential computational technique for studying the photophysical relaxation of molecular systems after light absorption. These phenomena require approximations that go beyond the Born-Oppenheimer approximation, and the accuracy of the results heavily depends on the electronic structure theory employed. Sophisticated electronic methods, however, make these techniques computationally expensive, even for medium size systems. Consequently, simulations are often performed on simplified models to interpret the experimental results. In this context, a variety of techniques have been developed to perform NAMD using approximate methods, particularly density functional tight binding (DFTB). Despite the use of these techniques on large systems, where ab initio methods are computationally prohibitive, a comprehensive validation has been lacking. In this work, we present a new implementation of trajectory surface hopping combined with DFTB, utilizing nonadiabatic coupling vectors. We selected the methaniminium cation and furan systems for validation, providing an exhaustive comparison with the higher-level electronic structure methods. As a case study, we simulated a system from the class of molecular motors, which has been extensively studied experimentally but remains challenging to simulate with ab initio methods due to its inherent complexity. Our approach effectively captures the key photophysical mechanism of dihedral rotation after the absorption of light. Additionally, we successfully reproduced the transition from the bright to dark states observed in the time-dependent fluorescence experiments, providing valuable insights into this critical part of the photophysical behavior in molecular motors.

Lie Pseudogroups à la Cartan

We present a modern formulation of Élie Cartan’s structure theory for Lie pseudogroups and prove a reduction theorem that clarifies the role of Cartan’s systatic system. The monograph is divided into three parts. In part one, using notions coming from the theory of Lie groupoids and algebroids, we introduce the framework of Cartan algebroids and realizations, structures that encode Cartan’s structure equations and notion of a pseudogroup in normal form. In part two, we present a novel proof of Cartan’s Second Fundamental Theorem which states that any Lie pseudogroup is equivalent to a pseudogroup in normal form. In part three, we prove a new reduction theorem that states that, under suitable regularity conditions, a pseudogroup in normal form canonically reduces to a generalized pseudogroup of local solutions of a Lie-Pfaffian groupoid.

Creating Benchmarks for Lithium Clusters and Using Them for Testing and Validation.

Metal clusters often have a variety of possible structures, and they are calculated by a wide range of methods; however, fully converged benchmarks on the energy differences of structures and spin states that could be used to test or validate these methods are rare or nonexistent. Small lithium clusters are good candidates for such benchmarks to test different methods against well-converged relative energetics for qualitatively different structures because they have a small number of electrons. The present study provides fully converged benchmarks for Li4 and Li5 clusters and uses them to test a diverse group of approximation methods. To create a dataset of well-converged single-point energies for Li4 and Li5, stationary structures were optimized by Kohn-Sham density functional theory (KS-DFT) and then single-point energy calculations at these structures were carried out by two quite different beyond-CCSD(T) methods. To test other methods single-point energy calculations at these structures were carried out by KS-DFT, Mo̷ller-Plesset (MP) theory, coupled cluster (CC) theory, five composite methods (Gaussian-4, the Wuhan-Minnesota (WM) composite method, and the W2X, W3X, and W3X-L composite methods of Radom and co-workers), multiconfiguration pair-density functional theory (MC-PDFT), complete active space second-order perturbation theory (CASPT2), and n-electron valence state second-order perturbation theory (NEVPT2). Our results show that rhomboid and trigonal bipyramid (TBP) geometries are the most stable structures for Li4 and Li5, respectively. Using the W3X-L method to obtain our best estimates, the mean unsigned deviations were calculated for other methods for several structures and spin states of both Li4 and Li5. Binding energies and M diagnostics were calculated for all structures. The data in this paper are valuable for assessing the reliability of current electronic structure theories and also developing new density functionals and machine learned models.

The Syntactic Profile of Nepali Ergativity

Ergative case-marking in Nepali is syntactically shallow even compared to other Indo-Aryan languages. The structure of ergative case-marking in Nepali is examined in the context of structural, inherent, and dependent theories of case assignment, and in particular Legate’s typology of Inherent case and the Marantz Case Generalization, and it is determined that a dependent case theory best describes the Nepali pattern.

A Comprehensive Evaluation Method for Assessing the Seismic Resilience of Building Sites Based on the TOPSIS Model and the Entropy Method

The seismic design philosophy based on resilience represents the latest development in structural seismic design theory and is currently the most advanced concept in the field of seismic design research. A building site serves as the foundation and environment for structures, and evaluating its seismic resilience is a crucial aspect of designing and assessing buildings’ seismic resilience. To meet the needs of evaluating building seismic resilience, the concept of seismic resilience for building sites is introduced in this paper. This evaluation is approached from three dimensions: seismic action, site seismic capacity, and site recovery capability. An indicator system for evaluating the seismic resilience of building sites is constructed using 23 key indicators that reflect the site’s seismic resilience. A seismic resilience evaluation model for building sites is established based on the TOPSIS model and the entropy method. The feasibility and rationality of the proposed evaluation method are demonstrated through application examples. The research findings in this paper provide a valuable reference for advancing the evaluation of both building seismic resilience and site seismic resilience.

A minimum cost and maximum fairness-driven multi-objective optimization consensus model for large-scale group decision-making

The rise of social media and e-democracy has driven a paradigm shift in decision-making, notably reflected in the changing ways of public participation and policymaking within decision-making processes. The increased focus on fairness and efficiency not only complicates the consensus-building process among diverse interests and perspectives, but also significantly adds to the complexity of decision-making and its implementation. In this context, balancing the interests of all parties while ensuring fairness and improving decision-making effectiveness becomes crucial. To address these challenges, this study develops a multi-objective optimization consensus framework for large-scale group decision-making (LSGDM) that integrates the interests of decision-makers (DMs) and a moderator, providing a more comprehensive tool. Specifically, this study first designs a DM weight determination method based on structural hole theory within fuzzy social networks. The proposed DM weight determination method effectively leverages the flexibility of fuzzy social networks and the comprehensiveness of structural hole theory to enhance the accuracy and reliability of weight assignment. Building on this, a novel clustering method based on the maximum group consensus level is developed, taking into account the varying importance of different DMs. Furthermore, a minimum cost and maximum fairness-driven multi-objective optimization LSGDM consensus model, referred to as MCMF-MO-LSGDM, is explored in this study. Finally, the utility and superiority of the constructed model are confirmed through comparative analysis and simulation experiments against existing related works.