Reasonable Model Research Articles

Formation of dual quasi-continuous networked structure and its strengthening effect in Ti-6Al-4V alloy reinforced with graphene via powder bed fusion

A dual-networked distribution of the reinforcements holds promise for achieving a balance between high-strength and moderate plasticity in titanium matrix composites (TMCs). Unfortunately, achieving this improvement in TMCs via additive manufacturing (AM) methods, presents significant challenges. Those challenges arise from the inherent differences in chemical and physical properties, which often led to agglomeration of the reinforcement and cracks caused by the high thermal residual stresses. To overcome those issues, this study focuses on the development of Ti-6Al-4V (Ti64) alloys incorporated with 0.5 wt% graphene nanosheets (GNSs) constructing a dual quasi-continued TiC network structure via powder bed fusion. The results exhibited a super-high tensile yield strength (1307 MPa), accompanied by a moderated elongation of 2.6% with reduced residual stress. The microstructure, phase contents, and mechanical performance were thoroughly investigated. A thermo-metallurgical-mechanical coupling model was developed, considering factors such as laser absorption effects and laser scanning strategy. Finally, a reasonable dual-network model was built to elucidate the contribution of various strengthening factors. Overall, this study illustrates that the strength of GNS/Ti64 composites is affected by the factors of GNS distribution, quasi-continue network in-situ TiC particles, temperature and residual stress field, offering a reference for fabricating high-strength nanocarbon/Ti64 composites by AM methods.

Microanalysis of behavior characteristics for asphalt binders on different mineral surfaces based on MD and SEM

To investigate the influence of different minerals on the behavior characteristics of asphalt binders at micro scale, molecular dynamics (MD) and scanning electron microscopy (SEM) were combined to study the interaction of asphalt binders and different mineral surfaces. Firstly, commonly used aggregates were analyzed by X-ray diffraction (XRD), and the main mineral categories were obtained, including quartz, albite, biotite and calcite. Secondly, the asphalt-mineral micro models were established by combining the reasonable asphalt micro model and mineral micro model, and the interface behavior characteristics were analyzed by MD simulation. Finally, the interface structure of asphalt and minerals was observed through SEM testing, which intuitively revealed the behavior characteristics of asphalt on different mineral surfaces. The results show that the relative concentration distribution of asphalt binders at the asphalt-mineral interface is relatively high, and it can be affected by mineral category, surface structure state and asphalt type. The transition zone of biotite, albite and asphalt interface is relatively wide, which means that the influence of biotite, albite and asphalt diffusion characteristics is relatively obvious, and the interface interaction ability is relatively strong. The study reveals the behavior characteristics of asphalt binders on different mineral surfaces, and provides a research basis for exploring the structural stability of asphalt-aggregate interface.

Informing the clinical reasoning of advanced practice students with nursing theories

This article examines the integration of a Clinical Nurse Reasoning (CNR) model into advanced nursing practice training to develop the clinical reasoning skills of advanced practice nurses. The article explores the contribution of Callista Roy's conceptual model of adaptation and presents a global analysis of a complex care situation that justifies the introduction of an CNR model paired with nursing knowledge to ensure comprehensive, quality nursing care.

Mechanical properties of concrete filled steel tube members constrained by carbon fiber reinforced materials under bidirectional coupled loading

Carbon fiber reinforced composite structures have been a research hotspot in recent years, with 9 specimens designed for static tests under bending and torsion loads of CFRP concrete filled steel tubes. The torque angle (T-θ) curve was studied from an experimental perspective. Subsequently, a reasonable finite element model was established using ABAQUS software. In addition, the effects of changes in parameters such as the number of steel concrete strength, bending ratio, and steel ratio on C–CF–CFRP-ST bending and torsion specimens were studied through numerical parameter research. Finally, equations of bearing capacity of CFRP-concrete filled steel tube under coupled bending and torsion are proposed.

Teaching Antimicrobial Decision-Making in Medical Education: A Qualitative Study

Background: Inappropriate use of antimicrobials contributes to the growing threat of antibiotic resistance. While physicians encounter infections in virtually every facet of medical practice, research has shown that physicians have difficulty determining the need for antimicrobials and choosing the right drug. Physicians’ difficulties with antimicrobial prescribing likely begin early in medical education, yet little is known about how medical students learn to make antimicrobial choices. Our study sought to better understand how medical students learn antimicrobial decision-making, including the impact of a new learning tool introduced in the Infectious Diseases (ID) and Microbiology preclinical course. Method: From 2021-2023, we conducted 18 individual interviews with a purposive sample of medical students at the University of Michigan who had taken the preclinical ID/Microbiology course during the 2019-2021 curricular years. We asked participants how they learned to make antimicrobial decisions and how the course and clinical rotations influenced their understanding of antimicrobial choice. The six participants who took the 2021 course were additionally asked how an antimicrobial decision-making tool introduced that year impacted that process (Figure 1). The tool was adapted from prior work on antimicrobial reasoning (Abdoler et al, 2020). Participants were asked whether they remembered being introduced to the tool (approximately 18 months prior) and if they utilized it during their clinical rotations. Results were analyzed using Dedoose Software to facilitate thematic analysis. Result: Several themes emerged on analysis. Nearly all participants reflected that they learned elements of antimicrobial decision-making during clinical rotations, through observation or direct interaction with physician mentors and patients. Several participants described the preclinical period as content learning, with clinical rotations providing a space to consolidate and scaffold knowledge, as well as transfer knowledge to new situations or tasks. Of the 6 students interviewed regarding the antimicrobial decision-making tool, only one remembered it and could accurately describe its components prior to being shown the tool during the interview. Conclusion: Results suggest that participants view the preclinical ID/Microbiology course primarily as an opportunity to learn content, and perceive learning antimicrobial decision-making directly from practicing physicians in the clinical portion of medical school. An antimicrobial decision-making tool introduced during the preclinical ID/Microbiology course in 2021 did not impact students’ conceptualization of how they learned this skill. Given that practicing physicians often make antimicrobial prescribing errors, regular re-introduction of the tool during clinical rotations may help bridge preclinical antimicrobial educational content to the clinical phase of learning, counteract inappropriate antimicrobial lessons encountered clinically, and ground students' burgeoning antimicrobial prescribing skills in a logical reasoning model.

Impact of the Reference Multiple-Time-Point Dosimetry Protocol on the Validity of Single-Time-Point Dosimetry for [177Lu]Lu-PSMA-I&T Therapy.

Internal dosimetry supports safe and effective patient management during radionuclide therapy. Yet, it is associated with high clinical workload, costs, and patient burden, as patient scans at multiple time points (MTPs) must be acquired. Dosimetry based on imaging at a single time point (STP) has continuously gained popularity. However, MTP protocols, used as a reference to judge the validity of STP dosimetry, differ depending on local requirements and deviate from the unknown patient-specific ground truth pharmacokinetics. The aim of this study was to compare the error and optimum time point for different STP approaches using different reference MTP protocols. Methods: Whole-body SPECT/CT scans of 7 patients (7.4-8.9 GBq of [177Lu]Lu-PSMA-I&T) were scheduled at 24, 48, 72, and 168 h after injection. Sixty lesions, 14 kidneys, and 10 submandibular glands were delineated in the SPECT/CT data. Two curve models, that is, a mono- and a biexponential model, were fitted to the MTP data, in accordance with goodness-of-fit analysis (coefficients of variation, sum of squared errors). Three population-based STP approaches were compared: one method published by Hänscheid etal., one by Jackson etal., and one using population-based effective half-lives in the mono- or biexponential curve models. Percentage differences between STP and MTP dosimetry were evaluated. Results: Goodness-of-fit parameters show that a monoexponential function and a biexponential function with shared population-based parameters and physical tail are reasonable reference models. When comparing both reference models, we observed maximum differences of -44%, -19%, and -28% in the estimated absorbed doses for lesions, kidneys, and salivary glands, respectively. STP dosimetry with an average deviation of less than 10% from MTP dosimetry may be feasible; however, this deviation and the optimum imaging time point showed a dependence on the chosen reference protocol. Conclusion: STP dosimetry for [177Lu]Lu-PSMA therapy is promising to boost the integration of dosimetry into clinical routine. According to our patient cohort, 48 h after injection may be regarded as a compromise for STP dosimetry for lesions and at-risk organs. The results from this analysis show that a common gold standard for dosimetry is desirable to allow for reliable and comparable STP dosimetry.

Short-term stochastic multi-objective optimization scheduling of wind-solar-hydro hybrid system considering source-load uncertainties

Uncertainties in energy outputs (source side) and load side are simultaneously present and cannot be ignored in the actual operation of multi-energy systems. Adopting a reasonable uncertainty modeling method and understanding the impact of source-load uncertainties on optimization scheduling are key to formulating accurate and effective multi-energy scheduling plans. To address the uncertainties on both the source side and the load side in wind-solar-hydro hybrid systems, this paper proposes a multi-objective optimization scheduling model based on stochastic programming theory. The model aims to maximize the net profit of the system's power generation and minimize the fluctuation of the remaining load. It employs Vine-Copula coupled with Monte Carlo simulation and the deep learning method TimeGAN to generate joint wind and solar power output and load scenario sets. The generated source-load uncertainty scenarios are then reduced to representative scenarios using the K-Means clustering method, which are used as inputs for the scheduling model. The proposed model is applied to a wind-solar-hydro energy base in China, and the results show that: 1) The Vine-Copula-based source-side scenario generation method can quantitatively consider the correlations among meteorological factors. The relative errors of the generated scenarios' statistics compared to the original data are all less than 5%, and the relative errors of the correlation coefficients are less than 10%. 2) The TimeGAN-based load-side scenario generation method avoids the presupposition of the load probability distribution. Compared to the original data, the generated scenarios have R2 and Pearson correlation coefficients of 0.77 and 0.87, respectively. Additionally, TimeGAN shows significant advantages over traditional random sampling methods in simulating extreme scenarios. 3) Both source-side and load-side uncertainties significantly impact the optimization scheduling results of multi-energy systems, leading to increased fluctuation of the remaining load and decreased net profit. 4) The combined source-load uncertainties have a synergistic negative impact on the multi-objective optimization scheduling results. 5) The Pareto front of the optimization results is a concave function with low marginal benefits. Decision-makers should adopt a compromise solution as a guide for the operation of multi-energy systems.

Educational models for cognition: Methodology of modeling intellectual skills for intelligent tutoring systems

Automation of teaching people new skills requires modeling of human reasoning because human cognition involves active reasoning over the new subject domain to acquire skills that will later become automatic. The article presents Thought Process Trees — a language for modeling human reasoning that was created to facilitate the development of intelligent tutoring systems, which can perform the same reasoning that is expected of a student and find deficiencies in their line of thinking, providing explanatory messages and allowing them to learn from performance errors. The methodology of building trees which better reflect human learning is discussed, with examples of design choices during the modeling process and their consequences. The characteristics of educational modeling that impact building subject-domain models for intelligent tutoring systems are discussed. The trees were formalized and served as a basis for developing a framework for constructing intelligent tutoring systems. This significantly lowered the time required to build and debug a constraint-based subject-domain model. The framework has already been used to develop five intelligent tutoring systems and their prototypes and is being used to develop more of them.

MDTGAN: Multi domain generative adversarial transfer learning network for traffic data imputation

Accurate, real-time, and efficient traffic data, crucial for intelligent transportation systems, which is often disrupted in the real world due to the influence of weather, interference, and equipment failures. Generative Adversarial Networks (GANs), have achieved significant results in image restoration, providing insights for traffic data imputation. Recent research has focused on developing more effective and reasonable model by learning transferable common knowledge from different cities or different scenarios, which is also an excellent idea for improving the data imputation performance. In light of, we contrive to design a GANs based transferable traffic data imputation model, namely Multi Domain Generative Adversarial Transfer Learning Network (MDTGAN). Firstly, the model consists of two stages. In pre-training stage, the model utilizes the source domain datasets (Similar cities road network datasets) to update parameters and transferred them. Then, the model parameters are optimized using the target domain dataset (Target city road network dataset) in fine-tuning stage to avoid the issue of insufficient samples caused by data missing. Secondly, to adapt the model to different dataset topologies, MDTGAN models the node-level data by taking node time series as input, enabling the model to autonomously learn the prevalent spatiotemporal correlations inherent in nodes. Additionally, we introduce a domain discriminator module that guides the spatial encoder in learning domain-invariant features of network node spatial information, enhancing the model generalization ability. The experiments conducted on three publicly available datasets, in which one dataset is regarded as the target dataset, while the others serve as source datasets. The experimental results demonstrate that the MDTGAN model consistently outperforms the baseline models. Specifically, the MDTGAN model can transfer valuable knowledge from the source domain datasets to improve data imputation performance on the target domain dataset, providing practical significance for cities lacking historical traffic data.

Collaborative Problem-Solving in Knowledge-Rich Domains: A Multi-Study Structural Equation Model

Collaborative skills are crucial in knowledge-rich domains, such as medical diagnosing. The Collaborative Diagnostic Reasoning (CDR) model emphasizes the importance of high-quality collaborative diagnostic activities (CDAs; e.g., evidence elicitation and sharing), influenced by content and collaboration knowledge as well as more general social skills, to achieve accurate, justified, and efficient diagnostic outcomes (Radkowitsch et al., 2022). However, it has not yet been empirically tested, and the relationships between individual characteristics, CDAs, and diagnostic outcomes remain largely unexplored. The aim of this study was to test the CDR model by analyzing data from three studies in a simulation-based environment and to better understand the construct and the processes involved (N = 504 intermediate medical students) using a structural equation model including indirect effects. We found various stable relationships between individual characteristics and CDAs, and between CDAs and diagnostic outcome, highlighting the multidimensional nature of CDR. While both content and collaboration knowledge were important for CDAs, none of the individual characteristics directly related to diagnostic outcome. The study suggests that CDAs are important factors in achieving successful diagnoses in collaborative contexts, particularly in simulation-based settings. CDAs are influenced by content and collaboration knowledge, highlighting the importance of understanding collaboration partners’ knowledge. We propose revising the CDR model by assigning higher priority to collaboration knowledge compared with social skills, and dividing the CDAs into information elicitation and sharing, with sharing being more transactive. Training should focus on the development of CDAs to improve CDR skills.

Relationship Between Steelhead Parr Densities Observed During Snorkel Surveys and Emigration Estimates From Rotary Screw Traps

Abstract Snorkel surveys are widely used for monitoring fish populations because they are a versatile and cost-effective technique. However, snorkel surveys provide instantaneous measurements that might not describe continuous processes, such as the emigration of juvenile anadromous salmonids. A model capable of assessing the level of potential production of emigrating juvenile steelhead (anadromous Oncorhynchus mykiss) from watersheds in Idaho on the basis of snorkel surveys would be useful. Our objective was to determine the relationship between abundance of emigrants estimated from rotary screw traps (RSTs) and parr densities from snorkel surveys. We analyzed over 1,000 snorkel surveys and 70 year–watershed combinations of emigrant estimates. Density was positively related to emigrant abundance. The most reasonable model included a common slope among watersheds, a common year effect, and varying intercepts, supporting a hypothesis that some watersheds produce more emigrating juveniles at similar observed densities. This model explained 82.7% of the variation observed in emigrant abundance. Uncertainty was greater among watersheds than within a watershed. The model can be used to translate mean densities observed in snorkel surveys to abundance of steelhead emigrants and thus to infer the effects of a habitat restoration program or other management actions. This work is important as a step toward understanding juvenile emigrant abundance in watersheds without RSTs and to aid understanding of steelhead emigrant productivity in Idaho watersheds.

Inductive reasoning with type-constrained encoding for emerging entities

Knowledge graph reasoning, vital for addressing incompleteness and supporting applications, faces challenges with the continuous growth of graphs. To address this challenge, several inductive reasoning models for encoding emerging entities have been proposed. However, they do not consider the multi-batch emergence scenario, where new entities and new facts are usually added to knowledge graphs (KGs) in multiple batches in the order of their emergence. To simulate the continuous growth of knowledge graphs, a novel multi-batch emergence (MBE) scenario has recently been proposed. We propose a path-based inductive model to handle multi-batch entity growth, enhancing entity encoding with type information. Specifically, we observe a noteworthy pattern in which entity types at the head and tail of the same relation exhibit relative regularity. To utilize this regularity, we introduce a pair of learnable parameters for each relation, representing entity type features linked to the relation. The type features are dedicated to encoding and updating the features of entities. Meanwhile, our model incorporates a novel attention mechanism, combining statistical co-occurrence and semantic similarity of relations effectively for contextual information capture. After generating embeddings, we employ reinforcement learning for path reasoning. To reduce sparsity and expand the action space, our model generates soft candidate facts by grounding a set of soft path rules. Meanwhile, we incorporate the confidence scores of these facts in the action space to facilitate the agent to better distinguish between original facts and rule-generated soft facts. Performances on three multi-batch entity growth datasets demonstrate robust performance, consistently outperforming state-of-the-art models.

Simulation of rice grain breakage process based on Tavares UFRJ model

Understanding the breakage characteristics of rice grains is an important means to reduce rice breakage rate. However, the dynamic breakage mechanism of rice grain is unclear due to the lack of a reasonable breakage model. In this study, the uniaxial compression test and drop weight test of single rice were carried out, the breakage model of rice grain was constructed, the reliability of rice model was verified by the experiment and simulation results. The results showed that the fracture energy distribution of rice can be obtained by uniaxial compression test, the specific fracture energy of rice accords with a lognormal distribution, and the median specific fracture energy of rice is 479.75 J/kg. The damage accumulation coefficient and fragment size distribution of rice can be acquired by drop test, the result of damage accumulation coefficient of rice was 4.3. Rice grain breakage mainly occurs in the milling section of the vertical circulation rice mill.

Finite–Discrete Element Method Simulation Study on Development of Water-Conducting Fractures in Fault-Bearing Roof under Repeated Mining of Extra-Thick Coal Seams

The formation of water-conducting fractures in overlying strata caused by underground coal mining not only leads to roof water inrush disasters, but also water-conducting fractures penetrate the aquifer, resulting in the occurrence of a mine-water-inrush disaster and the loss of water resources. It destroys the sustainability of surface water and underground aquifers. This phenomenon is particularly significant in extra-thick coal seams and fault-bearing areas. Numerical simulation is an effective method to predict the failure range of mining overburden rock with low cost and high efficiency. The key to its accuracy lies in a reasonable constitutive model and simulation program. In this study, considering that the three parts of penetrating cracks, non-penetrating cracks, and intact rock blocks are often formed after rock failure, the contact state criterion and shear friction relationship of discrete rock blocks and the mixed fracture displacement–damage–load relationship are established, respectively. Combined with the Mohr–Coulomb criterion, the constitutive model of mining rock mass deformation–discrete block motion and interaction is formed. On this basis, according to the engineering geological conditions of Yushupo Coal Mine, a numerical model for the development of water-conducting cracks in the roof with faults under repeated mining of extra-thick coal seams is established. The results show the following: The constitutive relation of the continuous deformation–discrete block interaction of overlying strata and the corresponding finite element–discrete element FDEM numerical program and VUSDFLD multi-coal seam continuous mining subroutine can numerically realize the formation process of faults and water flowing fractures in overlying strata under continuous mining of extra-thick multi-coal seams. The toughness of sand mudstone is low, and the fracture will be further developed under the repeated disturbance of multi-thick coal seam mining. Finally, it is stabilized at 216–226 m, and the ratio of fracture height to mining thickness is 14.1. When the working face advances to the fault, the stress concentration occurs in the fault and its overlying rock, which leads to the local fracture of the roof rock mass and the formation of cracks. The fault group makes this phenomenon more obvious. The results have been preliminarily applied and tested in Ningwu mining area, which provides theoretical support for further development of roof water disaster control under the condition of an extra-thick coal seam and avoids the loss of water resources in surface water and underground aquifers.

Effects of Soil Microorganisms on Carbon Sequestration under Different Mixed Modification Models in Pinus massoniana L. Plantation

In forests, microbial populations in the soil can directly influence the decomposition of carbon from surface plants, promoting carbon storage and stability. However, in sustainable forest management, it is still unclear how soil microorganisms under different plantation types affect organic carbon sequestration and whether the mechanisms of influence are the same. In this research, we focused on four mixed forests and pure Pinus massoniana-planted forest in the state-owned forest farm of Dushan County. Three replicated plots were set up for each model, and soil samples were collected from different layers (0–20 cm, 20–40 cm, and 40–60 cm), totaling 45 samples. We elucidated the effects of soil microorganisms on carbon sequestration under five mixed modification models of P. massoniana and further explored the mechanisms by which microbial functional communities regulate soil carbon sequestration under different mixed models through molecular sequencing and collinear network analysis. Variance analysis indicated that the soil organic carbon (SOC) of the same soil layer varied significantly, and there were also significant differences in the composition of soil bacterial and fungal microbial communities. Moreover, the bacterial community was more sensitive to changes in the vegetation environment, while the fungal community structure was more resistant to changes in the soil environment. Correlation analysis indicated that the diversity and composition of the bacterial community had more positive effects on soil organic carbon than those of the fungal community. Linear fitting and redundancy analysis (RDA) showed that particulate organic carbon (POC) in soil had the strongest correlation with SOC content. Soil microorganisms affected the storage and stability of soil carbon mainly by regulating the conversion of litter (carbon sources) into POC. The soil environment of different mixed models had different effects on soil carbon accumulation. Both correlation and collinearity network analyses indicated that soil microbial functional groups could enhance carbon storage by regulating readily oxidizable carbon (EOC) and POC content in mixed forest plantations. The results of our study provide a sound basis for replanting a reasonable forest model structure to improve forest carbon storage.

Perceptions about dementia clinical trials among underrepresented populations: A nationally representative survey of U.S. dementia caregivers.

The research community has historically failed to enroll diverse groups of participants in dementia clinical trials. A unique aspect of dementia care research is the requirement of a study partner, who can attest to the care recipient's clinical and functional capacity. The aim of this study is to assess racial and ethnic differences and the importance of various trial considerations among dementia caregivers, in their decision to participate in clinical research as study partners. We embedded a vignette about a hypothetical dementia clinical trial in a nationally representative survey of U.S. dementia caregivers, oversampling non-Hispanic Black and Hispanic caregivers. Dementia caregivers were asked about their willingness to participate in the trial with their care recipient and rated the importance of nine considerations in hypothetical decisions to participate. Caregiver demographic characteristics were analyzed as predictors of trial participation in a base demographic model. In a second reasons model caregiver demographic characteristics and the rated importance of the nine considerations were separately analyzed as predictors; both models used survey-weighted logistic regression. The sample consisted of 610 dementia caregivers, including 156 non-Hispanic Black and 122 Hispanic caregiver participants. In the base demographic model, hypothetical trial participation was negatively associated with older caregiver age (OR (odds ratio) = 0.72, p = < 0.001). In the reasons model, the rated importance of a social responsibility to help others by participating in research was significantly associated with participation (OR = 1.56, p = 0.049), while the importance of the possibility of the care recipient experiencing serious side effects was negatively associated with participation (OR = 0.51, p = 0.003). In both models there was no significant difference in hypothetical participation between non-Hispanic Black and non-Hispanic White caregivers, or between Hispanic and non-Hispanic White caregivers. Hispanic and non-Hispanic Black dementia caregivers were not less likely than non-Hispanic White dementia caregivers to participate in a hypothetical dementia clinical trial. Our study suggests that failures to recruit diverse populations in dementia clinical research are not attributable to less willingness among members of underrepresented groups but may instead reflect structural barriers and historic exclusion from trial participation.

Criteria for Building Residential Arrangement Models on the Coastal Island System of Vietnam

This research marks a significant milestone by proposing a set of comprehensive criteria for building residential arrangement models on Vietnam's coastal island system. The unique characteristics of these islands, such as their limited space and resources, pose complex challenges for population placement. Therefore, establishing the proper criteria for population placement is crucial and pivotal to ensure this area's sustainable and harmonious development. The author has meticulously derived 13 essential criteria from various data sources, including articles, project topics, field surveys, and expert consultations. These criteria are not just a theoretical framework but a practical tool to build a reasonable population arrangement model to bring sustainable, diverse, and harmonious development to the island community.

Convergence analysis of the time-varying discrete-time Altafini model on signed network

This paper mainly studies the convergence of time-varying discrete-time Altafini model. Firstly, we convert the bipartite consensus of the Altafini model with n nodes into consensus of the DeGroot model with 2 n nodes by adopting lifting approach. Secondly, based on structural balance theory and node relabelling theory, the consensus of enlarged DeGroot model can be further equivalently converted into the consensus of one simple DeGroot model with n nodes. Thirdly, by using the equivalent transformation and the ergodic coefficient theory, we change the sufficient condition from ‘repeatedly jointly strongly connected’ graphs to ‘repeatedly jointly scrambling’ graphs, and verify that the new condition also can lead to the bipartite consensus of the Altafini model. This novel criterion allows for determining the bipartite consensus of the Altafini model without the condition of ‘repeatedly jointly strongly connected’ graphs. Next, we provide a concise proof for the case of opinions converging to 0 with the condition of ‘repeatedly jointly strongly connected’ by the ergodic coefficient theory. Finally, we develop a reasonable coevolution model to illustrate the effectiveness and strong applicability of our main theoretical results.

Structural features and Holocene activity of the Motuo fault zone, eastern Himalaya syntaxis

The Motuo Fault zone (MTF), the southeast boundary of the eastern Himalaya syntaxis, is an important information carrier to understand the present-day kinematics and geodynamics of the Tibetan Plateau. However, little knowledge has been obtained about its Holocene activity and structural features of the MTF due to the ubiquitous vegetation covering. Interpretations of satellite imageries, outcrop and trench observations, together with the radiocarbon and Optically Stimulated Luminescence (OSL) dating results reveal that (1) the MTF consists of multiple NE–SW-trending branch faults, forming a wide fault system that could be generally divided into the western segment, central segment and eastern segment; (2) the drainage systems, mountain ridges and alluvial fans have been systematically left-laterally deflected or offset along the MTF; (3) two surface-rupturing earthquakes occurred close to or slightly earlier than 9.41 ± 0.94 ky BP and after ∼1540 yr BP, respectively, indicating that the MTF is an Holocene active fault. Due to the very limited exposure, it is quite possible that many paleoseismic events have not been revealed by this study and previous publications. For a better understanding and assessment of the seismogenic behavior of the MTF, a complete paleoseismic sequence and reasonable earthquake recurrence model need to be set up, especially considering the complex geometry of the fault system that probably suggests intricate fault rupturing.

Assessment of ϒ -states above BB¯ -threshold using a constituent-quark-model based meson-meson coupled-channels framework

The ϒ(10753) state has been recently observed by the Belle and Belle II collaborations with enough global significance to motivate an assessment of the high-energy spectrum usually predicted by any reasonable quark model. In the framework of a constituent quark model which satisfactorily describes a wide range of properties of conventional hadrons containing heavy quarks, the quark-antiquark and meson-meson degrees of freedom have been incorporated with the goal of elucidating the influence of open-bottom meson-meson thresholds into the ϒ states whose masses are within the energy range of the ϒ(10753)’s mass. It is well known that such effects could be relevant enough as to dynamically generate new states and thus provide a plausible explanation of the nature of the ϒ(10753) state. In particular, we have performed a coupled-channels calculation in which the bare states ϒ(4S), ϒ(3D), ϒ(5S), and ϒ(4D) are considered together with the threshold channels BB¯, BB¯*, B*B¯*, BsB¯s, BsB¯s*, and Bs*B¯s*. Among the results we have described, the following are of particular interest: (i) a richer complex spectrum is gained when thresholds are present and bare bound states are sufficiently nonrelativistic; (ii) those poles obtained in the complex energy plane do not have to appear as simple peaks in the relevant cross sections; and (iii) the ϒ(10753) candidate is interpreted as a dressed hadronic resonance whose structure is an equally mixture of a conventional bb¯ state and B*B¯* molecule. Published by the American Physical Society 2024