Dynamic Scaling Research Articles

Experimental Measurements of Explosion Effects Propagating in the Real Geological Environment—Correlation with Small-Scale Model

This research focuses on comparing small-scale and full-scale measurements of wave propagation from explosions by using scaling relationships to find significant correlations between the two. The study investigates how seismic waves generated by explosions behave in the geological environment. The research covers various aspects such as the development of the model, the explosive materials used, measurement methods, evaluation techniques, and relevant software. A scientific approach based on the principle of backward Fourier transform was used to process and evaluate the data, which helps to filter the frequencies. One of the important calculations discussed is the determination of the attenuation coefficient, which helps to describe how waves attenuate as they pass through a material. The research also deals with dynamic scaling, using the dynamic exponent as a scaling factor to provide a better understanding of the behavior of waves at different scales. By comparing real in situ data with results from small-scale models, the study provides a robust framework for predicting the effects of explosions in complex geological environments. The research results show a high correlation coherence of the statistical data files of up to 4.1%. For dynamic tasks and model scaling, an important result can be pointed out, namely the approximately fourfold decrease in the exponents of the dependence on the distance from the excitation source and the amplitudes between P-waves (0.4316) and R-waves (0.1219). Conclusions are targeted at the possibility of correlating three types of results: small-scale simulations, numerical simulations, and a real full-scale experiment.

Efficacy of fosaprepitant for the prevention of postoperative nausea and vomiting in patients undergoing gynecologic surgery: a multicenter, randomized, double-blind study

Abstract Purpose This study aims to investigate whether adding fosaprepitant to palonosetron and dexamethasone is effective in preventing postoperative nausea and vomiting (PONV) in high-risk patients undergoing gynecologic surgery. Methods Eligible patients undergoing gynecological surgery were randomized into two groups (1:1). One group received fosaprepitant (150 mg) and the other (control) received a placebo infusion. Both groups received a single dose of palonosetron (0.25 mg) and dexamethasone (5 mg) together with therapeutic medication. The primary endpoint was the absence of vomiting and no use of rescue antiemetics during the first 24 h after surgery; complete response rate (CRR). Results CRR was significantly higher in the fosaprepitant group compared to the control group 0–24 h after surgery (P = 0.037; relative risk [RR], 1.116; 95% confidence interval [CI], 1.007 to 1.235). Moreover, CRR was also significantly higher during the 24–48 h (P = 0.004; RR, 1.148; 95% CI, 1.045 to 1.261) and 48–72 h (P = 0.039; RR, 1.083; 95% CI, 1.005 to 1.168) observation periods respectively. The complete control rate was higher in the fosaprepitant group than in the control group during the 0–24 h observation period (P = 0.012; RR, 1.367; 95% CI, 1.067 to 1.751). Nausea and rescue antiemetic use were comparable between the two groups. The severity of vomiting was significantly higher in the fosaprepitant group than in the control group on the second day (P = 0.016). Dynamic pain visual analog scale score was lower in the fosaprepitant group and quality of recovery-15 scores were significantly higher in the same group during 0–24 h observation period (P = 0.018 and 0.005, respectively). Conclusions The triple combination of fosaprepitant, palonosetron, and dexamethasone was superior in the prevention of PONV after gynecologic surgery in high-risk patients. We suggest that for high-risk patients, a triple combination therapy may be a better choice. Trial registration Registered at the Chinese Clinical Trial Registry (https://www.chictr.org.cn/showproj.html?proj=171741) with No. ChiCTR2200060890 on June 13, 2022. Principal investigator: Jingdun Xie.

An explicitly magnetic modified embedded atom method formalism for coupled spin dynamics and molecular dynamics

Abstract In this paper, we augment the modified embedded atom method formalism to include magnetic spin-spin interactions for elements with a persistent magnetic moment. While previous spin coupling methods have been based on pair potentials, our Magnetic MEAM formalism, which we term MagMEAM, incorporates the many-body and angular effects of MEAM allowing for the strength of the magnetic interaction to vary with atomic environment. In particular, this allows potentials using this formalism to differentiate the magnetic interaction of different stable phases of magnetic elements such as the ferritic and austenitic phases of iron. This, in turn, allows for a more robust and realistic description of magnetism in polymorphic materials than was previously possible. The motivation for MagMEAM, including the insufficiency of magnetic pair potentials, is presented and the structure of the formalism is developed. A sample iron potential is developed using this formalism and shown to exceed the capabilities of existing magnetic pair potentials by simultaneously reproducing the magnetic energy of both martensite and austenite as well as the dynamic mechanical and magnetic properties of martensite. This newly designed formalism will allow for deeper explorations in the the complex interaction between different phases of polymorphic magnetic materials at the molecular dynamics scale.

Optimizing traffic management for public services during high-demand periods using cloud load balancers

In times of high demand, effective traffic management is essential for public service platforms to maintain reliable, continuous access for users. This paper explores the role of cloud load balancers as a solution for optimizing traffic distribution in public services during peak periods. By dynamically distributing network traffic across multiple servers, cloud load balancers enhance system scalability, flexibility, and response time, which are critical for minimizing service disruptions. The paper further discusses best practices for configuring load balancers, including geographic balancing and redundancy, and highlights the importance of integrating dynamic scaling and monitoring tools to adapt to sudden traffic surges. Additionally, the potential of artificial intelligence (AI) and predictive analytics in advancing cloud load balancing is considered, emphasizing their value for predictive scaling and automated traffic management. Key challenges, including data privacy and dependency on third-party infrastructure, are also addressed, with recommendations for enhancing load balancer deployment in public service applications. This paper concludes with strategic recommendations for public service providers to leverage cloud technology effectively, ensuring improved reliability, security, and cost-efficiency in service delivery. Keywords: Cloud Load Balancers, Traffic Management, Public Service Platforms, Dynamic Scaling, Predictive Analytics, Service Reliability.

Digitalization, Management, and Synthesis of Thermal-Hydraulic Legacy Data Using the Dynamical System Scaling

A unique attribute of the nuclear industry is the extent by which designers, developers, operators, and regulators pay attention to demonstrating the safety case. Nuclear installation resiliency or safety takes overriding priority over function and performance. The preparation of the safety case heavily relies on extensive experimental campaigns that challenge the target design under a spectrum of postulated accident scenarios. For safety reasons, these experiments, typically conducted in subscale test apparatuses intended to reproduce the postulated accident scenarios and event sequences realistically, but subject to proven similarity criteria, are often referred to as scaling analyses. Since the dawn of the nuclear industry, significant resources have been committed to the construction and operation of such test facilities, and a massive amount of data has been generated. Such test data have been and are still used to validate the fundamental assumptions of nuclear power plant phenomena. As we move into the digital world, capturing this multiple decades worth of information in a transparent, easily accessible, and usable manner for the public and industry stakeholders is of paramount importance. Over the last few years, FPoliSolutions has been actively developing an enterprise digital data ontology platform (OGMA) intended to do just that. The goal is to help designers use experimental data to assess their safety case evaluation models. The platform or application programming interface (API) was designed to ontologically organize the experimental data, as well guide the user in the complex analytics associated with the interpretation and use of such test results. The OGMA API supports the user in accessing a library of sophisticated mathematical procedures for performing scaling analyses. For this purpose, the dynamical system scaling (DSS) procedure invented by Dr. Jose’ Reyes was formulated as one of the services in the digital platform. These methods have been demonstrated to provide an excelled mathematical apparatus to identify similarity criteria or quantify distortions between measured data and the target prototypical conditions. Moreover, DSS can provide a level of synthesis across separate effect tests and integral effect tests that has the promise of yielding efficiency in the evaluation code assessment activities, as setting up evaluation models that support the safety case of current and future nuclear power plants is often a daunting and expensive task.

Improved Estimates of Folding Stabilities and Kinetics with Multiensemble Markov Models.

Markov State Models (MSMs) have been widely applied to understand protein folding mechanisms by predicting long time scale dynamics from ensembles of short molecular simulations. Most MSM estimators enforce detailed balance, assuming that trajectory data are sampled at an equilibrium. This is rarely the case for ab initio folding studies, however, and as a result, MSMs can severely underestimate protein folding stabilities from such data. To remedy this problem, we have developed an enhanced-sampling protocol in which (1) unbiased folding simulations are performed and sparse tICA is used to obtain features that best capture the slowest events in folding, (2) umbrella sampling along this reaction coordinate is performed to observe folding and unfolding transitions, and (3) the thermodynamics and kinetics of folding are estimated using multiensemble Markov models (MEMMs). Using this protocol, folding pathways, rates, and stabilities of a designed α-helical hairpin, Z34C, can be predicted in good agreement with experimental measurements. These results indicate that accurate simulation-based estimates of absolute folding stabilities are within reach, with implications for the computational design of folded miniproteins and peptidomimetics.

Green Energy Transition: Evaluating Carbon-To-Liquid Technologies for 2050 Climate Goals and Energy Security

China's coal-to-liquid (CTL) sectors must quickly adjust to a low-carbon economy to meet the energy demand while mitigating environmental and energy security challenges. Similarly, China's climate goals for 2050 are gravely jeopardized if the industry's enormous ecological impact is not addressed. This research develops a conceptual framework for environment-security-economic evaluation of the high-carbon energy sector. Moreover, it constructs a system dynamics material model to predict the upgrade plan's industrial capacity and carbon emission scale. Finally, it calculates the dynamic optimal deployment scale at various epochs based on the sequential decomposition goal of "carbon peak" and "carbon neutrality." These measures are needed to anticipate the development of China's CTL industry. The road map predicts that by 2025, China's CTL industry will be able to meet at least 5% of the country's total oil demand. The adoption of scale A process will improve the economic and environmental performance of the CTL industry due to the cost savings and efficiency gains at a larger scale. In addition, the industry's overall energy efficiency will rise in 2023 and exceed 50% after 2035. Findings indicate that the CTL sector will approach a carbon peak in 2029 and neutrality and transition to hydrogen generation by 2050.

Strategic deployment of hydrogen fuel cell buses and fueling stations: Insights from fleet transition models

Establishing new hydrogen value chains is challenging, requiring economies of scale and balanced supply-demand dynamics. Municipalities can mitigate this risk through government support and deployment strategies. This study analyzes Edmonton's transition to zero-emission buses (ZEBs), focusing on hydrogen fuel cell electric vehicles (HFCEVs) and hydrogen fueling stations (HFSs). Using scenario-based modeling and S-curve models for technology diffusion, we project the adoption of battery electric vehicles (BEVs) and HFCEVs. Deploying over 1000 ZEBs by 2040 is necessary to meet Net-Zero targets, with 310–760 HFCEVs required for the municipal bus inventory. This results in an estimated hydrogen demand of 6.2–14.5 t-H2/day and a reduction of 0.4–1.0 Mt-CO2 in tailpipe emissions by 2050. We use these scenario projections to develop a phased deployment strategy, optimizing fleet operations to reduce HFS costs by 50–60% from 8 to 9 C$/kg-H2 to 3–4 C$/kg-H2. The study underscores the importance of strategic planning and infrastructure investment in realizing net-zero goals, providing a model applicable globally.

Mechanical Expertise Management for the Information Management inScaling Systems with Deep Learning Process

Dynamic scaling information management refers to the adaptive process of adjusting resources and managing data in real time to meet varying demands in computational and storage environments, particularly in cloud computing and data-intensive applications. This approach ensures optimal performance and resource utilization by automatically allocating or deallocating computing power, storage capacity, and network bandwidth based on current workloads and system performance metrics. Key components include monitoring tools that continuously assess resource usage, predictive analytics to anticipate future demands, and automated orchestration systems that execute scaling actions without human intervention. This paper introduces Predicted Dynamic Scaling in Information System Development (PDS-ISD) as a novel framework for talent cultivation within ISD education. By synthesizing empirical observations and theoretical models, PDS-ISD offers a predictive tool to forecast talent cultivation outcomes based on key educational factors. Through a comprehensive review of literature, the paper explores the theoretical foundations and practical applications of PDS-ISD in ISD education. Additionally, simulated results demonstrate the effectiveness of PDS-ISD in predicting talent outcomes across various scenarios. The findings highlight the importance of factors such as curriculum design, instructor expertise, student readiness, and project demands in shaping talent cultivation outcomes. The paper concludes with implications for practice and future research directions to further enhance the predictive capabilities and applicability of PDS-ISD in ISD education. Additionally, simulated results demonstrate the effectiveness of PDS-ISD in predicting talent outcomes across various scenarios, with an average accuracy of 85%. The findings highlight the importance of factors such as curriculum design, instructor expertise, student readiness, and project demand in shaping talent cultivation outcomes

Optimizing data transmission in 6G software defined networks using deep reinforcement learning for next generation of virtual environments

Data transmission of Virtual Reality (VR) plays an important role in delivering a powerful VR experience. This increasing demand on both high bandwidth and low latency. 6G emerging technologies like Software Defined Network (SDN) and resource slicing are acting as promising technologies for addressing the transmission requirements of VR users. Efficient resource management becomes dominant to ensure a satisfactory user experience. The integration of Deep Reinforcement Learning (DRL) allows for dynamic network resource balancing, minimizing communication latency and maximizing data transmission rates wirelessly. Employing slicing techniques further aids in managing distributed resources across the network for different services as enhanced Mobile Broadband (eMBB) and Ultra-Reliable and Low Latency Communications (URLLC). The proposed VR-based SDN system model for 6G cellular networks facilitates centralized administration of resources, enhancing communication between VR users. This innovative solution seeks to contribute to the effective and streamlined resource management essential for VR video transmission in 6G cellular networks. The utilization of Deep Reinforcement Learning (DRL) approaches, is presented as an alternative solution, showcasing significant performance and feature distinctions through comparative results. Our results show that implementing strategies based on DRL leads to a considerable improvement in the resource management process as well as in the achievable data rate and a reduction in the necessary latency in dynamic and large scale networks.

Inverse dynamics analysis of 4-DOF stacking robot with closed chain structure

A four-degree-of-freedom PR1300 stacking robot with two parallel quadrilateral structures connected in series at the shoulder was designed. The hybrid robot with local closed chains has the characteristics of compact structure, high stiffness, and high modularity. To solve the dynamic problem of the hybrid robot. Firstly, by analyzing the induced relationship between joints, the stacking robot with a local closed chain structure is transformed into two branch chain mechanisms, and the kinematic model of the robot is established using the D-H parameter method. Secondly, the Kane method is applied to model the dynamics of the two branch chains separately, and the rigid body dynamics Kane equation of the entire 4-DOF stacking robot with a closed chain structure is obtained through the augmented matrix. Finally, the SolidWorks Motion mechanical simulation platform and MATLAB software were used to program dynamic simulations and numerical calculations, and the results were compared to verify the correctness of the dynamic theory derivation. This theory provides a necessary theoretical basis for the subsequent synthesis of robot dynamic scales.

Structural Plasticity as a Driver of the Maturation of Pro-Interleukin-18.

Dynamics are often critical for biomolecular function. Herein we explore the role of motion in driving the maturation process of pro-IL-18, a potent pro-inflammatory cytokine that is cleaved by caspases-1 and -4 to generate the mature form of the protein. An NMR dynamics study of pro-IL-18, probing time scales over 12 orders of magnitude and focusing on 1H, 13C, and 15N spin probes along the protein backbone and amino-acid side chains, reveals a plastic structure, with millisecond time scale dynamics occurring in a pair of β-strands, β1 and β*, that show large structural variations in a comparison of caspase-free and bound pro-IL-18 states. Fits of the relaxation data to a three-site model of exchange showed that the ground state secondary structure is maintained in the excited conformers, with the side chain of I48 that undergoes a buried-to-exposed conformational change in the caspase-free to -bound transition of pro-IL-18, sampling a more extensive range of torsion angles in one of the excited states characterized, suggesting partial unpacking in this region. Hydrogen exchange measurements establish the occurrence of an additional process, whereby strands β1 and β* locally unfold. Our data are consistent with a hierarchy of dynamic events that likely prime pro-IL-18 for facile caspase binding.

A tri-chromosome-based evolutionary algorithm for energy-efficient workflow scheduling in clouds

Cloud computing is increasingly attracting workflow applications, where workflows need to satisfy execution deadlines and energy consumption is to be minimized. So far, numerous studies have adopted evolutionary algorithms to optimize the energy consumption of workflow execution. Dynamic voltage and frequency scaling (DVFS) has been widely employed to save energy on computing devices running workflow tasks. However, most existing evolutionary algorithms focus on evolving task execution order or mapping from tasks to resources, while neglecting the evolution of task runtime to leverage the dynamic voltage and frequency scaling (DVFS) technology for further energy saving. To compensate for that deficiency, this paper designs a tri-chromosome-based evolutionary algorithm, namely TCEA, to evolve three types of decision vectors (i.e., task order, task and resource mapping, and task runtime) simultaneously using three problem-specific mechanisms. Firstly, we construct a search space by using the tasks’ minimum and optimal runtime, and propose a solution representation mechanism to simplify the decision vector for task runtime between 0 and 1. Secondly, we design a deadline constraint handling mechanism to distribute those durations exceeding the deadline to each task based on their extension of the minimum runtime. Thirdly, we exploit the workflow structure to cluster decision variables without direct constraints into the same group. During each iteration, only the order of tasks within a group evolves to avoid precedence constraints, thus performing searches within the feasible space. At last, we conduct comparison experiments on five types of real-world workflows with 30 to 1000 tasks. The energy consumed by TCEA is much less than those consumed by the state-of-the-art workflow scheduling algorithms, demonstrating the superior performance of TCEA in energy saving.

Network-on-Chip (NoC) Applications for IoT-Enabled Chip Systems: Latest Designs and Modern Applications

Network-on-Chip (NoC) technology has emerged as a critical innovation in the field of integrated circuit design, addressing the growing demands for efficient, scalable, and high-performance on-chip communication. This review provides a comprehensive examination of NoC, highlighting its architecture, key features, and current applications. NoC leverages a network-based approach, utilizing routers and links to interconnect various components on a chip, thereby overcoming the limitations of traditional System-on-chip (SoC) architectures that rely on shared bus systems. The review underscores NoC’s superior communication efficiency, scalability, and reduced latency, which are essential for modern high-performance computing, multi-core processors, and advanced embedded systems. It also explores NoC’s ability to optimize power consumption through dynamic voltage scaling and power gating, making it a favorable choice for energy-efficient designs. Despite its complexity, NoC’s modularity and adaptability make it suitable for a wide range of applications, from artificial intelligence accelerators to high-performance data centers. In conclusion, NoC stands as a transformative technology that enhances the performance and scalability of integrated circuits, paving the way for more sophisticated and powerful electronic systems. As the demand for higher computational capabilities and efficiency continues to rise, NoC is poised to play an increasingly vital role in the advancement of electronic design and architecture.

AIOps in Action: Automating AI Deployment and Management of Large Language Models for Scalable and Ethical Operations

This comprehensive article explores the transformative role of Artificial Intelligence for IT Operations (AIOps) in the deployment and management of Large Language Models (LLMs). It delves into the automation strategies that streamline LLM deployment, including data preparation, model training optimization, and continuous integration and deployment practices. The article addresses the unique challenges in LLM management, such as resource allocation complexities and latency issues, presenting AIOps-driven solutions that leverage predictive analytics and dynamic scaling techniques. A significant focus is placed on the synergies between AIOps and MLOps, highlighting how their integration enhances model versioning, governance, and performance monitoring. The article also examines the critical aspects of real-time monitoring and incident management, showcasing how AIOps enables sophisticated anomaly detection and automated incident response. Ethical considerations in AIOps-driven LLM deployment are thoroughly discussed, emphasizing the importance of bias mitigation, transparency, and accountability. Looking ahead, the article explores future trends in AIOps for LLM management, including advancements in automation technologies and their implications for operational scalability and efficiency. Through a combination of theoretical analysis and practical case studies, this article provides a comprehensive overview of how AIOps is revolutionizing the landscape of AI operations, offering insights into both the current state and future potential of automated, scalable, and ethically responsible LLM management.

Fractional Brownian motion in confining potentials: non-equilibrium distribution tails and optimal fluctuations

Abstract At long times, a fractional Brownian particle in a confining external potential reaches a non-equilibrium (non-Boltzmann)
steady state. Here we consider scale-invariant power-law potentials $V(x)\sim |x|^m$, where $m>0$, and employ the optimal fluctuation method (OFM) to determine the large-$|x|$ tails of the steady-state probability distribution $\mathcal{P}(x)$ of the particle position. The calculations involve finding the optimal (that is, the most likely) path of the particle, which determines these tails, via a minimization of the exact action functional for this system, which has recently become available. Exploiting dynamical scale invariance of the model in conjunction with the OFM ansatz, we establish the large-$|x|$ tails of $\ln \mathcal{P}(x)$ up to a dimensionless factor $\alpha(H,m)$, where $0<H<1$ is the Hurst exponent. We determine $\alpha(H,m)$ analytically (i) in the limits of $H\to 0$ and $H\to 1$, and (ii) for $m=2$ and arbitrary $H$, corresponding to the fractional Ornstein-Uhlenbeck (fOU) process. Our results for the fOU process are in agreement with the previously known exact $\mathcal{P}(x)$ and autocovariance. The form of the tails of $\mathcal{P}(x)$ yields exact conditions, in terms of $H$ and $m$, for the particle confinement in the potential.
For $H\neq 1/2$, the tails encode the non-equilibrium character of the steady state distribution, and we observe violation of time reversibility of the system except for $m=2$. To compute the optimal paths and the factor $\alpha(H,m)$ for arbitrary permissible $H$ and $m$, one needs to solve an (in general nonlinear) integro-differential equation. To this end we develop a specialized numerical iteration algorithm which
accounts analytically for an intrinsic cusp singularity of the optimal paths for $H<1/2$.


Muon spin relaxation study of spin dynamics on a Kitaev honeycomb material H3LiIr2O6

The vacancy effect in quantum spin liquid (QSL) has been extensively studied. A finite density of random vacancies in the Kitaev model can lead to a pileup of low-energy density of states (DOS), which is generally experimentally determined by a scaling behavior of thermodynamic or magnetization quantities. Here, we report detailed muon spin relaxation (μSR) results of H3LiIr2O6, a Kitaev QSL candidate with vacancies. The absence of magnetic order is confirmed down to 80 mK, and the spin fluctuations are found to be persistent at low temperatures. Intriguingly, the time-field scaling law of longitudinal-field (LF)-μSR polarization is observed down to 0.1 K. This indicates a dynamical scaling, whose critical exponent of 0.46 is excellently consistent with the scaling behavior of specific heat and magnetization data. All the observations point to the finite DOS with the form N(E)∼E−ν\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N(E)\\sim {E}^{-\ u }$$\\end{document}, which is expected for the Kitaev QSL in the presence of vacancies. Our μSR study provides a dynamical fingerprint of the power-law low-energy DOS and introduces a crucial new insight into the vacancy effect in QSL.

Ocean-atmosphere-ice processes in the Ross Sea: A review

The Ross Sea has been the site of extensive investigations since the earliest days of polar exploration. The International Geophysical Year of 1957-58 enhanced research activities with the establishment of scientific stations and the collection of oceanographic observations in the area. While many features of its oceanography, ecology, physics, glaciology, geology, and biogeochemistry are known, recent advances provide new insights into its structure and function, as well as into its relationship to global climate. We present a comprehensive review of the advances of understanding the main processes occurring in the area, such as the formation of dense shelf water and the production of Antarctic Bottom Water (AABW), as well as the main drivers (at both large and local scales) of local dynamics and water mass variability. We also summarize the main modeling applications, which are still limited and need to be improved using high-resolution models and, locally, limited-area models to explain processes driven mainly by thermodynamics and water-mass transformations. The Ross Sea forms the most saline AABW due to the activity of two polynyas in the western sector. A salinity gradient occurs on the shelf, with fresh Low Salinity Shelf Waters concentrated in the eastern Ross Sea, which is influenced by the inflow of fresh water from the Amundsen and Bellingshausen Seas. This freshwater inflow was thought to be the cause of a multi-decadal freshening of the High Salinity Shelf Water, precursor to the AABW, although a rebound in salinity in the Ross Sea has been observed since 2014. The increase in salinity has also affected the production of AABW, with the respective rebound occurring almost simultaneously.

Mechanisms of Controllable Growth and Ohmic Contact of Two-Dimensional Molybdenum Disulfide: Insight from Atomistic Simulations.

ConspectusTwo-dimensional (2D) semiconducting transition metal dichalcogenides (TMDs), in particular molybdenum disulfide (MoS2), have recently attracted huge interest due to their proper bandgap, high mobility at 2D limit, and easy-to-integrate planar structure, which are very promising for extending Moore's law in postsilicon electronics technology. Great effort has been devoted toward such a goal since the demonstration of protype MoS2 devices with high room-temperature on/off current ratios, ultralow standby power consumption, and atomic level scaling capacity down to sub-1-nm technology node. However, there are still several key challenges that need to be addressed prior to the real application of MoS2-based electronics technology. The controllable growth of wafer-scale single-crystal MoS2 on industry-compatible insulating substrates is the prerequisite of application while the currently synthesized MoS2 films mostly are polycrystalline with limited sizes of single-crystal domains and may involve metal substrates. The precise layer-control is also very important for MoS2 growth since its electronic properties are layer-dependent, whereas the layer-by-layer growth of multilayer MoS2 dominated by the van der Waals (vdW) epitaxy leads to poor thickness uniformity and noncontinuously distributed domains. High density up to 1013 cm-2 of sulfur vacancies (SVs) in grown MoS2 can cause unfavorable carrier scatting and electronic properties variations and will inevitably disturb the device performance. The dangling-bond-free surface of MoS2 gives rise to an inherent vdW gap at metal-semiconductor (M-S) contact, which leads to high electrical resistance and poor current-delivery capability at the contact interface and thereby substantially limits the performances of MoS2 devices.In this Account, we briefly review recent experimental and theoretical attempts for addressing the aforementioned challenges and present our own insights from atomistic simulations. We theoretically revealed the vital role of substrate steps for guiding unidirectional nucleation of monolayer MoS2 and uniform nucleation and edge-aligned growth of bilayer MoS2 by advanced simulations. The established thermodynamic mechanisms have successfully directed the experimental works on the controllable growth of 2 in. single-crystal monolayer and centimeter-scale uniform bilayer MoS2. The postgrowth repair mechanism of SV defect in MoS2 via thiol chemistry treatment has been theoretically explored with the consideration of side reaction of surface functionalization to help experimentally reduce SV defect density by 75%. Beyond the atomic level understanding, theoretical simulations proposed the electronic states hybridization mechanism across the semimetal-MoS2 vdW interface, thereby guiding experimental effort for realizing Ohmic contact at the MoS2-Sb(0112) vdW interface with record-low contact resistance.These advances provide a sound basis with an atomic-level understanding for addressing the related issues. However, there are still notable gaps in terms of system size and time scale of dynamics between atomistic simulations and experimental observations for the studies of MoS2 growth and interfaces. The combination of multiscale simulations and artificial intelligence technology is expected to narrow these gaps and provide a more insightful understanding of the controllable growth and interfacial properties modulation of MoS2. We conclude the Account with the standing challenges and outlook on future research directions from the theoretical perspective.

Walking-dilaton hybrid inflation with B − L Higgs embedded in dynamical scalegenesis

We propose a hybrid inflationary scenario based on eight-flavor hidden QCD with the hidden colored fermions being in part gauged under U(1)B−L. This hidden QCD is almost scale-invariant, so-called walking, and predicts the light scalar meson (the walking dilaton) associated with the spontaneous scale breaking, which develops the Coleman-Weinberg (CW) type potential as the consequence of the nonperturbative scale anomaly, hence plays the role of an inflaton of the small-field inflation. The U(1)B−L Higgs is coupled to the walking dilaton inflaton, which is dynamically induced from the so-called bosonic seesaw mechanism. We explore the hybrid inflation system involving the walking dilaton inflaton and the U(1)B−L Higgs as a waterfall field. We find that observed inflation parameters tightly constrain the U(1)B−L breaking scale as well as the walking dynamical scale to be ~ 109 GeV and ~ 1014 GeV, respectively, so as to make the waterfall mechanism worked. The lightest walking pion mass is then predicted to be around 500 GeV. Phenomenological perspectives including embedding of the dynamical electroweak scalegenesis and possible impacts on the thermal leptogenesis are also addressed.