Initial State Research Articles

A model with multiple intracranial aneurysms: possible hemodynamic mechanisms of aneurysmal initiation, rupture and recurrence

BackgroundHemodynamic factors play an important role in aneurysm initiation, growth, rupture, and recurrence, while the mechanism of the hemodynamic characteristics is still controversial. A unique model of multiple aneurysms (initiation, growth, rupture, and recurrence) is helpful to avoids the confounders and further explore the possible hemodynamic mechanisms of aneurysm in different states.MethodsWe present a model with multiple aneurysms, and including the states of initiation, growth, rupture, and recurrence, discuss the proposed mechanisms, and describe computational fluid dynamic model that was used to evaluate the likely hemodynamic effect of different states of the aneurysms.ResultsThe hemodynamic analysis suggests that high flow impingement and high WSS distribution at normal parent artery was found before aneurysmal initiation. The WSS distribution and flow velocity were decreased in the new sac after aneurysmal growth. Low WSS was the risk hemodynamic factor for aneurysmal rupture. High flow concentration region on the neck plane after coil embolization still marked in recanalized aneurysm.ConclusionsAssociations have been identified between high flow impingement and aneurysm recanalization, while low WSS is linked to the rupture of aneurysms. High flow concentration and high WSS distribution at normal artery associated with aneurysm initiation and growth, while after growth, the high-risk hemodynamics of aneurysm rupture was occurred, which is low WSS at aneurysm dome.

The Role of Renewable Energy in Enhancing Environmental Sustainability in Saudi Arabia: Analysis of Policies and Legislation Related to Renewable Energy and Evaluation of their Effectiveness

In this paper, we will study the role of renewable energy in promoting environmental sustainability in Saudi Arabia in light of existing policies and legislation. This study assesses the effectiveness of these current policies and legislation in encouraging the energy transition. A survey was conducted among around 200 experts in the field of energy and the environment to highlight the current state of initiatives taken to encourage the transition to clean energy and identify the strengths and weaknesses weaknesses of current policies and laws. By examining these policies and their impact, the study aims to provide insights to improve renewable energy frameworks and maximize large-scale contribution to the energy transition to mitigate environmental challenges in Saudi Arabia.

The State of Supplier Diversity Initiatives by Large Corporations: The New Sustainable Supply Chain?

We describe the state of supplier diversity efforts by large corporations from an international perspective. We examine data for the companies in the 2020 and 2022 Fortune Global 500 and find that corporate definitions of diversity are dynamic and vary across regions. Furthermore, while supplier diversity efforts are not yet widespread, these initiatives are increasingly common, especially in the form of references to diversity in companies’ supplier codes of conduct. Companies in North America and in certain economic sectors, such as the financial and healthcare sectors, are more likely to have such efforts in place. Based on our data, companies that report on their internal diversity and companies that have other forms of supplier sustainability initiatives are also more likely to have supplier diversity initiatives. We argue further that supplier diversity efforts will follow a trajectory similar to other supplier sustainability efforts. Finally, we suggest possible avenues for future research on supplier diversity.

Fixed-time synchronization of complex-valued neural networks for image protection and 3D point cloud information protection

This paper studies the fixed-time synchronization (FDTS) of complex-valued neural networks (CVNNs) based on quantized intermittent control (QIC) and applies it to image protection and 3D point cloud information protection. A new controller was designed which achieved FDTS of the CVNNs, with the estimation of the convergence time not dependent on the initial state. Our approach divides the neural network into two real-valued systems and then combines the framework of the Lyapunov method to give criteria for FDTS. Applying synchronization to image protection, the image will be encrypted with a drive system sequence and decrypted with a response system sequence. The quality of image encryption and decryption depends on the synchronization error. Meanwhile, the depth image of the object is encrypted and then the 3D point cloud is reconstructed based on the decrypted depth image. This means that the 3D point cloud information is protected. Finally, simulation examples verify the efficacy of the controller and the synchronization criterion, giving results for applications in image protection and 3D point cloud information protection.

On State Transition Probability and Performance of Direct Acyclic Graph Based Ledgers

Direct acyclic graph (DAG) based ledgers with multi-chain structures aim to solve the technical bottlenecks associated with classical blockchain technologies in the Internet of Things (IoT). The basic working principle of DAG-based ledgers is to validate new transactions by previous transactions in order to be added to the system. During the tip selection process in the unsteady regime, the state transition probability refers to the probability of a transaction changing from the initial state to an arbitrary state. The state transition probability plays an indispensable role in the performance and security analysis of the IoT relying on DAG-based ledgers. In this paper, we derive the exact expression and an approximate expression of the state transition probability, which both are in closed form. In addition, we propose and analyze three performance metrics, i.e., the expected cumulative weight, the expected number of steps, and the confirmation failure probability, which are derived from the state transition probability and greatly enrich the performance analysis and evaluation of the IoT. Markov chain Monte Carlo (MCMC) simulations are carried out to verify the derived analytical results and provide insight into the IoT using DAG-based ledgers.

Digital Economy: Türkiye’s Development Trends

This approach enables a comprehensive exploration of the digital economy's development trends in Türkiye, covering various sectors and dimensions. The article examines the current state of digital infrastructure, technology adoption, and related policy initiatives in Türkiye, and discusses the challenges and opportunities presented by the digital economy, with a particular focus on job creation, economic growth, and competitiveness. The aim of this article is to examine the development trends of the digital economy in Türkiye, highlighting the key factors that contribute to its growth and competitiveness. Türkiye has made significant strides in developing its digital economy, with investments in digital infrastructure, technology adoption, and policy initiatives aimed at fostering growth and competitiveness. The article also highlights the importance of addressing the digital divide and promoting digital inclusion, also ensuring data privacy.

Artificial-Intelligence-Based Surrogate Solution of Dissipative Quantum Dynamics: Physics-Informed Reconstruction of the Universal Propagator.

The accurate (or even approximate) solution of the equations that govern the dynamics of dissipative quantum systems remains a challenging task in quantum science. While several algorithms have been designed to solve those equations with different degrees of flexibility, they rely mainly on highly expensive iterative schemes. Most recently, deep neural networks have been used for quantum dynamics, but current architectures are highly dependent on the physics of the particular system and usually limited to population dynamics. Here we introduce an artificial-intelligence-based surrogate model that solves dissipative quantum dynamics by parametrizing quantum propagators as Fourier neural operators, which we train using both data set and physics-informed loss functions. Compared with conventional algorithms, our quantum neural propagator avoids time-consuming iterations and provides a universal superoperator that can be used to evolve any initial quantum state for arbitrarily long times. To illustrate the wide applicability of the approach, we employ our quantum neural propagator to compute the population dynamics and time-correlation functions of the Fenna-Matthews-Olson complex.

Relative controllability of linear state-delay fractional systems

AbstractIn this paper, our focus is on exploring the relative controllability of systems governed by linear fractional differential equations incorporating state delay. We introduce a novel counterpart to the Cayley-Hamilton theorem. Leveraging a delayed perturbation of the Mittag-Leffler function, along with a determining function and an analog of the Cayley-Hamilton theorem, we establish an algebraic Kalman-type rank criterion for assessing the relative controllability of fractional differential equations with state delay. Moreover, we articulate necessary and sufficient conditions for relative controllability criteria concerning linear fractional time-delay systems, expressed in terms of a new $$\alpha $$ α -Gramian matrix and define a control which transfer the system from any initial state to any final state within a given time. The theoretical findings are exemplified through the presentation of illustrative examples.

Abstract 5862: Retrotransposable elements mediate the drug-tolerant persistence in claudin-low breast cancer chemo-treatment

Abstract The emergence of acquired drug resistance through treatment remains a critical threat to efficient cancer therapies, which most often lead to relapse and metastasis. The development of resistance is an evolutionary adaptation process that heavily depends on tumor heterogeneity (underpinned by cellular plasticity). Therefore, a longitudinal deconvolution of treatment adaptation is critical to uncover the driving-force mechanisms underpinning resistance adaptation. In the clinic, chemotherapy remains the mainstream treatment for TNBC, and one of the primary chemo-agents is doxorubicin. Although the initial responsive rate of doxorubicin-based chemotherapies is up to 70%, it is well recognized that TNBC cells usually generate an adaptive response and result in acquired drug-resistance and multi-drug resistant phenotypes. Although numerous mechanisms of chemo-resistance evolution have been proposed, most of these studies focused on the initial and terminal states, from which we could not deconvolute the adaptation routes and might miss the transient fate-switching events. We hypothesize that the claudin-low TNBC chemo-resistant cells may derive from the early-stage reversible chemo-tolerant persistent state, and early-stage state-switching mediated by epigenetic landscape reprogramming might determine the adaptation through treatment. We developed an in vitro “pulsing-treatment” model to test the hypothesis, which could mimic the clinical treatment and provide therapeutically relevant insights into the acute drug-induced stress response and regrowth. Unexpectedly, we found that the human endogenous retrovirus-like elements (HERVs) show an aberrant bursting in the adaptation. To further interrogate the adaptation, we did the longitudinal single-cell multi-omics sequencing. With a novel bioinformatic workflow, we integrated the HERV’s expression information with protein-coding genes profiling and chromatin accessibility. The preliminary result indicated that a subpopulation of HERV-high cells might transiently mediate the persistence of cancer cells at the early stage of the treatment, which coupled with whole transcriptomic and chromatin landscape reprogramming, and molecular subtype switching. In this study, we employed a new strategy to investigate the longitudinal adaptation route through treatment and uncover a non-canonical element, which shed new light on drug resistance research and novel target screening. Citation Format: Zijian Zhang, Yiyang Wang, Xinlong Luo, Xiaomei Zhan, Jun Ding, Tao P. Wu. Retrotransposable elements mediate the drug-tolerant persistence in claudin-low breast cancer chemo-treatment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5862.

Abstract 3617: Expansion and persistence of anti-BCMA CAR T cells correlates with durability of responses in multiple myeloma patients

Abstract Background: Chimeric antigen receptor (CAR) T cells targeting B-cell maturation antigen (BCMA) can induce >70% response rates in patients with relapsed/refractory multiple myeloma (RRMM). However, most patients ultimately relapse. There are two FDA-approved CAR T products: ciltacabtagene autoleucel (cilta-cel) and idecabtagene vicleucel (ide-cel), the former having much more durable responses. The mechanisms that mediate more durable responses with these CAR T cells is unknown. Previous studies have shown that specific cell states in the apheresis and infusion products are associated with long-term efficacy of anti-BCMA CAR therapy in MM and the relationship between durability of response and CAR T cell persistence remains unclear. Methods: We analyzed serial bone marrow and PBMC samples from 20 MM patients who received BCMA CAR T therapy, including cilta-cel, ide-cel, and JCARH125. Flow cytometry and single-cell RNAseq were used to evaluate the features of CAR T cells that are associated with durable clinical responses. Results: While CAR T cells were detectable but variable among patients at 1-month post infusion, the frequency of CAR T cells in bone marrow at 1-month post-infusion is positively correlated with progression free survival (PFS) in all MM patients (p=0.009). This association was also evident in patients just treated with cilta-cel (p= 0.008). By 6 months, CAR T cells could still be detected by high-throughput flowcytometry in the cilta-cel-treated patients, but CAR T cells were not detectable in most of the patients receiving the other 2 products. Cilta-cel CAR T cells predominantly possessed an effector-like surface phenotype at 1-moth post infusion in both CD8+ and CD4+ compartments in bone marrow. These CAR T cells shifted to effector memory and central memory T cell states by 6 months, respectively. For ide-cel and JCARH125, patients with more durable responses (>6mos) there was a significantly higher proportion of CAR T cells with CD8+ effector memory-like state at 1-month post infusion. Conclusions: Our data demonstrate that distinct anti-BCMA CAR T cell treatments lead to very different cellular outcomes: Cilta-cel CAR T cell treatment leads to greater expansion and persistence compared to other anti-BCMA CAR T cells. Cilta-cel CAR T cells also shift from the initial effector state to a memory phenotype. These results provide insights into features of more efficacious anti-BCMA CAR T cells that can help guide the future development of more durable treatments for MM. Citation Format: Kai Wu, Karen Law, Guy Ledergor, Chang Liu, Zenghua Fan, Vibha Gurunathan, Averey Lea, Matthew Clark, Serena Kwek, Alexander Cheung, Jeffrey Wolf, Ajai Chari, Anupuma Kumar, Justin Eyquem, Thomas Martin, Lawrence Fong. Expansion and persistence of anti-BCMA CAR T cells correlates with durability of responses in multiple myeloma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3617.

Effect of arch foot settlement on soil arching in pile-supported embankments under localized surface loading: Numerical damping spring-based trapdoor tests

In most existing trapdoor tests, the supporting structures beneath the arch feet are usually stationary, however, for embankments with flexible or floating piles in deep soft soil areas, piles supporting the arch foot usually settle under vehicle loads, significantly affecting the settlement deformation and load transfer of the embankment. Therefore, A numerical damping spring-based trapdoor model that can account for arch foot settlement is proposed to investigate the effect of arch foot settlement on the soil arching, while comprehensively considering the influence of the interaction among the pile, embankment fill, and soft soil on the evolution of soil arching in a pile-supported embankment. The results show that arch foot settlement on the soil arching differs significantly at different stages: it weakens the soil arching in the initial state and enhances the soil arching in the recovery state but little affects the soil arching in the ultimate state. Moreover, the arch foot settlement significantly reduces the height and stability of the soil arch in relatively high soft soil stiffness conditions, leading to a soil arch that is susceptible to degradation under localized loads. In addition, the arch foot settlement increases the normalized displacement needed for full mobilization of soil arching, resulting in varied levels of mobilization under the same differential settlement.

Exploring Systemic Risk Dynamics in the Chinese Stock Market: A Network Analysis with Risk Transmission Index

Systemic risk refers to the potential for a disruption in one part of a financial system to trigger a cascade of adverse effects, impacting the functioning of the system. Despite the progress on novel systemic risk measures, research on dynamics of systemic risk network structure and its community effect is still in its initial state. In this study, we utilize price data from 107 representative Chinese stocks spanning the period from 2017 to 2022. A systemic risk network is derived from the Risk Transmission Index based on TENET and the QR–Lasso model. By utilizing DBSCAN, HITS and community detection algorithms on the network, we aim to propose a more suitable definition of systemically important companies, explore the interrelationships between companies, and discuss its plausible reasons for dynamics structural changes. The empirical findings demonstrate a substantial involvement of insurance companies in both contributing to and receiving systemic risk within the analyzed context. We identify prominent risk output and input centers, and emphasize the profound impact of the COVID-19 pandemic on the dynamics of systemic risk.

Pore-scale simulation of low-salinity waterflooding in mixed-wet systems: effect of corner flow, surface heterogeneity and kinetics of wettability alteration

The initial wettability state of the candidate oil reservoirs for low-salinity waterflooding (LSWF) is commonly characterized as mixed-wet. In mixed-wet systems, both the two-phase flow dynamics and the salt transport are significantly influenced by the corner flow of the wetting phase. Thus this study aims at comprehensive evaluation of LSWF efficiency by capturing the effect of corner flow and non-uniform wettability distribution. In this regard, direct numerical simulations under capillary-dominated flow regime were performed using the OpenFOAM Computational Fluid Dynamics toolbox. The results indicate that corner flow results in the transport of low-salinity water ahead of the primary fluid front and triggers a transition in the flow regime from a piston-like to multi-directional displacement. This then makes a substantial difference of 22% in the ultimate oil recovery factors between the 2D and quasi-3D models. Furthermore, the interplay of solute transport through corners and wettability alteration kinetics can lead to a new oil trapping mechanism, not reported in the literature, that diminishes LSWF efficiency. While the findings of this study elucidate that LSWF does exhibit improved oil recovery compared to high-salinity waterflooding, the complicating phenomena in mixed-wet systems can significantly affect the efficiency of this method and make it less successful.

Uncertainty-resilient constrained rendezvous trajectory optimization via stochastic feedback control and unscented transformation

This paper proposes an innovative approach for constrained rendezvous trajectory optimization (CRTO) using stochastic optimal feedback control and unscented transform (UT) uncertainty quantification. The method overcomes limitations of traditional deterministic CRTO solutions that suffer from uncontrolled terminal state error growth and severely deteriorated path constraint satisfaction when initial state uncertainty, dynamics uncertainty, and navigation uncertainty are considered. The approach involves constructing a stochastic optimal feedback control (SOFC) problem with chance constraints and introducing linear feedback control to regulate both the mean and variance of terminal state errors. UT is employed to approximately quantify the state errors and their propagation, transforming the SOFC problem into an unconstrained deterministic optimization (UDO) problem. Differential dynamic programming (DDP) is then used to solve the UDO problem. The obtained stochastic optimal solution provides a robust rendezvous trajectory and a corresponding explicit closed-loop guidance law, improving terminal accuracy and path constraint satisfaction in the presence of various considered uncertainties. The influence of different term weights in the objective function on terminal accuracy and path constraint satisfaction is also studied. The effectiveness of the proposed approach is verified through Monte Carlo simulation, demonstrating the robustness of the closed-loop control strategy. The results highlight the potential of the method in enhancing terminal accuracy and path constraint satisfaction in uncertain rendezvous scenarios.

Impact of boundary conditions on the modeled thermal regime of the Antarctic ice sheet

Abstract. A realistic initialization of ice flow models is critical for predicting future changes in ice sheet mass balance and their associated contribution to sea level rise. The initial thermal state of an ice sheet is particularly important, as it controls ice viscosity and basal conditions, thereby influencing the overall ice velocity. Englacial and subglacial conditions, however, remain poorly understood due to insufficient direct measurements, which complicate the initialization and validation of thermal models. Here, we investigate the impact of using different geothermal heat flux (GHF) datasets and vertical velocity profiles on the thermal state of the Antarctic ice sheet and compare our modeled temperatures to in situ measurements from 15 boreholes. We find that the temperature profile is more sensitive to vertical velocity than to GHF. The basal temperature of grounded ice and the amount of basal melting are influenced by both selection of GHF and vertical velocity. More importantly, we find that the standard approach, which consists of combining basal sliding speed and incompressibility to derive vertical velocities, provides reasonably good results in fast-flow regions (ice velocity >50 m yr−1) but performs poorly in slow-flow regions (ice velocity <50 m yr−1). Furthermore, the modeled temperature profiles in ice streams, where bed geometry is generated using a mass conservation approach, show better agreement with observed borehole temperatures compared to kriging-based bed geometry.

Exponential finite-time circle formation of multi-agent systems and its application in circle containment

This paper studies the exponential finite-time formation problems for a group of locomotive agents that maintain order on a circle. In an ideal environment, that is, without external disturbances, a combined protocol with linear continuous-time states and their nonlinear continuous functions is proposed. Each agent can adjust the movement speed itself by changing the exponent of the nonlinear function. The obtained closed-loop system will converge with an exponential speed when the initial states of the agents are far from the target location, and with a finite-time speed nearby. For the case with external disturbances, a combined protocol with linear continuous-time states and their sign functions is presented. Due to the discontinuity of the sign functions, the Filippov solutions are employed. The exponential finite-time circle formation will be achieved even in the existence of external disturbances. Moreover, the above result is extended to the application in circle containment, where the defenders will form a circle formation to surround the swarm of protectees. Finally, several computational simulations illustrate the validity of the proposed protocols.

Scalable Learning for Spatiotemporal Mean Field Games Using Physics-Informed Neural Operator

This paper proposes a scalable learning framework to solve a system of coupled forward–backward partial differential equations (PDEs) arising from mean field games (MFGs). The MFG system incorporates a forward PDE to model the propagation of population dynamics and a backward PDE for a representative agent’s optimal control. Existing work mainly focus on solving the mean field game equilibrium (MFE) of the MFG system when given fixed boundary conditions, including the initial population state and terminal cost. To obtain MFE efficiently, particularly when the initial population density and terminal cost vary, we utilize a physics-informed neural operator (PINO) to tackle the forward–backward PDEs. A learning algorithm is devised and its performance is evaluated on one application domain, which is the autonomous driving velocity control. Numerical experiments show that our method can obtain the MFE accurately when given different initial distributions of vehicles. The PINO exhibits both memory efficiency and generalization capabilities compared to physics-informed neural networks (PINNs).

A pre-defined finite time neural solver for the time-variant matrix equation [formula omitted

During the process of the general practical applications and scientific researches, time varying issues are an inescapable challenge to be tackled. In this article, a pre-defined finite-time zeroing neural networks (PDFT-ZNN) is devoted to solve the linear time-variant matrix equation (TVME) E(t)X(t)G(t)=D(t) within a finite time (FT), which contrasts with the general ZNN (G-ZNN) models with relatively long global convergence time. Moreover, the proposed PDFT-ZNN model’s convergence time could be calculated in advance by adhering to the designed system parameters; this has nothing to do with the model’s initial state. Additionally, after the convergence analysis, if the solution error is relative small, the simple and effective method is to introduce a linear item to accelerate the convergence speed, in comparison with only the power-type ZNN models. Theory-based analysis and simulation-based results further validate that, the neural state solved by the presented FT-ZNN model can reach the theory-based solution of E(t)X(t)G(t)=D(t) within the pre-defined finite time.

Control Strategy for Building Air Conditioning Cluster Loads Participating in Demand Response Based on Cyber-Physical System

In recent years, with rising urbanization and ongoing adjustments in industrial structures, there has been a growing dependence on public buildings. The load of public buildings gradually becomes the main component of the peak load in summer, among which the load of air conditioning is particularly prominent. To clarify the key problems and solutions to these challenges, this study proposes a multi-objective optimization control strategy for building air conditioning cluster participation in demand response based on Cyber-Physical System (CPS) architecture. In a three-layer typical CPS architecture, the unit level of the CPS achieves dynamic information perception of air conditioning clusters through smart energy terminals. An air conditioning load model based on the multiple parameter types of air conditioning compressors is presented. Then, the system level of the CPS fuses multiple pieces of information through smart energy gateways, analyzing the potential for air conditioning clusters when they participate in demand response. The system of system level (SoS level) of the CPS deploys a multi-objective optimization control strategy which includes the uncertainty of the initial states of air conditioning clusters within the intelligent building energy management system. The optimal model takes into account the differences in the environmental conditions of each individual air conditioning unit within the cluster and sets different operating modes for each unit to achieve load reduction while maintaining temperatures within a comfortable range for the human body. The Multi-Objective Particle Swarm Optimization (MOPSO) algorithm based on Pareto frontiers is applied to solve this optimization control strategy and to optimize the operational parameters of the air conditioning clusters. A comparative analysis is conducted with single-objective optimization results obtained using the traditional Particle Swarm Optimization (PSO) algorithm. The case study results indicate that the proposed multi-objective optimization control strategy can effectively improve the thermal comfort of the human body towards the controlled temperatures of air conditioning clusters while meeting the accuracy of demand response. In the solution phase, the highest temperature within the air conditioning clusters is 24 °C and the lowest temperature is 23 °C. Adopting the proposed multi-objective optimization control strategy, the highest temperature is 26 °C and the lowest temperature is 23.5 °C within the clusters and the accuracy of demand response is up to 92%. Compared to the traditional PSO algorithm, the MOPSO algorithm has advantages in convergence and optimization precision for solving the proposed multi-objective optimization control strategy.

Cylindrically Symmetric Diffusion Model for Relativistic Heavy‐Ion Collisions

AbstractA relativistic diffusion model with cylindrical symmetry, which propagates an initial state based on quantum chromodynamics in time toward a thermal equilibrium limit, is derived from nonequilibrium–statistical considerations: adapting an existing framework for Markovian stochastic processes representing relativistic phase‐space trajectories, a Fokker–Planck equation is obtained for the time evolution of particle‐number distribution functions with respect to transverse and longitudinal rapidity. The resulting partially‐evolved distribution functions are transformed to transverse‐momentum and pseudorapidity space, and compared with charged‐hadron data from the CERN Large Hadron Collider.