Initial State Research Articles

Optimal Consensus Control for Continuous-Time Linear Multiagent Systems: A Dynamic Event-Triggered Approach.

This article investigates the optimal consensus problem for general linear multiagent systems (MASs) via a dynamic event-triggered approach. First, a modified interaction-related cost function is proposed. Second, a dynamic event-triggered approach is developed by constructing a new distributed dynamic triggering function and a new distributed event-triggered consensus protocol. Consequently, the modified interaction-related cost function can be minimized by applying the distributed control laws, which overcomes the difficulty in the optimal consensus problem that seeking the interaction-related cost function needs all agents' information. Then, some sufficient conditions are obtained to guarantee optimality. It is shown that the developed optimal consensus gain matrices are only related to the designed triggering parameters and the desirable modified interaction-related cost function, relaxing the constraint that the controller design requires the knowledge of system dynamics, initial states, and network scale. Meanwhile, the tradeoff between optimal consensus performance and event-triggered behavior is also considered. Finally, a simulation example is provided to verify the validity of the designed distributed event-triggered optimal controller.

Association between anemia and vitaminD deficiency in German seniors : Aretrospective data analysis.

Anemia and deficiency of vitaminD (VDD) are frequently seen in seniors and an association is suspected. Approximately one third of the German population is affected by VDD, with a rising prevalence among seniors. To analyze the association between anemia and VDD among German seniors aged ≥ 60years. Retrospective cross-sectional data analysis (n = 4008) in anationwide working laboratory medical center (January-December 2019). Study parameters included amongst others: hemoglobin (Hb), calcifediol (25D) and calcitriol (1.25D), glomerular filtration rate (GFR) to assess the kidney disease outcomes quality initiative (KDOQI) state. The inclusion criteria were age ≥ 60years, normal C‑reactive protein (CRP) and leucocyte levels. The 25D was estimated in 4008 patients and 1.25D only in 411 patients. Mean age75years (±8.61 years; 60-99 years) with 30.6% males; mean GFR 62 ml/min/1.73 m3 (±22.74); 20% of patients were anemic, 35% were deficient for 25D (< 50 nmol/l), with men > women (p = 0.014). Linear regression analysis revealed asignificant effect of 25D values < 30 nmol/l on hemoglobin in males of KDOQII-III and females of KDOQII-IV (R2 = 0.052; p = 0.005; and R2 = 0.124; p < 0.001, respectively). For 1.25D aweak but significant effect on hemoglobin independent of KDOQI was only seen in women (R2 = 0.200; p = 0.005). In this cohort deficiency of 25D and 1.25D was significantly associated with hemoglobin independent of renal function only in women but not in men.

Energy Efficiency: The Market for Energy Management Systems in Nigeria

Energy efficiency and demand side management (EEDSM) are critical components of sustainable energy strategies globally. In Nigeria, implementing these practices is crucial to address the growing energy demand, enhance energy security, and mitigate environmental impacts. This paper explores Nigeria's Energy Management Systems (EMS) market, highlighting the current state of energy efficiency and demand side management initiatives, market drivers, barriers, and future prospects. The study emphasises the importance of EMS in optimising energy use and underscores the urgent need for policy recommendations. The analysis shows that despite significant challenges, there are substantial opportunities for growth in the EMS market, driven by the industrial, commercial, and public sectors. However, these opportunities can only be fully realised with the right policy support, underlining the necessity of immediate action in this area and making the audience feel the situation's urgency.

DEVELOPMENT CONSIDERATIONS AND OPPORTUNITIES A WINE TOURISM – THE CASE STUDY OF THE STATE ENTERPRISE QUALITY WINES INDUSTRIAL COMPLEX „MILEȘTII MICI”

Wine tourism in the Republic of Moldova occupies an important segment in the country's tourism. Investments in wineries and the state's initiative to register the Republic of Moldova on the map of European wine tourism have led to the creation of itineraries that allow visiting numerous domains and wineries. Moldovan wine products are in demand on the EU market, which contributes to the sustainable development of both the wine sector and wine tourism. This article represents a research of the difficulties and possibilities of development of the wine tourism activity in order to discover the solutions and ways of development of the wine tourism in the Republic of Moldova. The topic is current and needs to be researched. The purpose of the present scientific investigation is to research the wine tourism activity of „Mileștii Mici” Winery, the largest underground winery in the Republic of Moldova, in order to identify development obstacles and ways to overcome them. The present study was carried out based on the following research methods: analysis, synthesis, comparison, grouping, etc. The authors' contribution consists in the presentation, calculation and analysis of the main indicators regarding the volume of tourist activity, the evaluation of the performance, competition, risk and tourist potential of the „Mileștii Mici” Winery and the development of recommendations regarding the promotion of its tourist activity.

Adaptive Multi-Switching-Based Global Tracking Control for Switched Nonlinear Systems With Prescribed Performance

This paper is concerned with the tracking control for a class of switched nonlinear systems subject to prescribed performance. Firstly, a barrier function and a normalized function are introduced to achieve prescribed performance control, which ensures that the tracking error evolves within prescribed boundary. Then, multi-dimensional Taylor network provides a new approach for how to estimate the nonlinearity arising from the backstepping control process. Significantly, the proposed multi-switching-based adaptive controller realizes that all signals in the closed-loop system are globally uniformly ultimately bounded while ensuring asymptotic tracking. Different from most existing network-approximation-based control strategies, the developed method in this paper is not only independent of the initial state, but also can achieve global stability. Finally, it is easy to verify the effectiveness of the proposed control method through two simulations. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This research is motivated by the fact that the control ideas of many practical engineering systems can be provided by making a profound study on switched nonlinear systems. However, most of the existing results focused on semi-global stability of systems. Therefore, this study devotes to develop a novel adaptive prescribed performance control strategy, which can not only ensure global stability of closed-loop system but also realize the asymptotic tracking of the switched nonlinear systems. It is worth noting that the proposed control approach has great significance for many practical systems, such as circuit systems and single-link inverted pendulum systems.

A Recurrent Neural Network Approach for Constrained Distributed Fuzzy Convex Optimization.

This article investigates a class of constrained distributed fuzzy convex optimization problems, where the objective function is the sum of a set of local fuzzy convex objective functions, and the constraints include partial order relation and closed convex set constraints. In undirected connected node communication network, each node only knows its own objective function and constraints, and the local objective function and partial order relation functions may be nonsmooth. To solve this problem, a recurrent neural network approach based on differential inclusion framework is proposed. The network model is constructed with the help of the idea of penalty function, and the estimation of penalty parameters in advance is eliminated. Through theoretical analysis, it is proven that the state solution of the network enters the feasible region in finite time and does not escape again, and finally reaches consensus at an optimal solution of the distributed fuzzy optimization problem. Furthermore, the stability and global convergence of the network do not depend on the selection of the initial state. A numerical example and an intelligent ship output power optimization problem are given to illustrate the feasibility and effectiveness of the proposed approach.

Between Rhetoric and Reality: An Intertextual Analysis of EU Diversity Charters

This research article will present findings from an intertextual study of EU member states' Diversity Charter (DC) initiatives. With the aid of thematic analysis, we undertook this intertextual analysis of DCs to study and compare their content. We also consider the political and social context in which the DCs operate. In this regard, the research also evaluates how principles set by the EU Charter of Fundamental Rights are enacted in law by member states and why there is a need for expanding human rights guarantees with initiatives like the DC. Our findings conclude with five points about the current state of DC initiatives in EU member states. Furthermore, based on the findings and our professional experience, we note the critical areas of improvement that can be made to the existing DC. The article concludes with our proposal for developing the Reflective Realignment Approach to help companies create a more inclusive work environment.

Predicting the First Steps of Evolution in Randomly Assembled Communities.

Microbial communities can self-assemble into highly diverse states with predictable statistical properties. However, these initial states can be disrupted by rapid evolution of the resident strains. When a new mutation arises, it competes for resources with its parent strain and with the other species in the community. This interplay between ecology and evolution is difficult to capture with existing community assembly theory. Here, we introduce a mathematical framework for predicting the first steps of evolution in large randomly assembled communities that compete for substitutable resources. We show how the fitness effects of new mutations and the probability that they coexist with their parent depends on the size of the community, the saturation of its niches, and the metabolic overlap between its members. We find that successful mutations are often able to coexist with their parent strains, even in saturated communities with low niche availability. At the same time, these invading mutants often cause extinctions of metabolically distant species. Our results suggest that even small amounts of evolution can produce distinct genetic signatures in natural microbial communities.

The Role of Morphological Variation in Evolutionary Robotics: Maximizing Performance and Robustness.

Exposing an evolutionary algorithm that is used to evolve robot controllers to variable conditions is necessary to obtain solutions which are robust and can cross the reality gap. However, we do not yet have methods for analyzing and understanding the impact of the varying morphological conditions which impact the evolutionary process, and therefore for choosing suitable variation ranges. By morphological conditions, we refer to the starting state of the robot, and to variations in its sensor readings during operation due to noise. In this paper, we introduce a method that permits us to measure the impact of these morphological variations and we analyze the relation between the amplitude of variations, the modality with which they are introduced, and the performance and robustness of evolving agents. Our results demonstrate that (i) the evolutionary algorithm can tolerate morphological variations which have a very high impact, (ii) variations affecting the actions of the agent are tolerated much better than variations affecting the initial state of the agent or of the environment, and (iii) improving the accuracy of the fitness measure through multiple evaluations is not always useful. Moreover, our results show that morphological variations permit generating solutions which perform better both in varying and non-varying conditions.

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.

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 &amp;gt;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 (&amp;gt;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.