State Transition Research Articles

AI-Based Identity Verification Through Behavioral Biometrics: Enhancing Remote Work Security

With the increased adaptability of remote work patterns, secure and efficient identity verification has also become a paramount concern. The traditional model of authentication involving the use of passwords or security tokens fails to provide any continuous verification of the user and is also prone to hacking attempts. Behavioral biometrics, especially keystroke dynamics and mouse movement patterns, offer an effective alternative as they allow for unobtrusive user authentication that is based on the individual user's behavior and is therefore continuous. This paper explores the application of Long Short-Term Memory (LSTM) networks, a sequence-based AI model, for studying and differentiating behavioral biometrics. We are using freely available data sets of keystroke and mouse dynamics to design and test an LSTM based system which is capable of making a distinction between users and imposters. We have shown that LSTM networks are significantly better in handling time series data such as state transition sequences than other statistical machinery traditional machine learning methods, such as Random Forests and Support Vector Machines, where they scored 89% accuracy between normal user operations and abuse activity. This aspect of the research addresses the reason why LSTM networks are appropriate for live remote identity verification systems, which is their ability to learn long strips of sequences and the behavioral flow. The originality of the study is to expand secure authentication solutions based on artificial intelligence systems that can be integrated to facilitate remote working in any sector for enhanced security.

Charge Transfer‐Driven Conversion of Molecular Oxygen to Doublet State on Vanadium Diselenide (VSe2) Surface at Room Temperature

AbstractOxygen in the excited state is essential for organic synthesis and medical treatment. Herein, a novel phenomenon is reported in which the magnetic ground state of molecular oxygen undergoes a transition at room temperature from S = 1 to S = 1/2, corresponding to the transition of O2 from a triplet to a doublet state after stable physical adsorption on the defect‐free surface of bulk VSe2. This density functional theory (DFT) calculations demonstrate the stable physical adsorption of O2 on both 1T‐ and 2H‐VSe2 surfaces without further decomposition. Electron spin resonance (ESR) measurements confirm the spin state transition. Theoretical simulations reveal the charge transfer from entangled V‐3d and Se‐4p bands to oxygen as the leading cause of the spin state transition. This mechanism has not been previously proposed and offers multiple potential applications, from organic synthesis to medicine. Moreover, this approach can be extended to reveal new aspects of known catalytic materials and to design novel catalysts.

Caveolin-1 regulates context-dependent signaling and survival in Ewing Sarcoma.

Cellular plasticity is a hallmark function of cancer, but many of the underlying mechanisms are not well understood. We uncover Caveolin-1, a scaffolding protein that modulates plasma membrane domain organization, as a context-specific regulator of survival signaling in Ewing sarcoma (EwS). Single cell analysis reveals a distinct subpopulation of EwS cells, which highly express the cell surface marker CD99 as well as Caveolin-1. CD99 High cells isolated through flow cytometry differ from CD99 Low cells in morphology, gene expression, and survival capabilities in vivo. Our work demonstrates that elevated Caveolin-1 expression in these cells plays a key role in the regulation of PI3K/AKT survival signaling, through subcellular organization of PI3K activity at the cell surface. We thus propose a model where the CD99 High state develops a Cav-1 controlled signaling network to regulate cell survival that is distinct from the AKT-agnostic survival of CD99 Low cells. Overall, this work identifies a state transition of EwS cells and uncovers Caveolin-1 as a driver of survival signaling in a context-dependent manner.

A Novel Spherical Shortest Path Planning Method for UAVs

As a central subdivision of the low-altitude economy industry, industrial and consumer drones have broad market application prospects and are becoming the primary focus of the low-altitude economy; however, with increasing aircraft density, effective planning of reasonable flight paths and avoiding conflicts between flight paths have become critical issues in UAV clustering. Current UAV path planning often concentrates on 2D and 3D realistic scenes, which do not meet the actual requirements of realistic spherical paths. This paper has proposed a Gradient-Based Optimization algorithm based on the State Transition function (STGBO) to address the spherical path planning problem for UAV clusters. The state transition function is applied on the scale of medium and high-dimensional cities, balancing the stability and efficiency of the algorithm. Through evolution and comparisons with many mainstream meta-heuristic algorithms, STGBO has demonstrated superior performance and effectiveness in solving Medium-Altitude Unmanned Aerial Vehicle (MUAV) path planning problems on three-dimensional spherical surfaces, contributing to the development of the low-altitude economy.

Quantum Chemical Determination of Molecular Dye Candidates for Non-Invasive Bioimaging.

Molecular dyes containing carbazole-based π bridges and/or julolidine-based donors should be promising molecules for intense SWIR emission with potential application to molecular bioimaging. This study stochastically analyzes the combinations of more than 250 organic dyes constructed within the D-π-D (or equivalently D-B-D) motif. These dyes are built from 22 donors (D) and 14 π bridges (B) and are computationally examined using density functional theory (DFT). The DFT computations provide optimized geometries from which the excited state transition wavelengths and associated oscillator strengths and orbital overlaps are computed. While absorption is used as a stand-in for emission, the longer the absorption wavelength, the longer the emission should be as well for molecules of this type. Nearly 100 novel dyes reported in this work have electronic absorptions at or beyond 1200 nm, opening the possibility for future synthesis and experimental characterization of new molecular dyes with promising properties for bioimaging.

On Characterization of Algebraic Structure of Fuzzy Multiset Finite Automata

This paper enriches the theory of computing in the context of fuzzy sets and multisets. We have studied the computational model fuzzy multiset finite automaton (FMFA), which incorporates uncertainty about the next state of transition and multiple occurrences in the input set of automaton, and can be viewed as a generalized version of both classical automaton and fuzzy automaton. We have discussed the algebraic characterization of subautomaton of a FMFA, separatedness, strong connectedness and cyclic, directable and triangular FMFA in terms of equivalence classes induced by an equivalence relation defined on the state set of FMFA. Interestingly, for a given FMFA, a new FMFA is constructed which is a homomorphic image of the original FMFA. We have defined different poset structures associated with a given FMFA and showed that some of them are upper semilattices and discussed the inter-relationship of a given poset, a finite upper semilattice (FUSL), a FMFA and a poset/FUSL associated with a given FMFA. Finally, we have introduced the concept of decomposition of a FMFA in two different ways and characterized the strongly connected, triangular and directable FMFA.

Enhancing Near‐Infrared Absorption in Osmium(II) Complexes Under the Cumulative Influence of Terpyridine Ligand and Anions: Combined Experimental and DFT/TD‐DFT Investigation

AbstractThis work presents synthesis, characterization, and thorough examination on the photophysical and electrochemical behaviours of a new class of homo‐ and heteroleptic Os(II)‐terpyridine complexes. The compounds exhibit intense singlet metal‐to‐ligand charge‐transfer (1MLCT) bands in the visible region (490–500 nm) and weak and broad spin‐forbidden bands in the spectral window of 670–680 nm due to singlet ground state (1GS) to 3MLCT state transition, facilitated by spin‐orbit coupling. The complexes exhibit strong luminescence within 730–750 nm at room temperature having lifetimes in the domain of 101–158 ns, depending upon solvents. Taking advantage of active methylene protons, anion‐induced modulation of spectral and electrochemical characteristics of the complexes has been convincingly demonstrated via different optical channels and spectroscopic tools as well as by cyclic voltammetry. The augmentation in absorption spectral characteristics is quite remarkable in presence of F− and intense absorption bands spanning across the entire visible region and stretching up to NIR domain is achieved. The mode of complex‐anion interplay was elucidated via 1H, 31P{1H} and 19F NMR spectroscopy as well as by computational investigations using DFT and TD‐DFT methods. The complexes also act as molecular sensors and switches that can function in visible and NIR domains upon alternate addition of anion and acid.

Navigating Nongenetic Plasticity in Cancer Drug Resistance

Nongenetic plasticity has emerged as a key driver of cancer drug resistance, yet its precise origins, nature, and consequences are not fully clarified. This review examines technological, computational, and conceptual developments in the nongenetic determinants of drug resistance. We begin by proposing refined definitions of cellular state, fate, and plasticity. We subsequently contextualize the findings from multimodal approaches to investigate plasticity, highlighting how new single-cell lineage-tracing methods provide opportunities for quantitatively capturing state transitions, their timescales and heritability, and how they contribute to resistance mechanisms. We also draw parallels with concepts from developmental biology and microbial persistence research. Next, we cover the role that computational approaches have played in revealing the otherwise latent patterns of heterogeneity that underlie plasticity from complex datasets. We conclude by emphasizing the need for standardized terminology in this rapidly evolving field and the path from bench to bedside.

Deconstructing the Mapper algorithm to extract richer topological and temporal features from functional neuroimaging data.

Capturing and tracking large-scale brain activity dynamics holds the potential to deepen our understanding of cognition. Previously, tools from topological data analysis, especially Mapper, have been successfully used to mine brain activity dynamics at the highest spatiotemporal resolutions. Even though it is a relatively established tool within the field of topological data analysis, Mapper results are highly impacted by parameter selection. Given that noninvasive human neuroimaging data (e.g., from fMRI) is typically fraught with artifacts and no gold standards exist regarding "true" state transitions, we argue for a thorough examination of Mapper parameter choices to better reveal their impact. Using synthetic data (with known transition structure) and real fMRI data, we explore a variety of parameter choices for each Mapper step, thereby providing guidance and heuristics for the field. We also release our parameter exploration toolbox as a software package to make it easier for scientists to investigate and apply Mapper to any dataset.

Dynamic Modeling of Money Supply and GDP: A State-Space Approach

Using data from 2008 to 2023, this study investigates the dynamic relationship between money supply (M2) and GDP through a state-space modeling approach. Addressing the question of how effectively money supply impacts GDP, the study is grounded in the theoretical framework that monetary policy influences economic output, a concept drawn from classical and Keynesian economic theories. The data, sourced from official economic reports, highlights significant variability in both M2 and GDP, with GDP exhibiting slightly lower volatility. The distributional characteristics of the variables show a slight positive skew and a flatter-than-normal distribution, suggesting potential deviations from typical economic trends. The state-space model captures the relationship between M2 and GDP, indicating that money supply is an important explanatory factor for GDP. While the model’s observation equation shows a low and statistically significant standard deviation of the error term—suggesting that it explains variations in GDP effectively—the state transition equation reveals a mostly deterministic evolution of unobserved state variables, though this result lacks full statistical significance and warrants cautious interpretation. The log-likelihood value supports a fair model fit, yet additional macroeconomic variables or alternate model structures may enhance the analysis. The findings affirm the critical role of money supply in economic growth, consistent with economic theory. However, future research could explore more complex dynamics, including non-linear relationships and the effects of external shocks, to refine the understanding of the M2-GDP nexus. This study contributes empirical insights using a state-space framework, offering valuable implications for monetary policy and macroeconomic forecasting.

Fuzzy‐Markov Routing Policy and Priority‐Based Load Balancing for Cognitive Radio Ad Hoc Networks

ABSTRACTA cognitive radio ad hoc network (CRAHNs) is an infrastructure‐free network containing a cognitive radio, which can use the spectrum resources effectively through an adaptive change of parameter settings and decision‐making. Hence, it enhances the spectrum efficacy for satisfying the growing mobile traffic requests. In multihop CRAHNs, in order to ensure a reliable transmission and energy efficiency, the routing protocol should consider the metrics of channel quality, link stability, and energy consumption. In this paper, a Fuzzy‐Markov routing policy and priority‐based load balancing algorithm (FMRP‐PLB) for CRAHNs is designed. In this protocol, a combined routing metric is derived using delay, energy rate, and link expiration time metrics. For modeling the channel state, a Fuzzy‐Markov decision model is designed, in which the channel states and spectrum availabilities of SU are modeled as state transition matrices. Then, optimum routing decisions are made using the combined routing metric and the predicted transmission success probability. A load balancing metric (LBM) is derived in terms of available link capacity, remaining buffer size of node, and back‐off delay. During data transmission, based on the traffic priority and measured levels of LBM, the optimum paths are selected. Simulation results using NS2 suggest that FMRP‐PLB attains an increased packet delivery ratio with reduced delay and energy consumption.

Measurement-induced state transitions in dispersive qubit-readout schemes

Measurement-induced state transitions in dispersive qubit-readout schemes

First evidencing of guest‐induced spin transition for dinuclear Fe(III) complex supported by calix[4]arene Schiff base ligand

For the first time the partial low spin (LS) ‐ high spin (HS) state transition back, caused by trapped guest molecule release from the macrocyclic cavity of calix[4]arene ligand, decorated with salicylideneamine coordinating binding sites, was evidenced for new dinuclear [FeIII2] diamond core cluster, displaying mixed cis‐/trans‐N2O4 coordination environment for metal centers. Such unique spin state transition behavior, triggered by the changing of calix[4]arene backbone conformation, giving rise to a new paradigm of fine‐tuning spin‐dependent functionality in materials design, was unambiguously established by the 57Fe Mössbauer spectroscopy in combination with the single crystal X‐ray diffraction, IR, TGA‐DSC analysis.

A reversible positive and negative photoconductivity behavior modulated by polarization effect

Negative photoconductive devices exhibit a significant reduction in conductivity under illumination, enabling high-contrast optical modulation and promising great potential in optics. In this study, an ITO/BFCO/TiO2/FTO device was simply fabricated through a synergistic application of sol-gel and magnetron sputtering techniques. Interestingly, the ferroelectric polarization can be reversibly modulated by a low bias voltage in the ITO/BFCO/TiO2/FTO device, thereby exhibiting bipolar voltage dependent reversible behavior of positive and negative photoconductivity. This mechanism has been discussed, where the combined effect of illumination and applied voltage induces internal polarization reversal in Bi2FeCrO6, leading to a transition in the resistance state from low to high. This transition results in an elevation of the energy band, thereby impeding the migration of charge carriers. Additionally, femtosecond laser transient absorption spectroscopy reveals redshift in the initial absorption peak, indicating changes in the band structure induced by illumination, crucial for understanding carrier dynamics in a non-equilibrium state.

Organophosphate Hydrolysis by a Designed Metalloenzyme: Impact of Mutations Explained.

The efficient design of novel enzymes has been attainable only by a combination of theoretical approaches and experimental refinement, suggesting inadequate performance of de novo design protocols. Based on the analysis of the evolutionary trajectory of a designed organophosphate hydrolase, this work aimed at developing and validating the improved theoretical models describing the catalytic activity of five enzyme variants (including wild-type as well as theoretically derived and experimentally refined enzymes) performing the hydrolysis of diethyl 7-hydroxycoumarinyl phosphate. The following aspects possibly important for enzyme design were addressed: the level of theory sufficient for a reliable description of enzyme-reactant interactions, the issue of ground state (GS) destabilization versus transition state (TS) stabilization, and the derivation of the proper side chain rotamers of amino acid residues. For enzyme variants analyzed herein, differential transition state stabilization (DTSS, i.e., preferential TS binding by an enzyme over the GS binding) calculated with a non-empirical model of the interaction energy (i.e., multipole electrostatic plus approximate dispersion terms, MED) displayed a superior performance in ranking the enzyme catalytic activity. The MED DTSS-based systematic rotamer refinement performed with an efficient scanning procedure and accounting for long-range interaction energy terms is an important step capable of unlocking the full potential impact of the given residue that could be otherwise overlooked with a conventional static approach featuring optimization to the nearby minimum. While TS stabilization is the main factor contributing to the increased catalytic activity of the de novo-designed variant studied in this work, directed evolution refinement appears to impact the catalytic activity of another enzyme variant analyzed herein via GS destabilization.

Modern political myth: socio-ontological analyses

Relevance. The appeal to the topic of socio-ontological research of political myth is caused by the fact that in the conditions of rapidly becoming more complicated socio-political reality and the emergence of qualitatively new entities of social and political existence, conceptually holistic approaches to the study of political myth are practically not covered in Russian and world literature against the background of many political science, linguistic and socio-cultural studies.The purpose is to reveal the ontological essence of the political myth within the framework of its theoretical and practical application, taking into account the specifics of the social processes taking place in modern world politics.Оbjectives: to study the history of formation and the main approaches to the definition of the concept of "political myth"; to give a refined definition of the concept of "political myth" within the framework of our research; to conduct a categorical analysis of individual aspects of modern political reality; to formulate recommendations on the socio-ontological interpretation of modern political myth and work with it in world politics, diplomacy and public life.Methodology. Categorical-logical, system-structural, dialectical method, as well as hermeneutical and semantic analysis were used as a methodological basis.Results. The principles, strategies and approaches of social ontology in relation to the modern realities of world politics are analyzed.Conclusions. The results of our analysis have shown that the phenomenon of political myth has its own history of formation and study mainly in its connection with sociological, political science, socio-philosophical, socio-psychological and psycholinguistic studies. In the framework of this study, through the prism of social ontology, we consider both the dynamics and invariants of the processes of mythologization and myth-making, which are in a state of transition from "unipolarity" to "multipolarity". This is how we come to understand the political myth as a system of attributive and modal "coordinates" that define a model of a socio-political phenomenon or process in the context of power relations (co) subordination. The results obtained can be applied in lawmaking, diplomatic activities, "demythologization" and "peremythologization", teaching various courses, for example, on social and political philosophy.

Admission control algorithm for visible light communication random access network under delay and jitter constraints

Abstract This paper proposes a random access network admission control algorithm under delay and jitter constraints to efficiently guarantee the quality of service of a visible light communication system. The multi-packet reception technology is considered to enhance network service, and the interrupted Bernoulli process is used to model the arrival process of packets. The queuing model of the system is constructed from the perspective of a single transmission link, and this paper derives the probability of successful transmission for the terminals. The delay and jitter performance are evaluated according to the state transition matrix and Little’s law. The article set up the optimization problem with the goal of maximizing the throughput and the constraint of transmission probability. To solve the optimization problem better, we improve the tunicate swarm algorithm. Moreover, an admission control algorithm is designed under delay and jitter constraints. Simulation results demonstrate the influence of different parameters on the algorithm.

COMPARATIVE ANALYSIS OF TRIANGULAR FUZZY HIDDEN MARKOV MODELS AND TRADITIONAL HIDDEN MARKOV MODELS

This study optimizes Traditional Hidden Markov Models (THMMs) using Triangular Fuzzy Membership Functions, resulting in Triangular Fuzzy Hidden Markov Models (TFHMMs) that tolerate ambiguous observations and gradual state transitions in agricultural data prediction. Using oilseed area data from 1992 to 2022, we compare TFHMMs to traditional HMMs, focusing on predicting accuracy using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Corrected Akaike Information Criterion (AICc), and Hannan-Quinn Information Criterion (HQIC). Our research includes stationary parameters to improve model stability and uses the Viterbi technique to find optimal state sequences, which improves forecast interpretability. The results show that THMMs outperform TFHMMs at capturing the complicated patterns of agricultural data, with lower prediction errors and more reliability. This work emphasizes the potential of fuzzy logic in not improving probabilistic models for agricultural forecasting, providing a more delicate and accurate approach to analyzing and predicting agricultural trends.

Generative dynamical models for classification of rsfMRI data.

The growing availability of large-scale neuroimaging datasets and user-friendly machine learning tools has led to a recent surge in studies that use fMRI data to predict psychological or behavioral variables. Many such studies classify fMRI data on the basis of static features, but fewer try to leverage brain dynamics for classification. Here, we pilot a generative, dynamical approach for classifying resting-state fMRI (rsfMRI) data. By fitting separate hidden Markov models to the classes in our training data and assigning class labels to test data based on their likelihood under those models, we are able to take advantage of dynamical patterns in the data without confronting the statistical limitations of some other dynamical approaches. Moreover, we demonstrate that hidden Markov models are able to successfully perform within-subject classification on the MyConnectome dataset solely on the basis of transition probabilities among their hidden states. On the other hand, individual Human Connectome Project subjects cannot be identified on the basis of hidden state transition probabilities alone-although a vector autoregressive model does achieve high performance. These results demonstrate a dynamical classification approach for rsfMRI data that shows promising performance, particularly for within-subject classification, and has the potential to afford greater interpretability than other approaches.

Cost-effectiveness of camrelizumab plus rivoceranib for advanced hepatocellular carcinoma in the context of regional disparities in China.

To assess the cost-effectiveness of combining camrelizumab with rivoceranib versus sorafenib as initial treatment options for advanced hepatocellular carcinoma (HCC) across different developmental regions in China. Utilizing TreeAge Pro and data from the phase III randomized CARES-310 clinical trial, a model based on Markov state transitions was developed. Health state utility values were derived from the CARES-310 trial, and direct medical costs were obtained from relevant literature and local pricing data. The primary outcome measured was the incremental cost-effectiveness ratio (ICER), defined as the cost per additional quality-adjusted life year (QALY) gained per person. The ICERs were compared against the willingness-to-pay (WTP) thresholds of different regions in China, including low-income ($16,426.80), medium-income ($34,319.01), and high-income regions ($81,036.63). Sensitivity analyses were also conducted to assess the robustness and reliability of the model under various assumptions. A tornado diagram was used to illustrate the impact of parameter variations on the model's cost-effectiveness. For base case analysis, QALYs per person for the cohort receiving sorafenib were 0.91, with a corresponding cost of $8,860.97. For the cohort receiving camrelizumab plus rivoceranib, the QALYs per person were 1.71, with a corresponding cost of $16,190.72. The camrelizumab plus rivoceranib treatment group exhibited an increase of 0.80 QALYs and an additional expenditure of $7,329.75. The calculated ICER was $9,150.75 per QALY, which is below the WTP thresholds for all regions in China. The camrelizumab plus rivoceranib regimen is regarded as highly cost-effective in medium-income areas of China, with a probability of 99.9%. In high-income regions, the probability reaches 100.0%. Even in low-income regions, this regimen is considered 95.6% cost-effective. Sensitivity analysis further verified that these findings were robust across various assumptions. The combination of camrelizumab and rivoceranib as a treatment strategy not only improves health outcomes but also represents a cost-effective option across different developmental regions in China.