搜索结果：Computational Theory and Mathematics

Reducing adolescent childbearing is essential for improving sexual and reproductive health and the social and economic well-being of adolescents. Adolescent birth rate is an indicator to monitor progress towards Sustainable Development Goal (SDG) Target 3.7 by 2030, ensuring universal access to sexual and reproductive healthcare services. However, the availability and reliability of data on adolescent fertility rates are low. We produce annual estimates of age-specific fertility rates (ASFR) for girls aged 10-14 and 15-19 from 1950 to 2023 for the world, regions and 201 countries and territories. Based on a comprehensive ASFR database, we estimate the under-20 ASFR with a Bayesian hierarchical time series model, using covariates such as total fertility rates and female educational attainment and adjusting for biases and under-reporting. We calculate the number of births to adolescents by applying rates to the female population in the respective age groups. Between 1990 and 2023, global ASFR for girls aged 10-14 decreased by 76%, from 4.5 (95% uncertainty interval (UI) (4.1 to 5.2)) to 1.1 (95% UI 0.9 to 1.4) births per 1000, while ASFR for girls aged 15-19 fell by 48%, from 74.7 (95% UI 72.5 to 77.1) to 39.0 (95% UI 36.8 to 41.4) births per 1000. In 2023, births to mothers aged 10-14 years were estimated at 347 (95% UI 285 to 454) thousand, and for those aged 15-19 years at 12.2 (95% UI 11.5 to 12.9) million. The number of births among adolescents aged 15-19 in sub-Saharan Africa increased during 1990-2023 due to population growth and slower fertility declines. Despite significant global reduction in adolescent childbearing, regional and national disparities persist. Targeted interventions addressing child marriage, educational inequities and lack of access to reproductive health services will be critical for achieving related SDG targets and advancing gender equality and women's empowerment.

Quantification of drug-cell interactions and subsequent cellular responses by using experimental data together with mathematical models of assumed binding and signalling schematics is vital to many research programmes; data fitting provides estimates for important pharmacological parameters including kinetic parameters controlling drug affinity and efficacy. Ordinary differential equation (ODE) models are a key component of many receptor theory studies used for this purpose. In using ODE simulations to fit experimental data and estimate these parameters, the theory of the identifiability properties of the system is often overlooked. Indeed, structural identifiability analysis (SIA) is often overlooked in many fields of bio-modelling. Building on recent SIA for linear ligand binding models in receptor theory, we present a new analysis of identifiability properties of nonlinear receptor theory models. We include models of ligand depletion in binding assays and ligand-induced dimerisation (LID). The classical SIA approaches of Taylor Series and similarity transformation are applied, using detailed step-by-step calculations to illustrate the complexity of the implementations. New results are obtained which show that the nonlinear ligand-depletion counterpart models of non-identifiable linear ligand excess models are globally identifiable from a single timecourse. Also, the LID model is shown to be globally identifiable if an experimental aparatus-dependent parameter is obtained. The analysis highlights issues of tractability of the methods for similar and higher-dimensional nonlinear models in receptor theory.

Chronic conditions such as cardiovascular disease, diabetes, and cancer require sustained lifestyle changes and self-management, yet traditional care models often provide limited support for long-term behavior change. Digital health technologies, particularly virtual agents, computer-generated characters simulating human-like interactions through verbal and nonverbal cues, offer new ways to provide personalized, scalable, and continuous support. However, the ways in which distinct components within such digital health technologies, including those used in chronic care interventions, are chosen and combined remain underreported. We conducted a systematic scoping review to map how behavior change techniques (BCTs), health data types, and delivery channels are rationalized, combined, and applied in virtual agent-delivered interventions for chronic condition management. The review followed established scoping review frameworks and adhered to PRISMA-ScR reporting guidelines. A search was performed across PubMed, Scopus, PsycInfo, WebofScience, and IEEE Xplore in September 2024. Twenty-one studies met the inclusion criteria. We examined the rationales reported by authors for intervention design, categorized as theory-driven, practice-driven, empirically-driven, mixed, or not explicitly stated. Few studies explained why they selected specific techniques or how health data and delivery channels were intended to interact. Across studies, BCTs were identified but often not explicitly labelled. The most common agent-delivered techniques were self-monitoring, feedback, instruction on how to perform a behavior, and prompts and cues. These techniques were typically supported by subjective self-reports (e.g., symptoms, behaviors), objective data (e.g., step counts, blood pressure), adherence data (e.g., activity completion) and user preference data (e.g., preferred timing of reminders). Delivery channels comprised smartphone or tablet apps. This review provides the first systematic map of how BCT-health data-delivery channel combinations are applied in virtual agent interventions for chronic condition management. It highlights foundational design patterns and reporting gaps, emphasizing the need for transparent, theory-informed reporting to guide future development of adaptive, evidence-based digital health tools.

Mathematical problem-solving is fundamental for academic success and real-life challenges and involves more than procedural and computational skills. As most primary school mathematics problems are text-based, reading comprehension self-efficacy may be closely associated with students' performance in mathematical problem-solving. Grounded in Albert Bandura's social cognitive theory, this study examined the associations between reading comprehension self-efficacy and mathematical problem-solving, focusing on the sequential indirect associations involving of mathematical reasoning and critical thinking. A quantitative, correlational research design was employed. The sample consisted of 518 fourth-grade students from 14 provinces across seven regions of Türkiye. Data were collected using validated scales measuring reading comprehension self-efficacy, critical thinking, mathematical reasoning, and mathematical problem-solving skills. Data were analyzed using correlation analysis and regression-based mediation analysis. The findings revealed that mathematical reasoning and critical thinking partially accounted for the association between reading comprehension self-efficacy and mathematical problem-solving. The findings indicated significant indirect effects through critical thinking skills (β = 0.18, K 2 = 0.23), mathematical reasoning skills (β = 0.04, K 2 = 0.19), and the sequential pathway from mathematical reasoning to critical thinking (β = 0.01, K 2 = 0.03). Although the sequential mediation effect was statistically significant, its effect size was relatively small compared with the individual mediation effects, particularly that of critical thinking skills. These results highlight the interconnected roles of foundational cognitive processes in relation to higher-order mathematical problem-solving skills. The study contributes to the literature by emphasizing the relevance of considering reading comprehension self-efficacy, mathematical reasoning, and critical thinking as interconnected factors in early mathematics education.

People often express opinions that differ from their privately held views, a phenomenon known in economy as preference falsification. Expressed-private opinion (EPO) models capture this by assigning each agent two dynamical variables: a private (internal) and an expressed (external) opinion. Within the nonlinear q-voter model, two EPO variants have been studied so far: with and without self-anticonformity. In both formulations, agents update private and expressed binary opinions, one after another and at the same rate, which has led to two update schemes studied previously: act then think, in which an agent first updates its expressed and then its private opinion, and think then act, in which the order is reversed. To eliminate this ad hoc distinction and quantify the interplay between private and expressed opinions, we introduce the α-EPO q-voter model with asynchronous updating-in each elementary step, an agent updates its private opinion with probability α or its expressed opinion with complementary probability 1-α. We derive mean-field theory and a pair approximation, and validate them with Monte Carlo simulations on artificial and real organizational networks. Comparing the two model variants, we show that the collective outcome controlled by α strongly depends on self-anticonformity: With self-anticonformity, the results are robust to α, whereas without it α shifts the agreement-disagreement threshold and can change the type of phase transition. In the mean-field limit, this change occurs only for q=3, but the pair approximation reveals an additional low-connectivity regime in which both α and the average degree k also control the emergence and width of hysteresis for larger influence groups.

Plant diseases significantly affect agricultural productivity and global food security, while accurate disease identification remains challenging because of uncertain and overlapping visual symptoms in leaf images. Existing deep learning approaches often require large annotated datasets and suffer from limited interpretability in practical agricultural environments. This study presents a multi-parameter improved fuzzy soft set-based framework for plant disease classification using tomato leaf images from the PlantVillage dataset. The objective is to develop an interpretable and reliable classification model capable of handling uncertainty in plant disease patterns through feature-driven fuzzy similarity analysis. The methodology integrates image preprocessing, color and texture feature extraction, variance-based feature weighting, prototype generation using K-means clustering, and fuzzy similarity computation using Mahalanobis distance and Gaussian membership functions. RGB, HSV, and Gray-Level Co-occurrence Matrix (GLCM) features are extracted from standardized leaf images and evaluated within an improved fuzzy soft classification framework. Performance comparison is carried out using machine learning models including Support Vector Machine (SVM), Random Forest (RF), Linear Discriminant Analysis (LDA), and Naive Bayes (NB) implemented in Python using Scikit-learn libraries. Experimental simulation results demonstrate that the proposed framework achieves competitive classification performance while preserving interpretability and robustness under uncertain feature distributions. Performance evaluation is conducted through accuracy analysis, ROC-AUC curves, confusion matrices, ablation studies, and Wilcoxon Signed-Rank statistical testing. The proposed Improved Fuzzy Soft model achieved an accuracy of 88.57% which is less than LDA (94.92%), Random Forest (97.78%) and SVM (97.94%) classifiers. However, in the cross data set validation, the proposed Improved Fuzzy Soft model achieved an accuracy of 67.35% which is greater than LDA (51.02%), Random Forest (51.02%) and SVM (55.10%) classifiers. Statistical validation using the Wilcoxon Signed-Rank Test produced a p-value of [Formula: see text], confirming that the performance difference between the Improved Fuzzy Soft framework and the Random Forest classifier is statistically significant under the current experimental setting.

We present the positron coupled cluster singles and doubles (POS-CCSD) method to calculate positron binding energies in molecules. This framework treats electrons and positrons on an equal footing and includes up to simultaneous double-electron-single-positron excitations. We benchmark the approach by computing binding energies for atomic anions and several polar and non-polar polyatomic systems, comparing the results with independent theoretical studies and, where available, experimental data. The fully converged results for H- are in excellent agreement with quantum Monte Carlo and multi-reference configuration interaction results. Quantitative agreement with experiments is not reached in the present study due to the slow convergence of the binding energy with respect to the size of the orbital bases for the electrons and the positron. However, the POS-CCSD results underscore the critical role of electron correlation in the description of electron-positron systems required for a balanced description of these complex systems. In addition, we examine nuclear relaxation effects following positron attachment in LiH.

Current gene regulatory networks are limited by incomplete functional annotation and the difficulty of inferring causal relationships from expression data. Here we introduce Gene Deregulation Networks (GDNs), a new structure in which a directed link from gene C to gene E indicates that a deregulation of C makes a deregulation of E more probable. GDNs are inferred from expression data using a probabilistic theory of causation, without requiring prior biological knowledge. Using previously defined N- and T-genes with exclusive expression intervals for normal tissue and tumors, respectively, we construct separate GDNs for normal and for tumor tissues. Data are TCGA RNA-Seq bulk profiles from five cancer types. Links are identified via the Loevinger coefficient and pruned with Reichenbach-type and Mokken tests. We then project each sample onto its corresponding Gene Deregulation Network to visualize the deregulation cascades that have occurred. Finally, we define a simple dynamics: spontaneous evolution follows the direction of the GDN edges; interventions (e.g., gene knockdown) acting against spontaneous evolution induce cascades along the reversed network. The GDNs are represented by sparse, directed acyclic graphs. Genes with low deregulation frequency have high out-degrees, suggesting they act as upstream regulators. High-frequency genes have high in-degrees, indicating they are convergence points of cascades. Projecting samples onto the T-GDN reveals that early tumors rely mostly on spontaneous T-gene activations, whereas advanced tumors show wide, branching cascades. The N-GDNs show consistent size and structure across distinct tissues revealing similar protective machinery against tumor formation. In contrast, the T-GDNs quantitatively differ from tissue to tissue indicating different levels of transcriptional reprogramming. Simulated knockdown of EPHA10 and of an 8-gene panel illustrates how the network topology determines whether an intervention can be resisted by the tumor. A reported experiment on POM121 knockdown in two prostate cancer cell lines qualitatively confirms the predicted directionality of cascades. GDNs provide a robust, scalable, and annotation-free framework to understand cancer onset and progression. The separation into N- and T-GDNs, connected by NT-genes, offers a systematic basis for studying carcinogenesis and designing targeted therapies.

Modern bioinformatics faces escalating challenges stemming from both the inherent computational hardness of many fundamental problems and the rapidly growing scale and complexity of biological data, increasingly limiting the effectiveness of classical computational approaches. Quantum computing has emerged as a promising paradigm for addressing these challenges by enabling alternative problem representations and novel search strategies for exploring complex solution spaces. This systematic review provides a structured overview of the emerging field of quantum bioinformatics and aims to supplement recent reviews on this topic in the journal by providing an updated and structured synthesis of current research. We systematically collect and organize existing studies across 10 bioinformatics domains to identify research trends, dominant themes, and recurring methodological patterns. The review examines quantum and hybrid quantum-classical approaches, problem formulations, and encoding strategies, with particular attention to the constraints of noisy intermediate-scale quantum devices, including noise, limited scalability, and the need for error mitigation. We further synthesize reported limitations, open challenges, and prospective research directions.

This study presents a mathematical framework for investigating the dynamics of coexistence and competition among heterotrophic microbes across different time scales. Focusing on metabolic interactions, we examine how three strategies, public metabolizing, private metabolizing and cheating, shape population behaviour. The framework integrates generalized Lotka-Volterra dynamics with evolutionary game theory to capture the effects of resource exchange, particularly glucose made available by public metabolizers and sucrose as a shared substrate driving population growth. Game-theoretic pay-offs encode ecological costs and benefits, enabling analysis of frequency-dependent interactions among strategies. To capture evolutionary realism, we implement laboratory-inspired simulations in which strategies can switch between generations, mimicking mutation or phenotypic plasticity in microbial populations. These eco-evolutionary dynamics reveal conditions under which all three strategies coexist at interior equilibria and show how variation in growth advantages and, illustratively, phenotype-switching perturbations produce evolutionary shifts. Numerical analysis identifies ecological thresholds and fitness asymmetries that determine system robustness, long-term coexistence, and the persistence of a synthetic, cross-kingdom system linked by nutrient exchange. Together, these insights provide general principles for microbial coexistence and offer design guidelines for ecosystem engineering, biotechnological applications and the construction of stable synthetic communities under ecological and evolutionary constraints.

The aim of the present study is to obtain the numerical solutions of the fractional double-strain HIV co-infection model with intercellular delays and stochastic effects (FDS-HIV-IDS) by employing a novel radial basis neural network with Levenberg-Marquardt backpropagation (RBNN-LMB). The nonlinear model accounts for multiple interacting populations, and logistic growth is introduced to describe the interaction between wild-type and drug-resistant HIV strains. The analysis of the model considers threshold criteria for both local and global stability of infection-free, dominant, and coexistence equilibria. The nonlinear model incorporates multiple interacting groups, and its numerical solutions are approximated through the stochastic RBNN-LMB framework. Consequently, double-strain dynamics transition from stability to instability (periodic oscillations to chaos) more frequently and at earlier stages. This also leads to a higher total viral load compared to single-strain scenarios, highlighting the greater risk of treatment failure when resistance emerges. A dataset is generated using the numerical predictor-corrector method, where the data are split into 75% for training and 10% for validation and 15% for testing, in order to minimize the mean square error (MSE). The solver architecture consists of fifteen hidden neurons, a single input structure, and a radial basis activation function to approximate the system dynamics effectively. The accuracy of the method is demonstrated through the overlapping of predicted and reference outputs, while the very small absolute error (AE) values confirm its precision. Furthermore, statistical evaluations using different operators support the reliability and robustness of the proposed approach.

Digital marketing increasingly functions as a complex adaptive system characterised by non-stationary environments, strategic interaction, and multi-agent competition. Programmatic advertising exemplifies this complexity, where decisions must be made in real time under uncertainty. Under such conditions, traditional static optimisation methods often fail to deliver robust performance. This review synthesises bio-inspired computational approaches, reinforcement learning (RL), multi-agent reinforcement learning (MARL), and agentic artificial intelligence (AI) to develop an integrated theoretical perspective on adaptive optimisation in digital marketing. Following PRISMA 2020 guidelines, we conducted a systematic search of peer-reviewed research across six databases: Scopus, IEEE Xplore, ACM Digital Library, SpringerLink, ScienceDirect, and arXiv, supplemented by manual reference checking. Each computational paradigm is explicitly grounded in foundational biological literature, including work on evolution, foraging, swarm intelligence, and immune cognition. Reinforcement learning supports adaptive decision-making through mechanisms closely aligned with operant conditioning and foraging behaviour. Multi-agent reinforcement learning extends these principles to interactive marketing ecosystems via decentralised coordination and swarm-based learning. Agentic AI further advances adaptive capability by introducing goal-directed reasoning, memory, and higher-level decision orchestration. Contributions: The review identifies persistent fragmentation across marketing sub-domains and a lack of formal mathematical grounding for widely used bio-inspired analogies. To address these gaps, the study proposes a multi-layer bio-inspired framework and outlines a structured research agenda to guide the development of autonomous digital marketing systems.

Cognitive neuroscience has accumulated robust findings (e.g., order effects in judgment, multi-path motor preparation, perceptual binding, attentional selection, and the recursive construction of self and time) that systematically resist explanation within classical probabilistic and causal frameworks. We argue that these are not anomalies but signatures of a deeper, non-commutative architecture of cognition and brain dynamics. We propose quantum probability theory together with contextual probability theory, not as metaphorical analogies but as rigorous translational languages for cognitive processes in which observation actively transforms underlying state spaces. Underlying this framework is the conjecture that neural network dynamics in the brain are intrinsically organized to generate quantum-like representations-rather than merely being described by quantum mathematics from the outside. Their structural primitives (i.e., superposition, entanglement, projection, and non-commutativity) map onto premotor population coding, long-range cortical synchrony, prefrontal state dynamics, and default-mode network activity. On this basis we sketch a new sub-domain (namely, Cognitive Structural Science) that treats the geometry and algebra of cognitive state spaces as primary explananda, and outline a research program combining homologous human-macaque experiments with quantum-computer simulation as a constrained testbed.

A second-order modified mean-field theory has been developed to predict the static magnetization and initial magnetic susceptibility of textured ensembles of immobilized interacting superparamagnetic nanoparticles with uniaxial anisotropy. The theory takes into account the orientational texture of the easy magnetization axes and interparticle dipole-dipole interactions, including three-particle correlations. Simple analytical expressions are proposed for calculating the magnetization and initial magnetic susceptibility of ensembles of interacting immobilized particles with random, parallel, and perpendicular texturing of the easy magnetization axes with respect to the applied magnetic field. It is shown that texture manipulation allows the collective magnetic response to be controlled: parallel orientation of the axes leads to a rapid increase in susceptibility, whereas perpendicular orientation leads to its suppression. The results of the theory demonstrate quantitative agreement with the data of computer modeling performed using the Monte Carlo method, significantly exceeding the accuracy of known theories. The theory developed in this work is a tool for predicting and purposeful designing the magnetic properties of modern magnetically active materials.

Traditional asymptotic information-theoretic studies of the fundamental limits of wireless communication systems primarily rely on some ideal assumptions, such as infinite blocklength and vanishing error probability. While these assumptions enable tractable mathematical characterizations, they fail to capture the stringent requirements of some emerging next-generation wireless applications, such as ultra-reliable low latency communication and ultra-massive machine type communication, in which it is required to support a much wider range of features including short-packet communication, extremely low latency, and/or low energy consumption. To better support such applications, it is important to consider finite-blocklength information theory. In this paper, we present a comprehensive review of the advances in this field, followed by a discussion on the open questions. Specifically, we commence with the fundamental limits of source coding in the non-asymptotic regime, with a particular focus on lossless and lossy compression in point-to-point (P2P) and multiterminal cases. Next, we discuss the fundamental limits of channel coding in P2P channels, multiple access channels, and emerging massive access channels. We further introduce recent advances in joint source and channel coding, highlighting its considerable performance advantage over separate source and channel coding in the non-asymptotic regime. In each part, we review various non-asymptotic achievability bounds, converse bounds, and approximations, as well as key ideas behind them, which are essential for providing engineering insights into the design of future wireless communication systems.

The reactivity and function of biopolymers depend on their structure and flexibility. There is a limited number of methods that can be used for their studies in solutions. Among them, Raman optical activity (ROA) provides excellent sensitivity to conformational changes. So far, ROA studies of nucleic acid systems are relatively rare because of the complexity of these molecules and difficulties in interpretation of the spectra. To explore the link between spectral shapes and the structure, and to advance the experimental and computational methodologies, we measured Raman and ROA spectra of four oligonucleotides (polyA, polyC, polyG, and polyU) in a wide wavenumber range. Molecular dynamics (MD) and density functional theory (DFT) were used for the spectra simulations. Temperature-dependent vibrational spectral changes are consistent with melting curves obtained from electronic circular dichroism (ECD). The results show that the spectra well-reflect molecular geometry, including changes caused by temperature variation. Comparison of theoretical and experimental Raman and ROA intensities appears as a convenient way to validate and potentially develop MD force fields; the RNA.Shaw force field provided results superior to the RNA.OL3 one. The combined spectroscopic and computational methodology thus can be used as a powerful means to study solution properties of nucleic acids.

In this article, we study a nonlinear neuron membrane model describing the propagation of action potentials along nerve fibers, incorporating nonlinear elastic effects and higher-order dispersion. By applying the Hirota bilinear transformation, the given equation is converted into an equivalent bilinear form, which provides a suitable analytical framework for systematic construction of exact solutions. To enrich the functional solution space, we introduce a bilinear neural network method (BNNM), where neural network architectures are used as structured symbolic generators rather than numerical approximators. Both single-hidden-layer and double-hidden-layer configurations are constructed to generate exact analytical solutions. Through symbolic coefficient matching assisted by MAPLE, multiple admissible parameter sets are obtained. The presented framework yields a diverse family of exact wave structures, involving lump solutions, breather-type oscillatory waves, soliton-lump interaction states, double-period lump superpositions, three-wave interaction patterns, and hybrid lump-rogue wave excitations. The derived solutions are expressed in compact Hirota form and signified via three-dimensional, density, and contour visualizations, revealing strong spatial localization, temporal modulation, nonlinear energy redistribution, and coherent phase-locked propagation. The results represent that the neural-bilinear approach offers a powerful and systematic mechanism for constructing rich nonlinear wave families in neuron-type models. These analytical structures contribute to understanding localized pulse transmission, multi-wave interaction dynamics, and transient amplification phenomena in excitable biological media.

Resistance distance, originating from electrical network theory, provides a powerful framework for characterizing the structural and topological properties of chemical graphs. The objective of this study is to compute and analyze resistance-distance metrics for two distinct classes of DNA networks, with the aim of gaining deeper insight into their underlying molecular topology. Each DNA network is modeled as an electrical network by replacing every edge with a unit resistor. Techniques from classical electrical network theory are employed to derive resistance distances between selected pairs of vertices. Analytical simplification and elimination principles are used to obtain closed-form expressions for these distances. Explicit resistance distance formulas are obtained for representative vertex pairs in both classes of DNA networks. The results reveal how network topology, connectivity patterns, and structural variations influence effective resistance, highlighting clear distinctions between the two DNA network classes. The findings demonstrate that resistance distance effectively captures both local and global structural features of DNA networks. These results enhance the understanding of how topological complexity impacts electrical and structural behavior in molecular graphs, offering meaningful interpretations within chemical graph theory. This study provides a comprehensive resistance distance-based characterization of two classes of DNA networks. The results contribute to the broader understanding of structural flexibility and stability in molecular systems and suggest potential applications in chemical and biological engineering. MSC subject classification: 05C50, 05C90.

Digital wellness interventions for preventive health, such as prediabetes management, often face high dropout rates due to insufficient personalisation and motivational support. This design science research study presents a self-determination theory (SDT)-informed gamification framework to address these challenges. Drawing on a systematic literature review, the study identifies engagement barriers, facilitators, and empirical evidence to guide design. The framework maps strategies to SDT's core constructs: autonomy (user-defined goals with structured guidance and selectable difficulty levels), competence (adaptive challenges with badges and points), and relatedness (collaboration-focused progress sharing). The framework was co-designed with 20 young adults in Australia and validated by six multidisciplinary experts. The study revealed that 60% of participants felt overwhelmed with fully open goal setting, highlighting that structured guidance is essential for autonomy support in preventive wellness applications. These findings informed the development of MiCARE, a progressive web app that operationalises the framework with user-centred, motivationally aligned features. This study offers a replicable, theory-driven approach for designing engaging preventive wellness interventions.