Abstract: Biological cognition depends on learning structured representations in ambiguous environments. Computational models of structure learning typically overlook the temporally extended dynamics that shape learning trajectories under such ambiguity. In this paper, we reframe structure learning as an emergent consequence of constraint-based dynamics. Informed by a literature on the role of constraints in complex biological systems, we build a framework for modelling constrain-based dynamics and provide a proof-of-concept computational cognitive model. The model consists of an ensemble of components, each comprising an individual learning process, whose internal updates are locally constrained by both external observations and system-level relational constraints. This is formalised using Bayesian probability as a description of constraint satisfaction. Representational structure is not encoded directly in the model equations but emerges over time through the interaction, stabilisation, and elimination of components under these constraints. Through a series of simulations in environments with varying degrees of ambiguity, we demonstrate that the model reliably differentiates the observation space into stable representational categories. We further analyse how global parameters controlling internal constraint and initial component precision shape learning trajectories and long-term behavioural alignment with the environment. The results suggest that constraint-based dynamics offer a viable and conceptually distinct foundation for modelling structure learning in adaptive systems. We further analyse how global parameters controlling internal constraint and initial component precision shape learning trajectories and long-term behavioural alignment with the environment. We show that this allows to capture structure learning even in cases where it is maladaptive, such as delusion-like belief updating.

Abstract: The constant external electric field, applied to a semiconductor nanostructure, changes the symmetry. It can be cylindrical symmetry, for the field parallel to the z-axis, or a symmetry breaking, for the field parallel to the x-y plane. The symmetry changes affect the optical properties of the system, which are subject of the presented considerations. Below we present a theoretical calculation of optical functions for CdSe Nanoplatelets with excitons, in an external homogeneous electric field. We consider various configurations, with the external field perpendicular and parallel to the platelet planes. With the help of the real density matrix approach, we calculate the linear electro-optical functions of CdSe nanoplatelets, taking into account the effect of a dielectric confinement on excitonic states. The impact of platelet geometry (thickness, lateral dimension), and on the applied field strength, on the spectrum, is discussed.

Abstract: The smart agriculture system requires high efficiency to automatically maximize crop yields and minimize losses. Wireless sensor networks (WSNs) are essential for maintaining system sustainability through sensing and connectivity. However, they encounter challenges related to cost, interoperability, and reliability. Efforts have been made to expand sensing capabilities while managing costs and addressing variability in sensor communication and power consumption. Despite these efforts, a comprehensive solution—especially for orchard fields—remains undeveloped. This study introduces a coordinated WSN design to optimize sensing and connectivity in agricultural fields. We employ an integrated sensing and connectivity (ISAC) strategy to create a complete solution. Our hybrid approach combines graphical computation with distance-vector algorithms for reliable, cost-effective deployment. Additionally, resilient connectivity is achieved through effective channel modeling and adaptive beamforming. The proposed method, combined with quantitative heterogeneous network selection using MLR-AHP, addresses interoperability issues and improves network resilience. Results indicate improved sensor placement and wireless ranking, even with only 5 nodes. The solution extends sensor battery life, maintains 99% coverage, and empirical tests validate its effectiveness for designing and deploying WSNs in orchard fields.

Abstract: Background/Objectives: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide and is frequently associated with chronic hepatitis C virus (HCV) infection. Early prediction of HCC in HCV patients remains challenging due to complex clinical patterns. This study aims to develop and evaluate machine learning models for the early prediction of hepatocellular carcinoma in patients with HCV. Methods: Clinical and laboratory data from HCV patients were analyzed using a machine learning–based framework. The dataset was preprocessed, and relevant features were selected prior to model development. Six supervised machine learning algorithms—CatBoost, XGBoost, LightGBM, Gaussian Naive Bayes, Extra Trees, and Random Forest—were implemented. Hyperparameter optimization was performed using the Optuna framework. Model performance was assessed using standard evaluation metrics, including accuracy, precision, recall, and F1-score. Results: The experimental results demonstrate that machine learning techniques can effectively identify patterns associated with the progression to hepatocellular carcinoma in HCV patients. Among the evaluated models, ensemble-based algorithms achieved the highest predictive performance, outperforming baseline approaches across multiple evaluation metrics. Conclusions: The findings confirm that machine learning models can serve as valuable decision-support tools for the early detection of hepatocellular carcinoma in patients with HCV. Integrating such models into clinical workflows may enhance early diagnosis and improve patient outcomes. Future work will focus on expanding the dataset and validating the models in real-world clinical settings.

Abstract: Carbon-based bifunctional oxygen electrocatalysts with rationally designed architectures are essential for high-performance rechargeable zinc-air batteries (ZABs), yet the concurrent optimization of catalytic activity, durability, and mass transport remains challenging. Herein, hierarchical ZnCo carbon nanofibers/carbon nanotubes (CNFs@CNTs) are fabricated via single-nozzle electrospinning followed by melamine-assisted pyrolysis under a ZnCl₂-regulated atmosphere. During thermal treatment, Co species embedded within carbon nanofibers catalyze in situ carbon nanotube growth, while ZnCl₂ vapor modulates the carbonization process and surface chemistry, collectively generating a hierarchical CNFs@CNTs architecture with high surface area and abundant exposed active sites. As a result, ZnCo CNFs@CNTs exhibit outstanding bifunctional ORR/OER activity, surpassing Zn-free and Co-free counterparts. Combined structural and electrochemical analyses reveal that the synergistic interaction between Co active centers and Zn-assisted carbon structural regulation enhances reaction kinetics and long-term stability. When implemented as air electrodes in rechargeable ZABs, ZnCo CNFs@CNTs deliver high power density, reduced charge-discharge polarization, and excellent cycling durability, demonstrating strong practical applicability. This work presents an effective strategy for constructing hierarchical CNFs@CNTs composites via electrospinning and dual-component thermal regulation, offering new insights into the design of high-efficiency bifunctional air electrodes for advanced ZABs.

Abstract: Sustainable Development Goal 6 underscores the global commitment to universal and equitable access to safe and affordable drinking water by 2030. Polycyclic aromatic hydrocarbons (PAHs) are persistent organic pollutants of major environmental concern due to their toxicity, mutagenicity, and carcinogenicity, posing risks to ecosystem integrity and human health. Despite these concerns, data on PAH contamination in community water systems within rapidly urbanizing regions of sub-Saharan Africa remain scarce. This study evaluated the occurrence, distribution, and bioaccumulation of PAHs in the Ijegun–Egba River, Lagos State, Nigeria. Physicochemical properties of water samples were determined using standard analytical methods, while PAH concentrations in water, sediments, and fish tissues were quantified by gas-chromatography with flame ionization detection. Visual and organoleptic characteristics, temperature, and pH (7.15) complied with World Health Organization guidelines; however, electrical conductivity (7,245 µS/cm) exceeded recommended limits, indicating significant anthropogenic influence. PAH concentrations were low in the water column but markedly elevated in sediments and aquatic biota, with the highest levels detected in fish gills, followed by sediments and muscle tissues. The contaminant profile was dominated by low-molecular-weight PAHs, particularly fluoranthene (~30%) and pyrene (~15%), along with naphthalene, anthracene, phenanthrene, acenaphthylene, acenaphthene, fluorene, and chrysene. This pattern reflects sediment-associated accumulation and trophic transfer, raising concerns about chronic exposure risks for communities dependent on the river for fisheries and domestic use. Oil depot operations and jetty-related activities were identified as major contamination sources. The findings highlight the need for strengthened monitoring, regulatory enforcement, and mitigation strategies to safeguard aquatic ecosystems and public health.

Abstract: In the field of geophysics, several erroneous theories were long accepted as fundamental laws and formulas. Recent corrections to these misconceptions have been made possible through the application of Exploratory Data Analysis (EDA). This article outlines how EDA contributed to these breakthroughs and provides a brief guide for those interested in adopting this approach. In addition, while introducing new data analysis methods based on EDA, I will also discuss the remaining challenges that warrant further clarification.

Abstract: The increasing availability of high-resolution gridded meteorological data poses significant challenges for efficient storage and rapid data access. This study proposes an adaptive variable-resolution storage (AVRS) strategy for gridded meteorological datasets, in which the spatial resolution of data blocks is dynamically adjusted according to local feature characteristics. Composite radar reflectivity (CREF) data are employed as a representative case to evaluate the performance of the proposed method. The AVRS approach partitions the data into fixed-size spatial blocks and assigns multiple resolution levels based on block-level statistical properties, enabling high-resolution preservation in feature-intensive regions while applying coarser resolution in spatially homogeneous areas. Experimental results indicate that the proposed strategy achieves effective storage reduction, with compression ratios ranging from 11.60% to 14.44% of the original data volume. Despite the substantial reduction in storage size, high reconstruction accuracy is maintained. The MSE ranges from 0.74 to 1.54, with RMSE values between 0.86 and 1.24, while the MAE remains low (0.10–0.22). The PSNR consistently exceeds 34.90 dB, with an average value above 37 dB, demonstrating limited information loss and good structural preservation. In addition, the AVRS strategy significantly improves query efficiency, reducing the average query time from 0.5 s for fixed-resolution storage to 0.2 s. Overall, the proposed method provides a practical and efficient solution for managing large-scale gridded meteorological data in atmospheric research and operational applications.

Abstract: Quantum entanglement is conventionally characterized as a structural property of tensor-product Hilbert spaces, with limited emphasis on its geometric or gauge-theoretic organization. Within standard quantum field theory on a fixed classical spacetime background, we show that entanglement can be represented as a global compatibility constraint acting on internal degrees of freedom in the gauge-bundle formulation of the Standard Model. This representation is encoded by a vacuum-level internal gauge structure $\Xi(x)$ that is locally pure gauge, dynamically inert, and acts exclusively on internal fibers, leaving all local dynamics and the Standard Model Lagrangian unchanged. We formalize this perspective as a vacuum internal gauge structure (VIGS) and prove a corresponding structural result—the Vacuum Internal Gauge Theorem—which establishes that global compatibility relations associated with $\Xi$ act only on internal Hilbert-space factors and preserve locality and no-signaling within the regime considered here. The framework complements standard Hilbert-space and algebraic descriptions of entanglement by making explicit how global internal correlations can be organized geometrically without invoking nonlocal dynamics. Finally, we identify an experimentally accessible operational signature, based on correlated versus independent internal-frame scrambling, that distinguishes this geometric representation within existing entanglement platforms.

Abstract: Digital messaging applications structure everyday work in China, with WeChat often used via employees’ personal accounts, organisational communication becomes merged with private life. This study uses interpretative phenomenological analysis to examine how workplace cyberbullying (WCB) is experienced and understood in routine WeChat-mediated work. Nine semi-structured interviews were conducted with early-career, non-managerial Chinese women (aged 26–32) who had experienced WCB. The analysis identified five themes showing that WCB was typically embedded in daily digital work practices rather than confined to isolated hostile incidents. Participants reported reputational attacks, public undermining, and exclusion in group chats alongside gendered degradation, such as sexualised rumours about promotion, as well as client‑initiated online sexual harassment. They also recounted culturally normalised hierarchical cyber-control through monitoring of responsiveness, demands for deference in group spaces, and expectations of late-night and weekend compliance. Some accounts described paternalistic “dad-flavour” messaging that framed obedience as care or guidance. Work demands routinely crossed into participants’ personal spheres through after-hours contact and corporate visibility requirements via personal accounts. Many participants avoided formal reporting, citing uncertainty about what counts as WCB, low confidence in organisational action, and the risks of challenging authority. Coping relied on venting, emotional detachment, avoidance, and technical workarounds, alongside a clear desire for organisational protection. These findings highlight the need for stronger digital communication governance, including clear policies on personal-account use for work, after-hours contact, mandatory corporate visibility practices, and escalation routes for client-initiated sexual harassment.

Abstract: This study constitutes the second part of a comprehensive investigation of the persistence of weighted average cost of capital (WACC) rates despite declining risk-free interest rates. While theory suggests that WACC should reflect lower risk-free interest rates and decline as well with falling government bond yields, empirical evidence reveals minimal adjustment in reported WACC figures. Disclosed WACC of DAX40 companies remain between 7% and 8% as yield of a ten-year German government bond fell from 4.1% to –0.2%. After the quantitative risk analysis (part I) systematically lacks market-based and fundamental explanations – demonstrating that neither systematic risk, overall market risk, earnings risk nor leverage increased sufficiently to justify this stability – this article addresses the resulting explanatory gap through qualitative inquiry. Employing grounded theory methodology, we investigate causes and consequences of persistent WACC through systematic analysis of 18 problem-centered semi-structured expert interviews (22 respondents comprising corporate finance executives, investment bankers, strategy consultants, auditors). The investigation reveals that behavioral economics (risk aversion, opportunism, subjectivity), organizational constraints (strategic path dependency, implementation complexity, financial criterion rigidity), and model-theoretic discretion (parameter averaging, analyst influence, supplementary risk adjustments) substantially shape practical WACC determination – factors that quantitative risk analysis cannot capture. Practitioners employ disclosed WACC strategically to reconcile investor return requirements with long-term operational stability, avoid audit friction, and hedge geopolitical-monetary risks – consequences that generate capital opportunity costs offsetting traditional value-maximization objectives. Combined quantitative and qualitative evidence yields actionable insights for value-based capital cost methodologies aligned with organizational and market realities.

Abstract: This paper addresses the problem of text classification models being vulnerable and lacking robustness under adversarial perturbations by proposing a robust text classification method based on large language model calibration. The method builds on a pretrained language model and constructs a multi-stage framework for semantic representation and confidence regulation. It achieves stable optimization of classification results through semantic embedding extraction, calibration adjustment, and consistency constraints. First, the model uses a pretrained encoder to generate context-aware semantic features and applies an attention aggregation mechanism to obtain global semantic representations. Second, a temperature calibration mechanism is introduced to smooth the output probability distribution, reducing the model's sensitivity to local perturbations. Third, adversarial consistency constraints are applied to maintain feature alignment between original and perturbed samples in semantic space, ensuring dynamic preservation of semantic robustness. The method adopts a joint loss function to balance three optimization objectives: classification accuracy, robustness, and confidence. To verify its effectiveness, sensitivity experiments on hyperparameters, environments, and data distributions are conducted. The results show that the model maintains high performance and stability under conditions such as word substitution, noise injection, and class imbalance, significantly outperforming several mainstream baseline models. This study achieves the integration of semantic-level robustness optimization and calibration learning, providing a new approach for building highly reliable text classification systems.

Abstract: To solve the problem of constructing the frequency responses (FR) of filters on switched capacitors, which belong to the class of electronic circuits with a periodically changing structure, a method for modeling them in Micro-Cap and Delta Design environments is proposed. It allows you to evaluate the nature of changes in the FR of such filters in the time domain. As an example, a comparative analysis of the frequency response of a second-order analog bandpass filter, as well as two bandpass filter circuits with switching resistors and capacitors, is given. An assessment of the current state of EDA and trends in their development is given.

Abstract: The European Union Emissions Trading System (EU ETS) has experienced dramatic price fluctuations since its 2005 inception, raising questions about whether carbon pricing effectiveness exhibits threshold behaviour—specifically, whether there exists a minimum carbon price level below which market signals fail to stimulate renewable energy investment. This study applies the Hansen threshold regression methodology to investigate regime-dependent dynamics in the relationship between EU ETS carbon prices and renewable energy consumption over 2005–2024. We identify a statistically significant threshold at €20.71/tCO2 (bootstrap p = 0.048), which partitions the sample into distinct low- and high-price regimes. Below this threshold, carbon prices exhibit no significant positive effect on renewable deployment (β1 = −36.16, p = 0.246); above the threshold, a positive relationship emerges (β2 = +7.20, p = 0.081), with each additional euro associated with 7.20 TWh of additional renewable consumption. Technol-ogy-specific analysis reveals that solar electricity exhibits particularly strong respon-siveness to above-threshold carbon prices (β2 = +1.71, p = 0.019). The threshold estimate is robust to alternative trimming specifications, functional forms, and outlier exclusion. These findings suggest that the EU ETS achieved effectiveness as a driver of renewable energy only after carbon prices exceeded approximately €21/tCO2—a transformation that coincided with the implementation of the Market Stability Reserve. The results provide empirical support for carbon price floor mechanisms and validate structural reforms aimed at strengthening the credibility of the carbon market.

Abstract: This study explored the Ultrastructural and oxidative stress-related genes and proteins changes in the laminar tissue of dairy cows with oligofructose (OF)-induced laminitis. Twelve clinically healthy, non-pregnant Chinese Holstein cows were randomly allocated into two groups: OF-overload group (n = 6) and Control group (n = 6). 17g/kg BW of oligofructose (OF) dissolved in 20 mL/kg BW of deionized water was provided to the OF-treated group, while the control group received 20 mL/kg BW of deionized water via a stomach tube. Laminar tissue samples were collected after euthanizing cows at 72 h. We observed that the gene and protein expression of Nrf2, Ho1, and Nqo1 significantly decreased, while keap1 significantly increased in the OF group as compared to the control group. Moreover, the distribution of Keap1 expression significantly increased, while that of the Nrf2 significantly decreased in the OF group relative to the control group. However, in the OF group, the lamina densa appears thick and damaged, with interrelated collagen fibers, and lightly stained; number of hemidesmosomes on the cell membrane decreased; the distance between the basal cells and the epidermal lamellae increased; epidermal basal cells have deformed nuclei with reduced chromatin than in control cows. In conclusion, unbalanced gene and protein status may be the stem cause for the epidermal detachment which confirmed the increased oxidative stress in the OF group.

Abstract: Background: Addressing the pervasive issue of low medication adherence is a critical step in reducing South Africa's disease burden. While counselling is mandated in key disease initiatives, primary health care services often employ directive, information-based methods that do not sufficiently encourage lasting behavior change. Motivational Interviewing is a person-centered, evidence-based approach that enhances motivation, fosters adherence, and supports active engagement in care. However, the extent of its application in South African PHC remains unexplored. Objectives: This review synthesizes current evidence on MI's usage in South African primary health care, emphasizing its effectiveness, identifying implementation gaps, and its potential to enhance person-centered disease management. Methods: This scoping review followed the Arksey and O'Malley framework and PRISMA-ScR guidelines. The research question was formulated with an expansive scope to comprehensively map MI's application, ensuring alignment with policy-oriented aims to integrate MI systematically into PHC. Electronic databases and grey literature were searched for studies published from 2000 to 2025 on MI or similar counselling in South African PHC and community public sector settings. Data were collected and summarized against established objectives to generate critical insights to inform policy and practice improvements. Results: Of the 38 identified records, 21 studies met the inclusion criteria. Among these, 81% used MI as brief, MI-informed counselling in routine PHC services rather than full-protocol MI. Seventy-six percent focused on HIV adherence, while 62% ad-dressed non-communicable diseases, with emerging evidence in tuberculosis care. Reported benefits included improved medication adherence, increased engagement and retention in care, and stronger patient-provider relationships. Challenges included limited staff training, inadequate supervision or monitoring, and reliance on project-based delivery. Conclusion: Motivational Interviewing is practical and can be scaled up to support behavior change in South African PHCs, particularly to improve adherence and long-term care engagement. However, its effectiveness is constrained by uneven implementation and insufficient system support. Integrating MI into national policies, PHC routines, and workforce training could strengthen person-centered care and improve disease control.

Abstract: The aim of the research was to develop an innovative meat and plant-based product in the form of a paste with potential beneficial health properties. The verification of beneficial health properties was carried out based on the results of the nutritional analysis with respect to the meat and plant-based reference products. The quality assessment considered both physical and sensory characteristics. The results showed that the meat and plant-based product had a reduced content of fat, saturated and monounsaturated fatty acids, cholesterol and a lower energy value compared to the reference meat-based product, with a simultaneous increase in the proportion of polyunsaturated fatty acids and dietary fibre. In terms of plant-based products, the meat-plant hybrid prototype distinguished itself with a higher protein content and a more favourable amino acid profile. Sensory evaluation confirmed the high desirability of the aroma, taste, and overall acceptability of the meat-plant product compared to reference products. Reformulation enabled the development of an innovative product with balanced nutritional and sensory characteristics, providing a valuable, health-promoting alternative to traditional meat-based products and classic plant-based pastries.

Abstract: Complementary therapies are part of therapeutic support practices used by a growing number of individuals as a complement to conventional medicine. However, the lack of regulation and formal training on these therapies within health-related degree programs makes it difficult for professionals to adequately guide patients regarding their use. This paper proposes criteria for the inclusion of basic content on complementary therapies in medical curricula, with the aim of promoting a critical, informed, and safe understanding, without encouraging their direct practice by physicians.

Abstract: Bacteria-based cancer therapies have re-emerged as a promising modality due to the intrinsic capacity of certain bacterial species to preferentially colonize hypoxic and necrotic tumor regions [1, 2]. However, planktonic motile systems are frequently limited by rapid immune clearance, transient persistence, and uncontrolled payload release [2, 3]. This review introduces a synthetic biology framework that reframes bacterial biofilms from pathological barriers into programmable therapeutic scaffolds. By utilizing programmable microbial therapeutic chassis, researchers can enhance therapeutic duration within the tumor microenvironment (TME) while potentially minimizing systemic exposure [4, 5]. Central to this framework is the genetic modulation of matrix density, primarily via curli fiber-associated csgA expression. This approach may enable drug release kinetics governed by Fickian diffusion principles, allowing for sustained and controllable therapeutic delivery [6, 7].

Abstract: Anatomical variants are observed on paired body sides, yet many prevalence studies—particularly those based on osteological collections—report only right- and left-side frequencies without specifying whether findings occur bilaterally in the same individual. In such cases, the individual-level left–right structure is unobserved. Consequently, inference on laterality and bilateralism cannot be based on the reported data alone and must rely on explicit assumptions about within-individual dependence.We study this problem in the context of anatomic prevalence data, although the framework applies more broadly to paired binary outcomes. We parameterize the admissible joint distributions using a feasibility-based dependence index, λ, spanning the full range from independence to maximal feasible concordance implied by the marginal prevalences. Within this framework, we examine two complementary estimands: the paired odds ratio for laterality and bilateral prevalence.Analytic results and Monte Carlo simulations show that bilateral prevalence varies linearly and remains stable across the admissible dependence range, whereas the paired odds ratio exhibits intrinsic boundary instability as dependence approaches its feasible maximum due to vanishing discordant counts. Uncertainty-propagation analyses further indicate that laterality inference is robust to moderate misspecification of the dependence assumption. These results demonstrate that unobserved within-subject dependence is a structural inferential issue in paired binary meta-analysis and motivate feasibility-based sensitivity analysis when only marginal data are available.