Search Results (1,514)

Redox (reduction–oxidation) processes underlie all forms of life and are a universal regulatory mechanism that maintains homeostasis and adapts the organism to changes in the internal and external environments. From capturing solar energy in photosynthesis and oxygen generation to fine-tuning cellular metabolism, redox reactions are key determinants of life activity. Proteins containing sulfur- and selenium-containing amino acid residues play a crucial role in redox regulation. Their reversible oxidation by physiological oxidants, such as hydrogen peroxide (H₂O₂), plays the role of molecular switches that control enzymatic activity, protein structure, and signaling cascades. This enables rapid and flexible cellular responses to a wide range of stimuli—from growth factors and nutrient signals to toxins and stressors. Mitochondria, the main energy organelles and also the major sources of reactive oxygen species (ROS), play a special role in redox balance. On the one hand, mitochondrial ROS function as signaling molecules, regulating cellular processes, including proliferation, apoptosis, and immune response, while, on the other hand, their excessive accumulation leads to oxidative stress, damage to biomolecules, and the development of pathological processes. So, mitochondria act not only as a “generator” of redox signals but also as a central link in maintaining cellular and systemic redox homeostasis. Redox signaling forms a multi-layered cybernetic system, which includes signal perception, activation of signaling pathways, the initiation of physiological responses, and feedback regulatory mechanisms. At the molecular level, this is manifested by changes in the activity of redox-regulated proteins of which the redox proteome consists, thereby affecting the epigenetic landscape and gene expression. Physiological processes at all levels of biological organization—from subcellular to systemic—are controlled by redox mechanisms. Studying these processes opens a way to understanding the universal principles of life activity and identifying the biochemical mechanisms whose disruption causes the occurrence and development of pathological reactions. It is important to emphasize that new approaches to redox balance modulation are now actively developed, ranging from antioxidant therapy and targeted intervention on mitochondria to pharmacological and nutraceutical regulation of signaling pathways. This article analyzes the pivotal role of redox balance and its regulation at various levels of living organisms—from molecular and cellular to tissue, organ, and organismal levels—with a special emphasis on the role of mitochondria and modern strategies for influencing redox homeostasis. Full article

This paper presents the design, implementation and evaluation of an agentic virtual assistant (VA) for a medical clinic, combining large language models (LLMs) with retrieval-augmented generation (RAG) technology and multi-agent artificial intelligence (AI) frameworks to enhance reliability, clinical accuracy and explainability. The assistant has multiple functionalities and is built around an orchestrator architecture in which a central agent dynamically routes user queries to specialized tools for retrieval-augmented question answering (Q&A), document interpretation and appointment scheduling. The implementation combines LangChain and LangGraph with interactive visualizations to track reasoning steps, prompts using Gemini 2.5 Flash defines tool usage and strict formatting rules, maintaining reliability and mitigating hallucinations. Prompt engineering has an important role in the implementation and thus, it is designed to assist the patient in the human–computer interaction. Evaluation through qualitative and quantitative metrics, including ROUGE, BLEU, LLM-as-a-judge and sentiment analysis, confirmed that the multi-agent architecture enhances interpretability, accuracy and context-aware performance. Evaluation shows that the multi-agent architecture improves reliability, interpretability and alignment with medical requirements, supporting diverse clinical tasks. Furthermore, the evaluation shows that Gemini 2.5 Flash combined with clinic-specific RAG significantly improves response quality, grounding and coherence compared with earlier models. SBERT analyses confirm strong semantic alignment across configurations, while LLM-as-a-judge scores highlight the superior relevance and completeness of the 2.5 RAG setup. Although some limitations remain, the updated system provides a more reliable and context-aware solution for clinical question answering. Full article

The article discusses the control of nonlinear processes with first-order dominant dynamics, focusing on implementation using modern hardware available in various programmable devices and embedded systems. The first two approaches rely on linearization with an ultra-local process model, considering small changes of the process input and output around a fixed operating point, which can be adjusted through gain scheduling with the setpoint variable. This model is used to configure either the historically established automatic reset controller (ARC) or a stabilizing proportional (P) controller enhanced by an inversion-based disturbance observer (DOB). This solution can be interpreted as an application of modern control theory (MCT), as DOB-based control (DOBC) or as advanced disturbance rejection control (ADRC). Alternatively, they can be viewed as a special case of automatic offset control (AOC) based on two types of linear process models. In the third design method, setpoint tracking by exact linearization (EL) is extended with a nonlinear DOB designed using the inverse of the nonlinear process dynamics (EEL). The fourth approach augments EL-based tracking with a DOB derived from the transfer functions of nonlinear processes (NTF). An illustrative example involving the control of a liquid reservoir with a variable cross-section clarifies motivation for the definition of (linear) local and ultra-local process models as well as their advantages in designing robust control that accounts for process uncertainties. Thus, the speed, homogeneity, and shape of transient responses, the ability to reconstruct disturbances, control signal saturation, and measurement noise attenuation are evaluated according to the assumptions specified in the controller design. The novelty of the paper lies in presenting a unifying perspective on several seemingly different control options under the impact of measurement noise. By explaining their essence, advantages, and disadvantages, it provides a foundation for controlling more complex time-delayed systems. The paper emphasizes that certain aspects of controller design, often overlooked in traditional linearization procedures, can significantly improve closed-loop properties. Full article

This study aims to identify and interpret latent energy-economic typologies across European economies and to assess whether their energy transition paths exhibit convergence or persistent structural divergence. To achieve this objective, the paper investigates the energy–economic structure of thirteen European economies between 2000 and 2024 using an integrated fuzzy–machine learning framework. Eight indicators related to renewable energy, energy efficiency, emissions, electricity use, digitalization, investment, urbanization and economic development were analyzed to identify structural typologies across countries. Using the Fuzzy C-Means algorithm, four distinct clusters were identified: (i) moderately developed economies with balanced renewable adoption and energy efficiency, (ii) structurally integrated economies with medium energy intensity and stable economic performance, (iii) an emerging economy with persistent structural constraints, and (iv) advanced high-performance economies engaged in accelerated energy transition. To validate the fuzzy classification, Random Forest and XGBoost models were trained based on the same indicators, achieving high predictive accuracy (94% and 92%, respectively). Feature importance analysis reveals that CO₂ emissions, energy efficiency and urbanization play the most significant roles in differentiating country profiles. The proposed framework provides a comprehensive approach for understanding energy transition heterogeneity, structural convergence and the drivers shaping the evolution of European energy–economic systems. Full article

This study presents an analytical methodology for evaluating the influence of hull design parameters on the controllability and manoeuvrability of ships equipped with integrated electric propulsion systems. Unlike traditional approaches that examine the hull and propulsion plant independently, the proposed method employs a generalized model of transient modes within the propulsion complex, enabling the coupled interaction among the hull, propulsion units, electric motors, and the electrical power system to be captured during manoeuvring. Active experimental design and regression modelling are applied to construct controllability diagrams, identify the most influential dimensionless parameters, and reduce computational effort. The methodology is used to assess the effect of hull elongation (0.08–0.16 L) with curvature variation limited to 6%. The results show that this degree of elongation has minimal impact on turning performance and course-keeping stability, confirming the feasibility of such design modifications. The proposed approach provides an effective tool for early-stage design and modernization of electric ships and supports decision-making in ship behaviour prediction and traffic management. Full article

This study identifies key determinants shaping countries’ Sustainable Development Goals performance and develops classification models for predicting country group membership based on the SDG Index. The research addresses the urgent need to optimize development policies amid limited resources and the approaching 2030 Agenda deadline. Using data from 154 countries (2024), the analysis reveals that key SDG determinants are fundamentally method-dependent: discriminant analysis identified Goals 10, 6, 15, and 5 as most influential for differentiating countries by SDGI level, while Random Forest identified Goals 4, 9, and 2 as the most important predictors. This divergence reflects fundamentally different analytical perspectives—linear contributions to group separation versus complex nonlinear interactions and synergies between goals—with critical policy implications for prioritization strategies. Correlation analysis demonstrates that sustainable development dynamics operate differently across development stages: high-development countries show strongest associations with technological advancement and institutional capacity, while low-development countries exhibit compensation effects where basic infrastructure provision occurs alongside lagging human capital development. The discriminant model achieved 94.08% overall accuracy with perfect classification for extreme SDGI categories, while the Random Forest model provides complementary insights into interactive pathways. The scientific contribution lies in demonstrating that perceived variable importance depends on analytical framework rather than representing objective reality, and in providing validated classification tools for rapid assessment in data-limited contexts. These findings offer actionable guidance for evidence-based resource allocation and policy prioritization in the critical final years of SDG implementation. Full article

The Fun Palace, a project initiated by Joan Littlewood and Cedric Price in 1961, exemplifies a pivotal transformation in the approach of cultural institutions toward their visitors. By centering on the audience experience, the Fun Palace signifies a departure from conventional institutional practices and represents a seminal example of unconventional art institution projects. This paper will examine the project, closely looking at the architecture, programming, and cybernetic structure of the Fun Palace. The purpose of this examination is to demonstrate how and why the ideas of Littlewood and Price predate the contemporary policies of institutions interacting with their audiences. Full article

Conventional total variation (TV) regularization methods based on Laplacian or fixed-scale Hyper-Laplacian priors impose uniform sparsity penalties on gradients. These uniform penalties fail to capture the heterogeneous sparsity characteristics across different regions and directions, often leading to the over-smoothing of edges and loss of fine details. To address this limitation, we propose a novel regularization Hyper-Laplacian Adaptive Weighted Total Variation (HLAWTV). The proposed regularization employs a proportional mixture of Hyper-Laplacian distributions to dynamically adapt the sparsity decay rate based on image structure. Simultaneously, the adaptive weights can be adjusted based on local gradient statistics and exhibit strong robustness in texture preservation when facing different datasets and noise. Then, we propose an hyperspectral image (HSI) denoising method based on the HLAWTV regularizer. Extensive experiments on both synthetic and real hyperspectral datasets demonstrate that our denoising method consistently outperforms state-of-the-art methods in terms of quantitative metrics and visual quality. Moreover, incorporating our adaptive weighting mechanism into existing TV-based models yields significant performance gains, confirming the generality and robustness of the proposed approach. Full article

In this paper, a permanent-magnet-assisted synchronous reluctance motor (SYNRM) coupled with a newly built QDBC and a voltage-fed inverter (VFI) for a standalone PV water pumping system is suggested. Because power supply oscillations can result in short-term disruptions that affect drive performance in industrial applications involving these motors, a robust smooth control system is required to guarantee high efficiency and uninterrupted operation. According to the suggested architecture, a newly built quadratic boost regulator with a very high voltage gain, called a quadruple-diode boost converter (QDBC), is used to first elevate PV voltage to high levels. Additionally, to optimize the power output of the solar PV module, the perturbation and observation highest power point tracking approach (P&O) is implemented. To provide smooth synchronous motor starting, field-oriented control (FOC) of a voltage-fed inverter (VFI) is combined with hysteresis current control of the QDBC. The optimization algorithms discussed in this paper aim to enhance the efficiency of the SYNRM, particularly in operating a synchronous motor powered by variable energy sources such as solar PV. These algorithms function within a cybernetic system designed for water pumping, incorporating feedback loops and computational intelligence for improved performance. Afterward, the three-phase permanent-magnet synchronous motor that drives the mechanical load is fed by the resulting voltage via a voltage source inverter. Furthermore, a thorough hysteresis current control method implementation of the QDBC was suggested in order to attain optimal efficiency in both devices, which is crucial when off-grids are present. Even when the DC-link voltage dropped by up to 10% of the rated voltage, the suggested method was shown to maintain the required reference torque and rated speed. To verify the efficacy of the suggested method, a simulation setup according to the MATLAB 2022b/Simulink environment was employed. To gather and analyze the data, multiple scenarios with varying operating conditions and irradiance levels were taken into consideration. Finally, a working prototype was constructed in order to validate the mathematical analysis and simulation findings of the suggested framework, which includes a 1 kW motor, current sensor, voltage sensor, QDBC, and VCS inverter. Full article

This paper explores a stochastic model of noisy observations with a sparse true signal structure. Such models arise in a wide range of applications, including signal processing, anomaly detection, and performance monitoring in telecommunication networks. As a motivating example, we consider round-trip time (RTT) data, which characterize the transit time of network packets, where rare, anomalously large values correspond to localized network congestion or failures. The focus is on the asymptotic properties of the mean-square risk associated with thresholding procedures. Upper bounds are obtained for the mean-square risk when using the theoretically optimal threshold. In addition, a central limit theorem and a strong law of large numbers are established for the empirical risk estimate. The results provide a theoretical basis for assessing the effectiveness of thresholding methods in localizing rare anomalous components in noisy data. Full article

Whole-slide histology images (WSIs) can exceed 100 k × 100 k pixels, making direct pixel-level segmentation infeasible and requiring patch-level classification as a practical alternative for downstream WSI segmentation. However, most approaches either treat patches independently, ignoring spatial and biological context, or rely on deep graph models prone to oversmoothing and loss of local tissue detail. We present WSI-GT (Pseudo-Label Guided Graph Transformer), a simple yet effective architecture that addresses these challenges and enables accurate WSI-level tissue segmentation. WSI-GT combines a lightweight local graph convolution block for neighborhood feature aggregation with a pseudo-label guided attention mechanism that preserves intra-class variability and mitigates oversmoothing. To cope with sparse annotations, we introduce an area-weighted sampling strategy that balances class representation while maintaining tissue topology. WSI-GT achieves a Macro F1 of 0.95 on PATH-DT-MSU WSS2v2, improving by up to 3 percentage points over patch-based CNNs and by about 2 points over strong graph baselines. It further generalizes well to the Placenta benchmark and standard graph node classification datasets, highlighting both clinical relevance and broader applicability. These results position WSI-GT as a practical and scalable solution for graph-based learning on extremely large images and for generating clinically meaningful WSI segmentations. Full article

Milk and its by-products offer a concentrated source of proteins and nutrients that are essential for life and that can be challenging to obtain from other foods. There has been growing interest in the production, enhancement, and effective utilization of milk over time. The objective of this research paper is to contribute to ongoing efforts to enhance the production and collection of milk and dairy derivatives in Romania. In a study analyzing the dairy industry in the European Union, various indicators were examined with the aim of classifying countries and determining Romania’s position. To gain a comprehensive understanding of the dairy industry in the European Union, several indicators were considered, including milk production; different dairy products, such as butter and cheese; and data on bovine populations in various age groups. To efficiently classify the countries and identify Romania’s position, advanced data mining techniques were employed, including cluster analysis and neural network training. To enhance and advance the dairy industry in Romania, this study proposes the exploration of the potential advantages of implementing Industry 4.0 solutions, particularly on a larger scale, with Enterprise Resources Planning (ERP) software. Full article

Bio-cybernetic organisms combine biological locomotion with electronic control but face significant challenges regarding individual variability and stimulus habituation. This study introduces an Adaptive Neuro-Fuzzy Inference System (ANFIS) designed to dynamically calibrate to individual Gromphadorhina portentosa specimens. Using a miniaturized neural controller, we compared ANFIS’s performance against natural behavior and non-adaptive control methods. Results demonstrate ANFIS’s superiority: obstacle navigation efficiency reached 81% (compared to 42% for non-adaptive methods), and effective behavioral modulation was sustained for 47 min (versus 26 min). Furthermore, the system achieved 73% target acquisition in complex terrain and maintained stimulus responsiveness 3.5-fold longer through sophisticated habituation compensation. Biocompatibility assessments confirmed interface functionality over 14-day periods. This research establishes foundational benchmarks for arthropod bio-cybernetics, demonstrating that adaptive neuro-fuzzy architectures significantly outperform conventional methods, enabling robust bio-hybrid platforms suitable for confined-space search-and-rescue operations. Full article

Metropolitan service-placement optimization is computationally challenging under strict evaluation budgets and regulatory constraints. Existing approaches either neglect capacity constraints, producing infeasible solutions, or employ population-based metaheuristics requiring hundreds of evaluations—beyond typical municipal planning resources. We introduce a two-stage optimization framework combining Bayesian optimization with domain-informed heuristics to address this constrained, mixed discrete–continuous problem. Stage 1 optimizes continuous service area allocations via the Tree-structured Parzen Estimator with empirical gradient prioritization, reducing effective dimensionality from 81 services to 10–15 per iteration. Stage 2 converts allocations into discrete unit placements via efficiency-ranked bin packing, ensuring regulatory compliance. Evaluation across 35 benchmarks on Saint Petersburg, Russia (117–3060 decision variables), demonstrates that our method achieves 99.4% of the global optimum under a 50-evaluation budget, outperforming BIPOP-CMA-ES (98.4%), PURE-TPE (97.1%), and NSGA-II (96.5%). Optimized configurations improve equity (Gini coefficient of 0.318 → 0.241) while maintaining computational feasibility (2.7 h for 109-block districts). Open-source implementation supports reproducibility and facilitates adoption in metropolitan planning practice. Full article

This paper presents an AI-augmented pronunciation training approach for Russian language learners through a mobile application that supports an interactive learner–system feedback loop. The system combines a pre-trained Wav2Vec2Phoneme neural network with Needleman–Wunsch global sequence alignment to convert reference and learner speech into aligned phoneme sequences. Rather than producing an overall pronunciation score, the application provides localized, interpretable feedback by highlighting phoneme-level matches and mismatches in a red/green transcription, enabling learners to see where sounds were substituted, omitted, or added. Implemented as a WeChat Mini Program with a WebSocket-based backend, the design illustrates how speech-to-phoneme models and alignment procedures can be integrated into a lightweight mobile interface for autonomous pronunciation practice. We further provide a feature-level comparison with widely used commercial applications (Duolingo, HelloChinese, Babbel), emphasizing differences in feedback granularity and interpretability rather than unvalidated accuracy claims. Overall, the work demonstrates the feasibility of alignment-based phoneme-level feedback for mobile pronunciation training and motivates future evaluation of recognition reliability, latency, and learning outcomes on representative learner data. Full article