Search Results (13,995)

The evolution of digital technologies is reshaping parliaments worldwide, driving fundamental changes in their operations. Parliaments, being traditionally conservative institutions, typically lean toward “mature” emerging or disruptive technologies through cautious, incremental digital transformation attempts, resulting in complex digital parliamentary environments for their users, processes, systems, and tools. The paper employs an integrative literature review as its methodological tool, examining the concept of the “digital parliament” and the technologies that enable it. Using a PRISMA-informed methodology as a guide, we conducted an integrative review covering the period 2006–2025, and in this context, we retrieved 535 publications, screened 260, thoroughly examined 57, and analyzed and synthesized 34 studies addressing digital parliamentary technologies, digital platforms, and cooperative workspaces. We found that while specific parliamentary technology (ParlTech) applications—including big data analytics, artificial intelligence (AI), and hybrid parliamentary tools—are reaching institutional maturity, the concept of a digital parliament remains fragmented, lacking a unified definitional and operational framework. Key research gaps have been identified concerning user classification, the digitization of parliamentary functions, operations, and processes, as well as the institutionalization of cooperation platforms. Based on these findings, we propose strategic directions toward establishing a responsible, inclusive, and evidence-based digital parliament. This research contributes as a guideline for parliamentary organizations seeking to create, retain, and disseminate public value through the responsible adoption of emerging digital technologies. Full article

The intelligent detection of surface damage is crucial for ensuring safe and stable equipment operation. This study addressed the low efficiency, strong subjectivity, and inadequate representation capabilities of traditional single-feature-based damage identification methods by proposing an intelligent equipment surface damage classification method employing multi-feature fusion. The innovations of this method comprise the construction of a seven-dimensional model integrating brightness, texture, frequency domain, edge, and morphological features to comprehensively characterize the visual characteristics of six typical types of damage (fine crazing, inclusions, patches, pitted surfaces, rolled-in oxide scale, and scratches) and the application of a Bayesian probabilistic classification model based on adaptive kernel density estimation to identify damage type and determine the associated confidence. Experimental results obtained using a self-assembled dataset comprising 1619 training samples and 180 test samples indicated that the proposed method achieved a comprehensive accuracy of 97.77%, significantly outperforming traditional deep neural networks and Ant Forest algorithms. Thus, the proposed method provides an effective solution for the intelligent and efficient detection of equipment surface damage. Full article

Deploying deep vision models on edge hardware requires understanding how architecture and training hyperparameters jointly determine accuracy and inference latency, yet these relationships remain poorly characterized in a systematic, data-driven manner. This paper presents a two-stage statistical framework providing interpretable, closed-form insights into both. In the first stage, we apply distance correlation (dCor) and the maximal information coefficient (MIC) across seven image-classification datasets, revealing that batch size and total layer count are the strongest universal accuracy predictors (mean dCor: 0.228 and 0.174), while learning rate achieves the highest MIC (0.226), reflecting a non-monotonic relationship with accuracy. In the second stage, PySR symbolic regression (representing, to our knowledge, the first application to cross-dataset vision model accuracy prediction) derives compact, interpretable formulas. Dataset-specific models achieve $R^2$ from 0.20 to 0.45; a universal model achieves a mean leave-one-dataset-out $R^2$ of 0.23, remaining strictly positive on all held-out datasets, whereas ordinary linear regression collapses to $R^2=-0.71$. We further derive device-specific inference latency formulas for CPU, GPU, and NPU, outperforming classical baselines by $6.7\times$–$14.8\times$ in $R^2$ and confirming fundamental device heterogeneity. Together, these results offer interpretable surrogate models for screening deep vision architectures under accuracy and latency constraints in edge deployment. Full article

Hybrid additive manufacturing (HAM) integrates additive and subtractive processes within a unified production system, combining the geometric flexibility and material efficiency of additive manufacturing with the dimensional accuracy and surface quality of conventional machining. This review provides a comprehensive analysis of HAM technologies through a proposed four-criterion classification framework encompassing process integration strategy, additive manufacturing process type, machine architecture, and application domain. DED-based, PBF-based, and polymer-based hybrid systems are examined alongside integrated hybrid machines, retrofit solutions, and robotic architectures. A comparative analysis of representative commercial platforms evaluates build envelope, integration strategy, and monitoring capability. Documented performance outcomes across aerospace, automotive, energy, and biomedical sectors confirm substantial improvements in surface quality, fatigue performance, dimensional accuracy, and material efficiency relative to conventional manufacturing routes. Current limitations are critically assessed across technical, process integration, and economic dimensions, and a structured near-to-long-term research roadmap is proposed, prioritising in-process sensing and toolpath standardisation, digital twin-based adaptive process planning, and ultimately autonomous hybrid manufacturing cells with lifecycle certification. These findings position HAM as a central enabling technology for intelligent, flexible, and sustainable production within Industry 4.0 and Industry 5.0 paradigms. Full article

Variational quantum algorithms (VQAs) are a leading framework for realizing quantum advantages in the Noisy Intermediate-Scale Quantum (NISQ) era, with applications spanning discrete combinatorial problems and continuous optimization. While the topologies of parameterized quantum circuits (ansatzes) fundamentally govern both expressibility and trainability in continuous landscapes, existing reviews predominantly focus on static algorithmic classifications or discrete settings, leaving the structural evolution and practical limitations of ansatz architectures insufficiently explored. To address this gap, this review presents a systematic analysis of variational ansatz architectures, tracing their progression from static, pre-defined topologies to adaptive growth mechanisms. Beyond traditional gradient-driven and architecture-search paradigms, we evaluate supplementary strategies such as layerwise training and noise-adaptive construction, revealing inherent vulnerabilities such as local minima entrapment and the compilation overhead induced by calibration drift. The mathematical foundations of VQAs are outlined, and representative fixed ansatz architectures, including hardware-efficient, physics-inspired, and problem-specific designs, are characterized within continuous-domain mappings. Intrinsic limitations arising from barren plateaus (BPs) and noise-induced barren plateaus (NIBPs) are analyzed, revealing the fundamental coupling between circuit depth, parameter scaling, and trainability degradation. Furthermore, adaptive construction strategies and recent advances in automated variational quantum architecture search (VQAS) are examined. Through the synthesis of intrinsic limitations (BPs, NIBPs, and hardware-algorithm coupling) and the evaluation of standardized benchmarking protocols, this review rigorously assesses the resource trade-offs of current VQA frameworks. Ultimately, next-generation ansatz design will adopt hardware–software co-design principles grounded in physical constraints, enabling scalable and noise-resilient quantum optimization. Full article

This comprehensive review investigates hybrid propulsion technologies as a pathway to decarbonization and improved energy efficiency in the maritime sector. Through a review of the recent literature, this study synthesizes current knowledge on energy management strategies and capacity sizing approaches for hybrid ship propulsion systems. Reported results indicate that optimized energy management can reduce fuel consumption and greenhouse gas emissions while minimizing total operational costs. Among real-time strategies, the Equivalent Consumption Minimization Strategy emerges as particularly suitable for maritime use due to its low computational demand and independence from full voyage profile knowledge, yet its maritime application remains far less developed than in the automotive domain. Capacity sizing and energy management are usually treated as separate optimization problems, limiting the achievability of truly optimal solutions. Only a few studies adopt integrated co-optimization frameworks, and these are typically built around simplified or fixed operational profiles. Moreover, the coupling between energy management parameters, such as the ECMS equivalence factor, and hardware sizing remains insufficiently explored. To address this, the review contributes a ship-specific classification of energy management strategies, a consolidated treatment of battery sizing methods with explicit attention to degradation, and a generalized two-loop framework that couples component sizing with ECMS-based energy management. The findings suggest that future research should prioritize adaptive energy management formulations calibrated for stochastic maritime duty cycles, the incorporation of battery degradation models into co-optimization, and validation against stochastic, real-world operating conditions. Full article

The energy transition in maritime shipping is increasing the importance of alternative fuels and port infrastructure capable of handling them in a safe, regular, and economically justified manner. In this context, LNG remains a transitional fuel with a relatively high level of technological and organizational maturity, particularly in regions characterized by intensive liner, ferry, and RO-RO traffic. This article proposes a universal model for organizing LNG distribution within the port–transport system, based on three interdependent dimensions: demand potential, infrastructure readiness, and operational feasibility. The model structure enables the classification of ports according to their functions within the regional bunkering network and the identification of nodes of the greatest systemic importance. The model was validated using data on vessel calls, the structure of container and RO-RO traffic, LNG infrastructure status, and monthly traffic variability. The analysis demonstrated that the most justified LNG distribution arrangement in the Baltic Sea is polycentric in nature and concentrated in ports, combining a high degree of transport regularity with confirmed LNG readiness. The results indicate that the rationale for LNG infrastructure development is selective in nature and depends on the actual position of a port within the transport network, rather than solely on cargo throughput volume. The proposed model also retains its applicability to other alternative fuels after adjustment of technological, regulatory, and operational parameters. By supporting the selective development of alternative-fuel infrastructure in ports with the highest systemic relevance, the model contributes to sustainable maritime transport planning and to the transition toward lower-emission port–transport systems. Full article

(1) Background: This study develops a hybrid assessment matrix for the early identification of material-related risks in adaptive reuse projects, with a particular focus on material conservation. The Material Conservation for Adaptive Reuse Risk Evaluation Matrix (M-CARE) integrates UNESCO Tools, the ICCROM ABC Method and its agents of deterioration, and the material damage classifications of the MDCS Damage Atlas. (2) Methods: The framework was tested through a case study of Issız Han, an Early Ottoman caravanserai. Risk mechanisms identified through M-CARE were compared with deterioration patterns documented after 18 years of post-reuse operation. The comparison focused on the degree of correspondence between predicted deterioration mechanisms and observed deterioration patterns. (3) Results: The findings indicate a high degree of correspondence between the deterioration mechanisms identified through M-CARE and the deterioration patterns documented during field surveys. In particular, moisture-related deterioration patterns showed substantial correspondence with the risks identified during the assessment stage. The results also highlight the influence of cumulative microclimatic factors and the value of complementary analytical approaches for evaluating long-term deterioration processes. (4) Conclusions: M-CARE provides a practical and rapidly applicable decision-support framework for the early identification and classification of material-related risks in adaptive reuse projects, supporting more informed conservation planning and proactive heritage management. Full article

Carbon dioxide (CO₂) conversion in non-thermal plasma is a promising route for carbon utilisation under mild conditions. This study investigates the performance and dynamic behaviour of kaolin-based catalysts modified with Ni (nickel), Ni–Ce (nickel-cerium), and Fe–Cu (iron-copper) oxides in a Dielectric Barrier Discharge (DBD) reactor. Materials were characterised using X-ray diffraction, energy-dispersive X-ray fluorescence, and scanning electron microscopy with energy-dispersive X-ray spectroscopy. CO₂ conversion was evaluated at varying Plasma Energy Numbers (PEN = 1.65–20) with time-resolved gas analysis over a 10 min period. Results demonstrate that the kaolin support is not inert; its dielectric properties actively influence discharge characteristics. Ni-based catalysts exhibited the highest stable activity, reaching ~53% conversion for samples calcined at 500 °C. Conversely, adding cerium oxide significantly decreased conversion and induced temporal instabilities, contrasting with its typical role in thermal catalysis. Time-resolved measurements revealed that Ni–Ce and Fe–Cu systems exhibit initial activity followed by gradual deactivation, suggesting plasma-induced surface restructuring. These findings highlight that catalyst performance in DBD is governed by a complex interplay of chemical activity and plasma–material interactions. The generated time-series data provide a robust foundation for machine learning applications in predictive modelling and stability classification of plasma-catalytic systems. Full article

As a novel membrane treatment technology, forward osmosis (FO) has become a research hotspot in the field of membrane technology owing to its advantages such as low energy consumption and low pollution. Nevertheless, this technology still faces notable limitations, including lower water flux than reverse osmosis (RO), difficult regeneration of draw solutions, limited commercial membrane types, and unavoidable reverse solute flux, which restrict its large-scale industrial application. This paper reviews the characteristics of forward osmosis membranes, the classification of draw solutions, the characteristics of membrane fouling, as well as the applications and development trends of forward osmosis technology. Common FO membranes include cellulose acetate (CA) membranes, thin-film composite (TFC) membranes fabricated by interfacial polymerization, and aquaporin (AQP)-based biomimetic membranes. According to the types of draw solutes, draw solutions can be classified into gaseous solutions, organic compound solutions, inorganic compound solutions, magnetic nanoparticle-based draw solutions, polymer gel draw solutions, etc. Since FO is operated without external hydraulic pressure, it exhibits lighter membrane fouling compared with pressure-driven membrane separation technologies. FO membrane fouling can be mainly divided into four categories according to fouling types: inorganic fouling, organic fouling, colloidal fouling, and biofouling. FO technology has a wide range of applications and plays an important role in seawater desalination, pressure-retarded osmosis (PRO) power generation, wastewater treatment and reuse, and the energy field. Notably, the reconcentration and regeneration of draw solutions remain major energy and economic limitations restricting the large-scale deployment of FO. As a promising treatment technology, with continuous technological advances in membrane materials and draw solutions, FO will play a significant role in the energy field, especially in lithium extraction from geothermal water, promoting the iteration of forward osmosis technology from a “water treatment technology” to a “core technology for energy and resource recovery”. Full article

Background: Microbiome-oriented therapies, including fecal microbiota transplantation (FMT), phage therapy, and live biotherapeutic products (LBPs), represent a promising direction in modern biomedicine for addressing antimicrobial resistance (AMR), recurrent Clostridioides difficile infection (rCDI), and dysbiosis-associated conditions. Despite encouraging clinical outcomes, their integration into routine clinical practice remains limited due to regulatory heterogeneity and unresolved ethical challenges. Objective: This review aims to analyze international regulatory approaches to microbiome-based therapies and to identify key bioethical issues associated with their clinical application. Main content: The paper summarizes current scientific evidence and regulatory frameworks governing microbiome therapies in the United States, the European Union, Ukraine, and selected Asia-Pacific countries. Particular attention is given to differences in classification, approval pathways, and safety requirements. The review also examines major ethical concerns, including informed consent, donor screening, biosafety, data protection, and equitable access to innovative treatments. Conclusions: The analysis demonstrates that microbiome therapies have significant potential for improving clinical outcomes and supporting antimicrobial stewardship. However, their broader implementation requires the harmonization of regulatory frameworks, strengthening of biosafety standards, and development of clear ethical guidelines. International cooperation and accumulation of clinical evidence are essential for the safe and effective integration of microbiome-based interventions into healthcare systems. Full article

The COVID-19 pandemic exposed critical vulnerabilities in healthcare systems worldwide, placing healthcare workers (HCWs) at severe infection risk through direct patient contact. Epidemiological data confirm that HCWs were approximately seven times more likely to develop severe COVID-19 than other occupations, with over 7000 HCW deaths recorded globally by mid-2020. This paper presents the design and laboratory proof-of-concept validation of an IoT-based remote patient-monitoring system prototype—the IoT-Based Isolation Ward Monitoring System Prototype—designed to eliminate unnecessary patient-to-HCW physical contact while maintaining continuous, real-time physiological surveillance. The system integrates multi-sensor hardware comprising an AD8232 ECG module, a MAX30100 pulse oximeter, an NTC thermistor, and an MQ-135 ${\mathrm{C}\mathrm{O}}_2$ sensor. These sensors interface with an Arduino UNO for data acquisition, while localized edge computing is executed on a Raspberry Pi 3B. A convolutional neural network (CNN) trained on the MIT-BIH Arrhythmia Database classifies heartbeats into five distinct categories. By utilizing SMOTE resampling on 109,446 samples, the network achieves an on-device inference latency of under 200 ms. The sensor data are transmitted to a Firebase Realtime Database via an authenticated REST API, which synchronizes data across dual front-end interfaces: a LabVIEW desktop dashboard for clinical oversight and a cross-platform Flutter mobile application for mobile monitoring. End-to-end technical validation under controlled laboratory conditions confirmed round-trip cloud latencies between 300 and 800 ms, error-free threshold alert generation, and sub-second latency for the integrated chat utility. The proposed system uniquely combines hardware sensing, ML-based ECG classification, cloud storage, a LabVIEW physician dashboard, and bidirectional doctor–patient mobile communication into a single unified, low-cost platform. Full article

Background/Objectives: Clostridioides difficile infection (CDI) represents an important healthcare-associated complication in hospitalized patients, particularly in those exposed to antibiotics, prolonged hospitalization, and intensive treatment during COVID-19. This study aimed to design, evaluate, and interpret machine learning models for predicting CDI occurrence in hospitalized COVID-19 patients across individual pandemic waves, with respect to administered treatment and clinical characteristics. Methods: Anonymized clinical data from 3848 COVID-19-positive patients treated at the University Hospital of L. Pasteur in Košice, Slovakia, were analyzed following the CRISP-DM methodology. Four classification models were compared: logistic regression, Random Forest, XGBoost, and a multilayer perceptron. Missing values were addressed using MICE and KNN imputation, and class imbalance was handled through oversampling techniques. Given the low CDI prevalence of 2.68%, model performance was primarily assessed using the precision–recall area under the curve (PR-AUC), with AUROC reported for comparability. Interpretability was supported using SHAP, LIME, and odds ratio analysis. Results: The best-performing models achieved PR-AUC values up to 0.160, representing more than a fivefold improvement over the random baseline of 0.027. XGBoost reached the highest AUROC of 0.823, followed by Random Forest with 0.798. Inflammatory markers were identified as important predictors of CDI risk. A Flask-based decision-support web application was developed to provide CDI risk estimation with patient-specific explanations. A preliminary pilot usability evaluation involving two physicians yielded a mean System Usability Scale score of 73.75; however, the very small evaluator sample limits the generalizability of this finding. Conclusions: Interpretable machine learning models can support clinically meaningful CDI risk stratification in highly imbalanced COVID-19 hospital datasets. The proposed decision-support tool shows potential for future integration into clinical workflows, although external and prospective validation is required. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly being integrated into vascular surgery, particularly in diagnostic imaging, perioperative planning, intraoperative guidance, and postoperative surveillance. This literature review evaluates the current applications of artificial intelligence in vascular surgery and endovascular practice, with a particular focus on imaging technologies and their role in improving diagnostic precision, workflow efficiency, and patient outcomes. In addition, the review examines emerging AI applications in operative workflow optimization, endovascular navigation, postoperative surveillance, training platforms, and AI-assisted clinical decision support. Methods: A literature review was conducted using PubMed and Scopus with the search terms: (artificial intelligence OR AI OR neural network) AND (vascular surgery) AND (diagnosis OR treatment). Reference lists of included studies were manually screened, and additional recent studies were identified from relevant journals. Articles published in English up to April 2026 were included. Studies were assessed for their applications in vascular diagnostics, plaque characterization, endovascular workflow optimization, and postoperative surveillance. Results: AI demonstrated strong diagnostic performance across multiple imaging modalities. Deep learning systems achieved a sensitivity of 91.3% and specificity of 95.2% in peripheral arterial stenosis classification, while plaque characterization models showed accuracies up to 96% and substantial agreement with expert imaging interpretation. AI-assisted operative systems improved procedural efficiency through reductions in operative duration, radiation exposure, and contrast utilization. However, many studies were retrospective, single-center, and based on relatively small cohorts with heterogeneous endpoints. Conclusions: AI has significant potential to improve vascular surgical practice through enhanced image interpretation, procedural guidance, and individualized treatment planning. Despite promising outcomes, current evidence remains limited by methodological heterogeneity and insufficient external validation. Prospective multicenter studies and standardized evaluation frameworks are required before widespread clinical implementation can be achieved. Full article

The main motivation for this study is to develop a predictive framework that provides high accuracy at lower computational and experimental costs, resulting in better decision-making in the chosen application domain. Artificial neural networks (ANNs) are widely used for prediction, classification, and pattern recognition tasks. However, their performance is sensitive to the selection of architectural and learning parameters. Hence, an important research challenge is the effective selection of architectural and learning parameters. Several hybrid GA–PSO approaches have been proposed, but most of the existing studies simultaneously optimize network architecture and trainable parameters or focus on a single application domain. However, there is still a lack of systematic framework that optimizes these components separately and validates its performance on multiple heterogeneous datasets. To fill this gap, this study proposes a novel hybrid optimization algorithm, called GAPSO, which combines the genetic algorithm (GA) and particle swarm optimization (PSO) for efficient tuning of artificial neural network (ANN) parameters. The proposed framework is evaluated on five benchmark datasets, including AirPassengers, Sunspots, Death and Injury, Earthquake, and Insurance. In the proposed approach, PSO is used for determination of optimal network architecture (number of hidden neurons) and GA is used for optimization of connection weights and threshold values. The experimental results demonstrate that for four out of five datasets, the lowest MAPE values were achieved by GAPSO-ANN, and were competitive compared to ANN, GA-ANN, PSO-ANN, LSTM and XGBoost models. Additionally, the Wilcoxon signed-rank test showed statistically significant performance improvements (p = 0.03125). Full article