Search Results (3,062)

Background: Open banking (OB) is rapidly transforming financial ecosystems by enabling controlled data sharing among multiple actors through application programming interfaces (APIs). While this transformation promises innovation and competition, it also introduces complex security challenges that extend beyond purely technical considerations. Despite growing attention in academic and professional domains, existing reviews provide limited integration of security concerns with global adoption patterns and cross regional variation. Methods: This systematic review analyses empirical and conceptual research on security in OB published between 1999 and 2025, capturing early digital banking studies that later informed the development of OB. The literature is structured into three distinct phases: foundational digital banking developments, regulatory formalisation of OB frameworks, and post-implementation expansion of OB ecosystems. A comprehensive search was conducted across major academic databases and scholarly portals, complemented by relevant regulatory and policy sources. Following duplicate removal, title and abstract screening, full-text eligibility assessment, and methodological quality appraisal, 117 studies were retained for qualitative synthesis. Results: The findings reveal recurring security challenges arising from the interaction between technological infrastructures, regulatory frameworks, and user behaviour within OB ecosystems. Technical safeguards such as APIs, strong customer authentication, and encryption are necessary but insufficient when they are misaligned with regulatory implementation and user behaviour. Behavioural factors, including trust, consent understanding, and security-related decision making, play a central role in shaping ecosystem resilience. Based on this synthesis, the study develops a tri-dimensional security framework integrating technological, regulatory, and behavioural dimensions. The bibliometric analysis of 117 studies reveals that technological security dominates the literature (58%), followed by regulatory governance (44%) and behavioural dimensions (42%). However, only 17.9% of studies integrate all three dimensions simultaneously. APIs and authentication mechanisms represent the most frequent technological terms, while PSD2 and GDPR dominate regulatory discourse. Trust and decision-making are the most recurrent behavioural constructs. The relatively low proportion of fully integrated studies confirms a structural fragmentation within OB security research, thereby empirically justifying the proposed tri-dimensional framework. Chronologically, early studies (1999–2015) predominantly focused on technical security mechanisms and regulatory compliance, whereas more recent research (2020–2025) increasingly highlights the interplay between regulatory frameworks and user behaviour, suggesting a shift towards a more holistic understanding of security within OB adoption. Conclusions: This systematic review concludes that integrating technological, regulatory, and behavioural perspectives advances a more comprehensive understanding of security in OB ecosystems. The proposed tri-dimensional security framework provides a structured foundation for future research and supports policy-relevant and practice-oriented security design. Full article

Accurate and robust control of robotic joints is essential for high-performance robotic systems. However, conventional proportional–integral–derivative (PID) controllers suffer from limited adaptability when applied to brushless direct current (BLDC) motor-driven joints operating under nonlinear and time-varying conditions. To address this issue, this paper proposes a Radial Basis Function (RBF) neural network-enhanced self-tuning PID control strategy. The RBF neural network serves as an online identifier to approximate the nonlinear dynamics of the BLDC motor and to estimate the system Jacobian online. Based on the estimated Jacobian, the PID gains (K_p, K_i, and K_d) are adaptively updated using a gradient descent mechanism, enabling continuous adjustment to varying operating conditions. Simulation and experimental results demonstrate that the proposed method achieves negligible overshoot, faster settling performance, and improved steady-state accuracy compared with conventional PID and PI controllers. In addition, the proposed controller exhibits enhanced disturbance rejection capability and robust performance under abrupt speed variations and start–stop conditions. The proposed approach effectively combines the simplicity of PID control with the adaptability of neural networks, providing a practical and efficient solution for high-precision robotic joint control. Full article

Coal gangue accumulation causes land occupation and environmental risks in mining areas, but its use as a planting substrate offers a pathway for ecological restoration. This study optimized a coal gangue-based planting substrate by integrating phosphogypsum, spent mushroom substrate, biochar, and a water retention agent. Using a three-factor, four-level orthogonal design with a fixed value of 10% phosphogypsum, the study assessed the effects of the coal gangue-to-spent mushroom substrate ratio, biochar, and water retention agent on substrate properties, nutrient availability, plant growth, and Pb leaching. The results showed that the tested formulations maintained substrate pH within a near-neutral range across treatments. The coal gangue-to-spent mushroom substrate ratio was the dominant factor controlling substrate structure, water-holding capacity, nutrient status, and ryegrass growth. Increasing the spent mushroom substrate proportion reduced bulk density and improved field water capacity, pore structure, and nutrient availability. Biochar most effectively reduced Pb leaching, although excessive application inhibited growth, whereas the water retention agent mainly enhanced water retention and plant growth. The optimal formulation was 80% coal gangue, 20% spent mushroom substrate, 1% biochar, 1.5 g kg⁻¹ water retention agent and 10% phosphogypsum, providing a theoretical and practical optimization basis for coal gangue reutilization and ecological restoration in mining areas. Full article

Airborne microplastics (MPs) are increasingly recognized as a pervasive pollutant with potential implications for environmental and human health. Despite growing concern, the influence of seasonal dynamics and environmental conditions on MP distribution remains poorly understood. This study investigates the temporal variability and environmental drivers of MPs across outdoor settings, highlighting how factors such as temperature, wind speeds, and precipitation modulate their behaviors. Using a combination of shielded gravitational deposition sampling (Sigma-2) and bulk deposition sampling over four seasons, coupled with μ-FTIR single particle analysis, we quantified MP abundance, size distribution, morphology, and polymer composition across contrasting environments. Deposition fluxes differed between samplers, with bulk samplers yielding 131–1589 MP/m²/d and Sigma-2 samplers yielding 4208–39,126 MP/m²/d. Multivariate analyses indicate that temperature was significantly correlated with MP loading in the Sigma-2 sampler, whereas precipitation effects were not detectable within the temporal resolution of our dataset. Polymer profiles differed between samplers, with Sigma-2 samples enriched in polyamide (PA) and resin-type particles, and bulk samples containing higher proportions of rubber and acrylate. Spherical and irregular particles were the predominant morphologies across both samplers. Together, these findings provide new insights into the environmental controls governing airborne MP deposition and underscore the need for long-term, meteorology-integrated, and methodologically standardized monitoring strategies to improve exposure assessment and inform mitigation efforts. Full article

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide. Despite their often-asymptomatic progression and complex therapeutic management, a substantial proportion of CVDs is preventable through early intervention and lifestyle modification. However, effective pharmacological strategies to fully reduce disease burden and associated risk factors remain limited. Polyphenols are a structurally diverse class of bioactive compounds widely distributed in plant-based foods, characterized by multiple phenolic and hydroxyl groups that confer potent redox-modulating properties. Increasing evidence indicates that dietary polyphenols exert cardioprotective effects through antioxidant, anti-inflammatory, and endothelial-modulating mechanisms. Experimental studies (in vitro and in vivo) have demonstrated that polyphenols regulate key molecular pathways involved in oxidative stress, inflammation, and vascular function, including PI3K/Akt/eNOS, AMPK/SIRT1, and Nrf2 signaling. In parallel, epidemiological and clinical evidence support their association with improvements in blood pressure, glycemic control, lipid profiles, and body weight, critical determinants of cardiovascular risk. Importantly, the biological response to polyphenol intake is highly variable and influenced by genetic background, metabolism, gut microbiota composition, and bioavailability constraints. This review provides an updated and integrative analysis of the molecular mechanisms underlying the cardioprotective effects of polyphenols, emphasizing their role in endothelial function and nitric oxide bioavailability. Additionally, it highlights recent advances in polyphenol-based nutraceuticals, discusses translational limitations, and outlines future perspectives for their application in cardiovascular disease prevention and management. Full article

Autism spectrum disorder (ASD) has been associated with alterations in the gut microbiota and its metabolites, particularly short-chain fatty acids (SCFAs) and microbiota-derived tryptophan catabolites, which may influence neurodevelopment through immune and epigenetic mechanisms. We investigated whether stool SCFAs and tryptophan-pathway metabolites differ between children with ASD and typically developing controls, and whether these metabolites associate with ASD severity and systemic biochemical signatures. In this cross-sectional study, we analyzed stool samples from 229 children (160 with ASD, 69 controls) with complete SCFA and tryptophan-metabolite data, while urine metabolomics data were available for a subset and were used for exploratory stool–urine integration analyses. Children with ASD and controls were similar in age, but the ASD group had a higher proportion of males. Absolute concentrations of individual SCFAs, total SCFAs, and derived indices were broadly comparable between groups; nominal differences in propionate/acetate ratio and caproate did not remain significant after false discovery rate correction. Similarly, stool tryptophan-pathway metabolites reported as ng/a.u. based on the NanoDrop-derived proxy (tryptophan, kynurenine, indole-3-acetic, indole-3-lactic, indole-3-propionic, indole-3-aldehyde, N-acetyl-tryptophan, serotonin, melatonin, tryptamine) and functional ratios (kynurenine/tryptophan, indole-derived/tryptophan, serotonin/tryptophan) showed no robust ASD–control differences; N-acetyl-tryptophan was nominally higher in ASD but did not survive multiple-testing correction. In the ASD subgroup with available Childhood Autism Rating Scale (CARS) data (n = 34), SCFA and tryptophan indices showed only weak, non-significant correlations with global ASD severity. In contrast, correlation analyses revealed two coherent metabolic modules, i.e., an SCFA block with very strong internal correlations among individual SCFAs and total SCFAs and a tryptophan block with strong correlations between metabolites and their normalized ratios, while cross-module correlations were modest. These results indicate that stool SCFA and microbiota-derived tryptophan profiles do not robustly distinguish ASD from controls in this cohort, but they form stable metabolic modules compatible with microbiome–epigenome frameworks. Full article

The secondary cooling water system is difficult to control because of loop coupling, thermal inertia, and strict actuator constraints. In addition, when conventional proximal policy optimization (PPO) uses Gaussian action sampling with clipping, the mismatch between sampled and executed actions may degrade learning and control smoothness near actuator limits. To address these issues, this paper develops a Beta-policy and PID-inspired augmented-state proximal policy optimization framework, termed BPAS-PPO, for the secondary cooling water system. The framework augments the state with proportional, integral, and derivative error features, adopts a Beta-distribution policy for bounded continuous-action generation, and uses a piecewise dense reward for the dual-loop tracking task. Simulation studies on an identified linear two-input two-output (TITO) model within the selected operating region show that the complete PID-augmented state yields the most effective training representation among the tested alternatives. Compared with PID, Fuzzy-PID, and Gauss-PPO, BPAS-PPO shows lower overshoot, shorter settling time, better setpoint tracking and disturbance rejection, and smoother control actions near actuator limits. The proposed framework is effective for the studied system within the selected operating region, while its performance beyond this region requires further validation. Full article

Low-frequency seismic disturbances significantly limit the performance of precision engineering systems and active vibration isolation platforms. Model predictive control (MPC) is widely applied in such systems due to its ability to handle multi-variable dynamics and constraints. However, its performance strongly depends on model accuracy. To address this issue, this paper proposes a multilayer perceptron-enhanced model predictive control (MLP-MPC) framework for active vibration isolation. In the proposed approach, a multilayer perceptron (MLP) is trained offline to learn the mapping between the current system state and the free-response term in the MPC prediction equation. During online implementation, the trained MLP replaces the model-based free-response calculation while preserving the original quadratic programming structure of conventional MPC. The proposed method is evaluated on a single-degree-of-freedom active vibration isolation system under low-frequency sinusoidal excitation and measured seismic disturbances. The simulation results show that MLP-MPC achieves reduced running RMS tracking error and lower moving-window RMS error compared with conventional MPC and Proportional–Integral–Derivative (PID) control. The results suggest that integrating data-driven free-response estimation into predictive control provides a practical approach to enhancing the performance of low-frequency vibration suppression while maintaining computational feasibility. Full article

This paper proposes improving the dynamic characteristics of a personal mobility device (PMD) battery charger using fuzzy logic control (FLC). Recently, as the use of PMD has expanded, research on the PMD battery charger has also been actively conducted. To control the output voltage of the PMD battery charger, a simple proportional–integral (PI) controller is mainly used. However, increasing the gains of the PI controller to improve the dynamic characteristics of the PMD battery charger can cause overshoot, oscillations, and sensitivity to disturbances. To overcome these limitations, this paper proposes an FLC-based output voltage control of the PMD battery charger. The proposed control method determined the reference inductor current using the membership functions and output intensity. In addition, the proposed control method exhibits improved dynamic characteristics compared to PI-based output voltage control in the transient state, reducing the settling time by approximately 55.26% and reaching a steady state stably without overshoot. The validity of the proposed FLC-based output voltage control method was verified through simulation and experimental results. Full article

This paper presents a novel proportional–integral–derivative (PID) control framework for first $\alpha$-order systems evolving in fractal time. The main contribution is the extension of classical control theory to systems exhibiting anomalous temporal scaling by employing local fractal derivatives. In contrast to fractional-order PID (FOPID) approaches, which primarily model memory effects, the proposed fractal PID framework captures time-scaling behavior arising in non-smooth environments, such as viscoelastic friction and irregular contact surfaces. The closed-loop dynamics are formulated as a second $\alpha$-order fractal differential equation, from which a characteristic equation is derived to establish conditions for asymptotic stability. It is shown that, for a constant reference input and positive controller gains, the tracking error converges to zero as $t\to \infty$. In addition, a quantitative performance analysis demonstrates that the fractal-order $\alpha$ governs temporal stretching: smaller values of $\alpha$ lead to increased rise and settling times and reduced oscillation frequency. The effectiveness of the proposed approach is illustrated through applications to a thermal system with fractal heat input and robotic actuators operating in irregular environments. These results highlight the potential of fractal-time control as a systematic framework for modeling and controlling dynamical systems with non-integer temporal structure. Full article

Industrial Control Systems (ICSs) face dual imperatives: protecting critical infrastructure from escalating cybersecurity threats while reducing the environmental impact of AI-powered defense mechanisms. Current deep learning anomaly detection approaches achieve security performance but consumes substantial computational resources, creating an environmental paradox in which AI solutions designed to protect infrastructure contribute to carbon emissions at scale. This competition between cybersecurity effectiveness and sustainability objectives intensifies as regulatory frameworks increasingly mandate both security resilience and environmental accountability. This research presents Green-USAD, a sustainability-first AI framework that inverts traditional design paradigms by integrating energy efficiency as a primary architectural constraint from inception rather than applying compression retrospectively. The proposed approach advances green computing for critical infrastructure through four key contributions: (1) a compressed architecture with validation-guided convergence protocols achieving competitive detection performance with minimal computational overhead; (2) a multi-objective optimization framework using the Analytic Hierarchy Process to systematically balance security and sustainability requirements; (3) a hardware-validated energy measurement methodology addressing reproducibility challenges in green AI literature; and (4) a comprehensive evaluation demonstrating cross-datasets and edge-deployment viability. Validation on ICS benchmarks demonstrates that sustainability-first design achieves substantial energy reduction while maintaining operational detection accuracy, with measured training consumption below 1% of conventional approaches and proportional carbon emission reductions. Comparative analysis against post hoc compression baselines establishes fundamental advantages of design-from-inception over train-then-compress paradigms. Edge device deployment on resource-constrained hardware confirms real-world applicability for distributed industrial environments. Results establish that robust cybersecurity and environmental sustainability represent unified rather than competing objectives when intelligent systems are designed with sustainability as a foundational principle. Full article

This paper presents the development and implementation of a two-degree-of-freedom, model-based controller designed to enhance the efficiency and flexibility of a certain class of circuits used in thermo-hydraulic applications. The controller addresses significant challenges such as time-variable transport delays and actuator coupling, which are common in dynamic testing environments. By utilising a model-based control approach and the Smith predictor configuration, the proposed controller simultaneously tracks supply temperature and mass flow rate with improved performance compared to the proportional–integrative–derivative (PID) controller used in our laboratory. The system’s effectiveness is demonstrated through a virtual hydraulic complex model developed in the OpenModelica environment and experimental tests, following the implementation of the controller in the laboratory real-time control software. Both the simulation and experimental results indicate that the controller can closely follow pre-programmed temperature and flow rate waveforms while effectively rejecting disturbances, with an RMSE reduction of up to about 80% under the specific test protocol used in this work, making it suitable for applications required to deal with the above constrains, such as laboratory dynamic testing and thermo-mechanical and chemical process regulation. Full article

Dust concentration control in coal-fired power plants is challenged by large time delays and various disturbances, particularly in dry electrostatic precipitator-wet flue gas desulfurization (DESP-WFGD) processes, where the inner-loop dynamics are slower than those of the outer loop, limiting the effectiveness of conventional cascade tuning methods. This paper proposes a compensation-based simultaneous tuning method for cascade proportional-integral-derivative (PID)-PID control systems. The cascade structure is transformed into an equivalent single-loop system, allowing the outer-loop controller to reshape the equivalent plant dynamics. An equivalent controller is then designed using the simple internal model control method, from which the inner-loop controller is derived. Controller parameters are iteratively refined based on maximum sensitivity, overshoot, and integral absolute error. A feedforward controller is further introduced to reject measurable outer-loop disturbances. Simulation results under nominal, uncertain, and noisy conditions show that the proposed method achieves zero overshoot, improved robustness, and smoother control action compared with conventional separate tuning and Lee’s simultaneous tuning method. The proposed approach provides an effective and practical solution for dust concentration control in DESP-WFGD processes, and is extendable to industrial cascade systems with similar dynamic characteristics. Full article

Ankylosing spondylitis (AS) is a chronic immune-mediated inflammatory disease affecting the axial skeleton, characterized by progressive structural damage and functional impairment. Although biologic therapies targeting tumor necrosis factor and interleukin-17 have improved clinical outcomes, a substantial proportion of patients fail to achieve sustained disease control. Emerging evidence suggests that metabolic alterations may contribute to AS pathogenesis; however, systematic characterization of metabolism-related biomarkers and their regulatory networks remains limited, and the interplay between metabolic dysfunction and immune dysregulation in AS is poorly understood. Two whole-blood GEO datasets (GSE25101, GSE73754; n = 104) were integrated as the primary analytical cohort. A third dataset (GSE11886, n = 18; monocyte-derived macrophages) was included for exploratory cross-tissue analysis. Differential expression analysis identified 847 DEGs, which were refined to 16 metabolism-related genes through weighted gene co-expression network analysis (WGCNA) and GeneCards database filtering. Eleven machine learning algorithms with 5-fold cross-validation were applied to construct diagnostic models and identify hub genes. Validation analyses included immune cell infiltration estimation using CIBERSORT, metabolic pathway activity assessment via ssGSEA, single-cell transcriptomics from GSE268839, functional enrichment through GSEA/GSVA, and chromosomal localization analysis. A competing endogenous RNA (ceRNA) regulatory network was constructed to map post-transcriptional regulation. Natural compounds from 66 AS-treating traditional Chinese medicines were screened against hub genes using deep learning-based binding prediction. Multiple machine learning algorithms achieved comparable cross-validated performance (CV AUC range 0.741–0.836; top five models: 0.805–0.836) using the six hub genes (MFN2, SLC27A3, RHOB, SMG7, AKR1B1, LCOR) identified through SHAP-based feature importance analysis of the PLS model. Leave-one-dataset-out validation between the two whole-blood cohorts showed that all algorithms exceeded an AUC of 0.77 in Round 1 (validate: GSE73754, n = 72; best AUC 0.861), while Round 2 (validate: GSE25101, n = 32) yielded more modest performance (best AUC, 0.715) reflecting the smaller validation sample. Exploratory application to GSE11886 (macrophage-derived samples) showed near-chance performance, consistent with the tissue-source discrepancy. AS patients exhibited significant downregulation of oxidative phosphorylation, TCA cycle, and glycolysis pathways (p < 0.01), accompanied by elevated glutathione metabolism (p < 0.001). Immune cell deconvolution revealed reduced CD8+ T cell proportions correlating with MFN2 downregulation, and increased neutrophil frequencies correlating with SLC27A3 upregulation. Exploratory single-cell analysis indicated that RHOB expression was relatively enriched in border-associated macrophages and fibroblasts, while AKR1B1 was more prominently expressed in vascular endothelial cells and plasmacytoid dendritic cells. The ceRNA network identified 21 miRNAs and 65 lncRNAs forming 86 regulatory interactions, with four key regulatory axes (SATB1-AS1/miR-539-5p/LCOR, FAM95B1/miR-223-3p/RHOB, LINC01106/miR-106a-5p/MFN2, AATBC/miR-185-5p/SMG7) predicted to regulate hub gene expression. Compound screening identified betaine, pyruvic acid, citric acid, etc., as top-ranking candidates, with MFN2 showing the highest binding capacity among hub genes. This study provides an integrative framework linking metabolic reprogramming with immune dysfunction in AS. The six-gene diagnostic signature showed preliminary discriminatory ability in the available datasets, while the ceRNA regulatory network and natural compound screening results prioritize candidate regulatory pathways and compounds for future validation. These findings advance our understanding of AS pathogenesis and may guide future biomarker development and targeted intervention strategies. Full article

Asymmetric Cascaded Multilevel Inverters (ACMLIs) have emerged as a prominent solution for medium- and high-power applications due to their ability to provide an increased number of output voltage levels with fewer power switches. However, maintaining low total harmonic distortion (THD) and ensuring robust stability under varying operating conditions remain significant challenges. This study experimentally validates a voltage-feedback Proportional-Resonant (PR) control strategy for a seven-level ACMLI. Unlike conventional current-feedback methods, the proposed approach directly regulates the output voltage, providing superior harmonic suppression and enhanced steady-state accuracy. The stability and dynamic performance of the controller were theoretically analyzed using Bode diagrams and root locus methods, and further verified through the MATLAB Curve Fitting Tool (CFT) with a high correlation (R² = 0.9989). Experimental results demonstrate that the integration of the PR controller significantly improves power quality, reducing the current THD from 6.55% to 3.68% and the voltage THD to 2.94%. These findings confirm that the system fully complies with IEEE 519 standards and outperforms several existing strategies in the literature. The results establish the voltage-feedback PR control as a robust, high-precision, and practical alternative for power quality-oriented multilevel inverter applications in modern energy systems. Full article