Search Results (560)

In this work, we develop a disturbance suppression-oriented fuzzy sliding mode secured sampled-data controller for third-order parabolic partial differential equations that ought to cope with nonlinearities, hybrid cyber attacks, and modeled disturbances. This endeavor is mainly driven by formulating an observer model with a T–S fuzzy mode of execution that retrieves the latent state variables of the perceived system. Progressing onward, the disturbance observers are formulated to estimate the modeled disturbances emerging from the exogenous systems. In due course, the information received from the system and disturbance estimators, coupled with the sliding surface, is compiled to fabricate the developed controller. Furthermore, in the realm of security, hybrid cyber attacks are scrutinized through the use of stochastic variables that abide by the Bernoulli distributed white sequence, which combat their unpredictability. Proceeding further in this framework, a set of linear matrix inequality conditions is established that relies on the Lyapunov stability theory. Precisely, the refined looped Lyapunov–Krasovskii functional paradigm, which reflects in the sampling period that is intricately split into non-uniform intervals by leveraging a fractional-order parameter, is deployed. In line with this pursuit, a strictly $({\Phi }_1,{\Phi }_2,{\Phi }_3)-\varrho$ dissipative framework is crafted with the intent to curb norm-bounded disturbances. A simulation-backed numerical example is unveiled in the closing segment to underscore the potency and efficacy of the developed control design technique. Full article

Within the framework of brand communication, several companies choose to use bistable logos. These types of logos fall within the mechanisms inherent to bistable perception, where the interpretation of the two possible percepts involved may depend on the areas being observed or on prior instructions given to the observer to search for a particular shape within the ambiguous image. Perceptual factors related to the stimulus and the areas of eye fixation are called bottom-up aspects. The information exogenous to the bistable stimulus that determines perception is called top-down modulation. In order to determine whether certain bottom-up perceptual modulation areas for the Toblerone bistable logo are related to the search for each percept previously modulated by a written instruction, an experimental task was carried out with 34 participants using a Tobii T-120 eye tracker device, manufactured by Tobii in Danderyd, Sweden. Seven bottom-up modulation clusters were analyzed for ocular responses manifested in two different top-down modulation conditions. The results show that for each of the percepts, some areas correspond to the textual information offered as a top-down modulator. It is concluded that for the perception of the Toblerone^® logo, some areas are related to each percept, and the unimodal top-down modulation mechanisms operate in certain areas, while others can be assumed to be parts of the logo that contribute to the recognition of the two percepts involved. Full article

This article is aimed at assessing energy–economy models with a focus on their ability to capture the dynamic structural changes of economic systems and the related energy supply chains. A narrative literature review approach was employed, synthesizing relevant peer-reviewed research. The search yielded 229 publications spanning from 2015 to 2024. After applying screening criteria based on methodological transparency, quantitative modelling, and explicit energy–economy integration, 120 articles were retained, from which 23 representative modelling frameworks were selected. The review identifies five key dimensions shaping the realism and applicability of integrated models: geographical and temporal scope, technological detail, modelling approach, and the degree of micro- and macroeconomic realism. Results show a growing adoption of multi-scale modelling and a gradual shift toward hybrid structures combining technological and macroeconomic components. However, significant gaps remain: only 26% of the models move beyond equilibrium assumptions; 17% incorporate behavioural or heterogeneous agents; and almost half rely on exogenous technological change. Moreover, the representation of policy instruments—particularly performance standards, sectoral benchmarks, and public investment mechanisms—remains incomplete across most frameworks. Overall, this analysis highlights the need for more transparent coupling strategies, enhanced behavioural realism, and improved representation of financial and transition risks. These findings inform the methodological development of next-generation models and indicate priority areas for future research aimed at improving the robustness of policy-relevant transition assessments. Full article

The endogenous money creation paradigm posits that banks generate money through lending, with deposits serving as a byproduct. This study investigates the mechanism driving the “credit–deposit paradox” during Poland’s high-interest-rate environment, introducing innovative methodological approaches to quantify systemic monetary impairment. Using comprehensive monthly data from 2006 to 2024, we employ a mixed-methods framework featuring: (1) Bayesian vector autoregression with Minnesota priors to test dynamic interdependencies; (2) a novel money shortage indicator (MSI) that operationalizes credit–deposit decoupling through three theoretically grounded components; (3) Markov regime-switching analysis to identify persistent monetary stress regimes. Key findings reveal a structural decoupling between deposit growth and credit creation, with robust evidence that exogenous money inflows accumulate as idle deposits rather than stimulating lending. The economy experienced significant periods of money shortage conditions, with the most severe impairment occurring during recent high-stress periods. The analysis confirms the dominance of cost-push inflation from energy and food prices, while monetary factors played a limited role. High interest rates amplified credit demand suppression, creating conditions consistent with endogenous money creation disruption. Methodologically, this study enables three key advances: (1) systematic measurement of monetary transmission breakdowns; (2) empirical identification of structural factors disrupting credit–deposit dynamics; (3) temporal characterization of monetary stress persistence patterns. These contributions advance the endogenous money framework by demonstrating its vulnerability to behavioral, policy-induced, and exogenous disruptions during high-stress periods. Practically, the MSI offers policymakers a real-time diagnostic tool for identifying monetary transmission breakdowns, while the regime analysis informs targeted countercyclical measures. Specific policy recommendations include developing sector-specific liquidity facilities, coordinating fiscal transfers with monetary policy to prevent deposit–loan decoupling, and prioritizing supply-side interventions during cost-push inflation episodes. By integrating post-Keynesian theory with empirical evidence from Poland, this study contributes to understanding money creation mechanisms in highly stressed economic environments. Full article

In modern bioinformatics, the analysis of high-dimensional data (genomic, metabolomic, etc.) remains a critical challenge due to the “curse of dimensionality,” where feature redundancy reduces classification efficiency and model interpretability. This study introduces a novel method, AIMarkerFinder (v0.1.0), for analyzing metabolomic data to identify key biomarkers. The method is based on a denoising autoencoder with an attention mechanism (DAE), enabling the extraction of informative features and the elimination of redundancy. Experiments on glioblastoma and adjacent tissue metabolomic data demonstrated that AIMarkerFinder reduces dimensionality from 446 to 4 key features while improving classification accuracy. Using the selected metabolites (Malonyl-CoA, Glycerophosphocholine, SM(d18:1/22:0 OH), GC(18:1/24:1)), the Random Forest and Kolmogorov–Arnold Networks (KAN) models achieved accuracies of 0.904 and 0.937, respectively. The analytical formulas derived by the KAN provide model interpretability, which is critical for biomedical research. The proposed approach is applicable to genomics, transcriptomics, proteomics, and the study of exogenous factors on biological processes. The study’s results open new prospects for personalized medicine and early disease diagnosis. Full article

We study one of the world’s largest cattle markets by revisiting and extending previous work on the forecasting of Brazil’s Boi Gordo Index (BGI). Using an updated daily dataset (July 2006–September 2025, inflation-adjusted), we evaluate classical and machine learning (ML) approaches for price prediction. Methods include Exponential Smoothing (Simple, Holt, and Holt–Winters), ARMA/ARIMA/SARIMA, GARMA variants, GARCH, Theta, Prophet, and XGBoost; models are compared under a strictly chronological 90/10 holdout (~476 test days) using RMSE, MAE, and MSE, with the AIC guiding within-family selection. Results show that, for the full out-of-sample window, GARMA delivers the best overall accuracy, with ARMA and Holt–Winters close behind, while Prophet and XGBoost perform comparatively worse in this volatile setting. Performance is horizon-dependent: in the first 180 test days, prior to the late-2024 level shift, Holt attains the lowest RMSE/MSE, and XGBoost achieves the lowest MAE. No method anticipates the October–November 2024 exogenous jump and subsequent correction, highlighting the difficulty of structural breaks and the need for timely re-specification. We conclude that GARMA is a robust default for long, turbulent windows, whereas smoothing and ML methods can be competitive on shorter horizons. These findings inform risk measurement and risk mitigation strategies in Brazil’s cattle futures market. Full article

This paper presents a magnetic-flux-based encoder for BLDC drives that maintains high accuracy under rotor eccentricity and dynamic transients. Conventional Hall-sensor-based angle estimators rely on ideal sinusoidal flux assumptions and degrade in the presence of air-gap distortion or misalignment. To overcome these limitations, a nonlinear autoregressive network with exogenous inputs (NARXNet) is proposed as a temporal neural observer that learns the nonlinear, time-dependent mapping between measured flux densities and the true electrical rotor angle. A physics-informed data augmentation framework combines experimentally measured magnetic flux maps with dynamic simulation to generate diverse training scenarios at low and variable speeds. Validation demonstrates mean angular errors below 2°, 95th-percentile errors under 5°, and negligible drift, with enhanced resilience to eccentric displacement and acceleration transients compared to classical methods. The proposed approach provides a compact, data-driven sensing solution for robust, encoderless electric drive control. Full article

Non-starch polysaccharides (NSPs) in plant-based swine diets can reduce nutrient availability, and the use of exogenous NSP-degrading enzymes has been proposed as a practical approach to improve digestive utilization. This study examined the effects of a commercial enzyme mixture through both in vitro assessment and an in vivo trial in nursery pigs. The in vitro evaluation of seven commercial diets showed that enzyme supplementation increased dry matter, crude protein, crude fat, and crude fiber digestibility, with the most notable improvements observed in finisher, gestating, and lactating diets. In the in vivo experiment, pigs fed the enzyme-supplemented diet showed higher average daily gain (464 vs. 361 g/d; p = 0.07) and an improved feed conversion ratio (1.82 vs. 2.39; p = 0.07), together with numerically greater cecal butyrate concentration (2.13 vs. 1.06 mmol/dL; p = 0.10) and increased villous height in the jejunum and ileum. Although these responses did not reach statistical significance, they represent consistent trends that align with the in vitro findings and suggest potential benefits in nutrient utilization and gut morphology. Overall, the results indicate that NSP enzyme supplementation may support digestive function under specific dietary conditions, particularly in diets containing moderate to high NSP levels, and provide useful information for its practical application in swine nutrition. Full article

This paper introduces a novel control archetype designed to mitigate high-altitude electromagnetic pulse (HEMP) $E_3$ disturbances on the power grid, as well as information on performance and specifications of different control laws for the controller archetype. This method of protection has been overlooked in the literature until now. A controlled voltage supply is placed on the load-side of a transformer, diverting unwanted power from the transformer core to prevent saturation. The controlled voltage source is modeled using four control laws: an integral controller (capacitor), Linear Quadratic Regulator (LQR), an energy storage minimized feedforward control law, and a Hamiltonian feedback law. Results show that the Hamiltonian feedback law and the energy storage minimization feedforward control law both flat-line magnetic flux with similar actuator requirements. The LQR approach requires less energy storage than the other two laws, depending on control tuning, as it allows greater exogenous current flow through the neutral path to ground. This leads to further optimization opportunities based on acceptable exogenous current levels. A sweep of different LQR gains revealed a reduction of approximately $32\%$ in minimum control effort, $47\%$ in minimum power to maintain saturation bounds, $20\%$ in energy storage requirements, and $59\%$ in required controller bandwidth. Voltage and bandwidth requirements of the load-side controller are comparable to neutral blocking requirements with energy and power requirements being higher for the load-side controller. This, however, comes with the benefit of being able to use pre-existing assets—neutral blocking devices have not been deployed. Additionally, the load-side blocking capacitor degrades transformer performance compared to the unmitigated system. Full article

Exogenous carbon addition is widely regarded as an effective soil management strategy for rapidly increasing soil organic carbon, improving soil structure and function. However, a systematic comparison of the effects of diverse organic amendments on key soil attributes and processes is needed to inform their targeted application. We evaluated the impacts of seven organic amendments (biochar, organic fertilizer, corn straw, soybean straw, rapeseed straw, green manure, and carbon material) on a purple soil (Luvic Xerosols) in a pot experiment. The results showed that organic fertilizer and carbon material performed best in enhancing soil nutrient availability and promoting soil organic carbon content. Straw amendments promoted the formation of macro-aggregates. Green manure and straws enhanced carbon transformation-related β-glucosidase and cellobiohydrolase activities. Random Forest and structural equation modeling indicated that the organic amendments enhanced maize carbon sequestration capacity and biomass by improving aggregate stability and regulating the fungal community and by increasing nutrients and enhancing active carbon fractions. Green manure and organic fertilizer demonstrated the most significant agronomic effects. These findings provide guidelines for targeted organic amendment selection in purple soil regions. Full article

Background: Evidence on HAIs in Africa is fairly common. Objectives: The main objective was to identify the surveillance tools used for healthcare–associated infections (HAIs) in countries in the WHO African Region. Secondary objectives focused on the organization of surveillance, the pathogens involved, and the frequency of multidrug–resistant species. Inclusion and exclusion criteria: Observational or interventional studies on healthcare–associated infections in humans, published between January 2011 and December 2024, in French or English, were included. However, the following publications were not included: animal studies, healthcare–associated infections not related to healthcare, literature reviews, studies outside the period or geographical area, and studies in languages other than French or English. Sources of information and search date: The databases consulted were PubMed, Web of Science, EMBASE, Cochrane, African Index Medicus, Google Scholar, and AJOL. The search was conducted between January and March 2025. Risk of bias assessment: The risk of bias was assessed using a specific grid (eleven criteria), scored from one (low) to three (high). The studies were classified into three levels of methodological quality. The results of the bias assessment showed that the publications were excellent (strong and moderate) with a cumulative rate of 99.9%. Methods of synthesizing results: Data were extracted using a standardized grid and synthesized narratively. No meta–analysis was performed. Number of studies and characteristics: 95 studies were included, mostly cross–sectional studies (82.1%), cohorts (10.4%), and a few case reports. Most were from West Africa (60.0%), particularly Nigeria (16.8%) and South Africa (14.7%). Main results: • Most common pathogens: Staphylococcus aureus (53.7%), Escherichia coli (43.2%), Klebsiella pneumoniae (32.6%). • Resistance profile: ESBL (27.4%), MRSA (21.1%), multidrug resistance (13.7%). • Sources of HAIs: mainly exogenous (83.2%). • Laboratory methods: phenotypic (70.5%), genotypic or genomic rare (3.1%). • Scope of studies: local (96.8%), national (3.2%). Limitations of evidence: Risk of bias due to underreporting of HAIs, methodological heterogeneity, predominance of cross–sectional studies, low use of molecular methods, lack of modeling, and uneven geographical coverage. Overall interpretation and implications: surveillance of HAIs in Africa remains fragmented and poorly standardized. There is a need to strengthen national systems, integrate molecular methods, train professionals, and promote interventional research. The WHO GLASS program can serve as a framework for harmonizing surveillance. Full article

By employing the 2017 reform of China’s financial statement presentation as an exogenous shock, we evaluate how the change shapes the likelihood of stock price crashes. Our analysis indicates that firms affected by the reform exhibit notably higher crash risk after the new reporting format is adopted, and this finding remains consistent across multiple robustness checks. The increase in crash risk can be largely attributed to managerial incentives to manage earnings by reclassifying held-for-sale assets and other special items. Moreover, the reform exerts a stronger effect on firms that exhibit poor information transparency and receive little oversight from internal and external monitors. Full article

The accurate forecasting of electricity sales volumes constitutes a critical task for power system planning and operational management. Nevertheless, subject to meteorological perturbations, holiday effects, exogenous economic conditions, and endogenous grid operational metrics, sales data frequently exhibit pronounced volatility, marked nonlinearities, and intricate interdependencies. This inherent complexity compounds modeling challenges and constrains forecasting efficacy when conventional methodologies are applied to such datasets. To address these challenges, this paper proposes a novel decomposition–integration forecasting framework. The methodology first applies Variational Mode Decomposition (VMD) combined with the Zebra Optimization Algorithm (ZOA) to adaptively decompose the original data into multiple Intrinsic Mode Functions (IMFs). These IMF components, each capturing specific frequency characteristics, demonstrate enhanced stationarity and clearer structural patterns compared to the raw sequence, thus providing more representative inputs for subsequent modeling. Subsequently, an improved RevInformer model is employed to separately model and forecast each IMF component, with the final prediction obtained by aggregating all component forecasts. Empirical verification on an annual electricity sales dataset from a commercial building demonstrates the proposed method’s effectiveness and superiority, achieving Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Squared Percentage Error (MSPE) values of 0.044783, 0.211621, and 0.074951, respectively—significantly outperforming benchmark approaches. Full article

Thanks to both endogenous and exogenous antioxidants (AOs), the antioxidant defense system ensures redox homeostasis, which is crucial for protecting the body from oxidative stress and maintaining overall health. The food industry also exploits the antioxidant properties to prevent or delay the oxidation of other molecules during processing and storage. There are many classical methods for assessing antioxidant capacity/activity, which are based on mechanisms such as hydrogen atom transfer (HAT), single electron transfer (SET), electron transfer with proton conjugation (HAT/SET mixed mode assays) or the chelation of selected transition metal ions (e.g., Fe²⁺ or Cu¹⁺). The antioxidant capacity (AOxC) index value can be expressed in terms of standard AOs (e.g., Trolox or ascorbic acid) equivalents, enabling different products to be compared. However, there is currently no standardized method for measuring AOxC. Nanoparticle sensors offer a new approach to assessing antioxidant status and can be used to analyze environmental samples, plant extracts, foodstuffs, dietary supplements and clinical samples. This review summarizes the available information on nanoparticle sensors as tools for assessing antioxidant status. Particular attention has been paid to nanoparticles (with a size of less than 100 nm), including silver (AgNPs), gold (AuNPs), cerium oxide (CeONPs) and other metal oxide nanoparticles, as well as nanozymes. Nanozymes belong to an advanced class of nanomaterials that mimic natural enzymes due to their catalytic properties and constitute a novel signal transduction strategy in colorimetric and absorption sensors based on the localized surface plasmon resonance (LSPR) band. Other potential AOxC sensors include quantum dots (QDs, <10 nm), which are particularly useful for the sensitive detection of specific antioxidants (e.g., GSH, AA and baicalein) and can achieve very good limits of detection (LOD). QDs and metallic nanoparticles (MNPs) operate on different principles to evaluate AOxC. MNPs rely on optical changes resulting from LSPR, which are monitored as changes in color or absorbance during synthesis, growth or aggregation. QDs, on the other hand, primarily utilize changes in fluorescence. This review aims to demonstrate that, thanks to its simplicity, speed, small sample volumes and relatively inexpensive instrumentation, nanoparticle-based AOxC assessment is a useful alternative to classical approaches and can be tailored to the desired aim and analytes. Full article

Mine-induced surface subsidence threatens infrastructure and can trigger cascading geohazards, so accurate and computationally efficient monitoring and forecasting are essential for early warning. We integrate Persistent Scatterer InSAR (PS-InSAR) time series with a Bayesian-optimized CNN–LSTM designed for spatiotemporal prediction. The CNN extracts spatial deformation patterns, the LSTM models temporal dependence, and Bayesian optimization selects the architecture, training hyperparameters, and the most informative exogenous drivers. Groundwater level and backfilling intensity are encoded as multichannel inputs. Endpoint anchoring with affine calibration aligns the historical series and the forward projections. PS-InSAR indicates a maximum subsidence rate of 85.6 mm yr⁻¹, and the estimates are corroborated against nearby leveling benchmarks and FLAC^3D simulations. Cross-site comparisons show acceleration followed by deceleration after backfilling and groundwater recovery, which is consistent with geological engineering conditions. A symmetry-aware preprocessing step exploits axial regularities of the deformation field through mirroring augmentation and documents symmetry-breaking hotspots linked to geological heterogeneity. These choices improve generalization to shifted and oscillatory patterns in both the spatial CNN and the temporal LSTM branches. Short-term forecasts from the BO–CNN–LSTM indicate subsequent stabilization with localized rebound, highlighting its practical value for operational planning and risk mitigation. The framework combines automated hyperparameter search with physically consistent objectives, reduces manual tuning, enhances reproducibility and generalizability, and provides a transferable quantitative workflow for forecasting mine-induced deformation in complex goaf systems. Full article