Search Results (5,582)

Background: Renal Cell Carcinoma (RCC) is the most common type of kidney cancer and requires accurate histopathological grading for effective prognosis and treatment planning. However, manual grading is time-consuming, subjective, and susceptible to inter-observer variability. Objective: This study proposes EAT-Net (Efficient Attention Transformer Network), a dual-stream deep learning model designed to automate and enhance RCC grade classification from histopathological images. Method: EAT-Net integrates EfficientNetB0 for local feature extraction and a Vision Transformer (ViT) stream for capturing global contextual dependencies. The architecture incorporates Squeeze-and-Excitation (SE) modules to recalibrate feature maps, improving focus on informative regions. The model was trained and evaluated on two publicly available datasets, KMC-RENAL and RCCG-Net. Standard preprocessing was applied, and the model’s performance was assessed using accuracy, precision, recall, and F1-score. Results: EAT-Net achieved superior results compared to state-of-the-art models, with an accuracy of 92.25%, precision of 92.15%, recall of 92.12%, and F1-score of 92.25%. Ablation studies demonstrated the complementary value of the EfficientNet and ViT streams. Additionally, Grad-CAM visualizations confirmed that the model focuses on diagnostically relevant areas, supporting its interpretability and clinical relevance. Conclusion: EAT-Net offers an accurate, and explainable framework for RCC grading. Its lightweight architecture and high performance make it well-suited for clinical deployment in digital pathology workflows. Full article

Tensile tests of AlMg4.5 alloy were carried out at six strain rates to study the Portevin–Le Chatelier (PLC) effect. The measured engineering stress–time and engineering stress–engineering strain curves were evaluated by direct peak detection and reference function approximation. The waiting and decay times of the PLC effect, and the related stress jumps and drops, were determined. It was shown that, as a function of strain rate, the quotient of the decay and the waiting time forms a curve with a decreasing slope after an initial rapid rise; the same can be stated about the time derivative of the stress jumps. These relationships are suitable for identifying serrations that vary depending on the strain rate, in full harmony with the stress serration amplitudes observed in the tensile test diagrams. Full article

Liposomal systems are advanced carriers of active substances which, thanks to their ability to encapsulate these substances, significantly improve their pharmacokinetics, bioavailability, and selectivity. This article presents the results of spectroscopic studies for a selected compound from the 1,3,4-thiadiazole group, namely 4-[5-(naphthalen-1-ylmethyl)-1,3,4-thiadiazol-2-yl]benzene-1,3-diol (NTBD, see below in the text), in selected liposomal systems formed from the phospholipid 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC). Detailed spectroscopic analyses were carried out using electronic absorption and fluorescence spectroscopy; resonance light scattering (RLS) spectra measurements; dynamic light scattering (DLS); as well as time-resolved methods—fluorescence lifetime measurements using the TCSPC technique. Subsequently, based on the interpretation of spectra obtained by FTIR infrared spectroscopy, the preliminary molecular organization of the above-mentioned compounds within lipid multilayers was determined. It was found that NTBD preferentially occupies the region of polar lipid headgroups in the lipid multilayer, although it also noticeably interacts with the hydrocarbon chains of the lipids. Furthermore, X-ray diffraction (XRD) techniques were used to study the effect of NTBD on the molecular organization of DPPC lipid multilayers. Monomeric structures and aggregated forms of the above-mentioned 1,3,4-thiadiazole analogue were characterized using X-ray crystallography. Interesting dual fluorescence effects observed in steady-state fluorescence measurements were linked to the excited-state intramolecular proton transfer (ESIPT) effect (based on our earlier studies), which, in the obtained biophysical systems—liposomal systems with strong hydrophobicity—is greatly enhanced by aggregation-induced emission (AIE) effects. In summary, the research presented in this study, concerning the novel 1,3,4-thiadiazole derivative NTBD, is highly relevant to drug delivery systems, such as various model liposomal systems, as it demonstrates that depending on the concentration of the selected fluorophore, different forms may be present, allowing for appropriate modulation of its biological activity. Full article

A sliding mode predictive control (SMPC) scheme integrated with an extreme learning machine (ELM) disturbance observer is proposed for the trajectory tracking of a flexible air-breathing hypersonic vehicle (FAHV). To streamline the controller design, the longitudinal model is decoupled into a velocity subsystem and an altitude subsystem. For the velocity subsystem, a proportional-integral sliding mode surface is designed, and the control law is derived by minimizing a cost function that weights the predicted sliding mode surface and the control input. For the altitude subsystem, a backstepping control framework is adopted, with the SMPC strategy embedded in each step. Multi-source disturbances are modeled as composite additive disturbances, and an ELM-based neural network observer is constructed for their real-time estimation and compensation, thereby enhancing system robustness. The semi-globally uniformly ultimately bounded (SGUUB) stability of the closed-loop system is rigorously proven using Lyapunov stability theory. Simulation results demonstrate the comprehensive superiority of the proposed method: it achieves reductions in Root Mean Square Error (RMSE) of 99.60% and 99.22% for velocity and altitude tracking, respectively, compared to Prescribed Performance Control with Backstepping Control (PPCBSC), and reductions of 98.48% and 97.12% relative to Terminal Sliding Mode Control (TSMC). Under parameter uncertainties, the developed ELM observer outperforms RBF-based observer and Extended State Observer (ESO) by significantly reducing tracking errors. These findings validate the high precision and strong robustness of the proposed approach. Full article

The aim of this work was to investigate the evolution of the mechanical integrity of the selected offshore oil reservoir during its life cycle. The geomechanical stability of the reservoir formation, including the caprock and base rock, was investigated from the exploitation phase through waterflooding production to the final phase of enhanced oil recovery (EOR) with CO₂ injection. In this study, non-isothermal flow simulations were performed during the process of cold water and CO₂ injection into the oil reservoir as part of the secondary EOR method. The analysis of in situ stress was performed to improve quality of the geomechanical model. The continuous changes in elastic and thermal properties were taken into account. The stress–strain tensor was calculated to efficiently describe and analyze the geomechanical phenomena occurring in the reservoir as well as in the caprock and base rock. The integrity of the reservoir formation was then analyzed in detail with regard to potential reactivation or failure associated with plastic deformation. The consideration of poroelastic and thermoelastic effects made it possible to verify the development method of the selected oil reservoir with regard to water and CO₂ injection. The numerical method that was applied to describe the evolution of an offshore oil reservoir in the context of evaluating the geomechanical state has demonstrated its usefulness and effectiveness. Thermally induced stresses have been found to play a dominant role over poroelastic stresses in securing the geomechanical stability of the reservoir and the caprock during oil recovery enhanced by water and CO₂ injection. It was found that the injection of cold water or CO₂ in a supercritical state mostly affected horizontal stress components, and the change in vertical stress was negligible. The transition from the initial strike-slip regime to the normal faulting due to formation cooling was closely related to the observed failure zones in hybrid and tensile modes. It has been estimated that changes in the geomechanical state of the oil reservoir can increase the formation permeability by sixteen times (fracture reactivation) to as much as thirty-five times (tensile failure). Despite these events, the integrity of the overburden was maintained in the simulations, demonstrating the safety of enhanced oil recovery with CO₂ injection (EOR-CO₂) in the selected offshore oil reservoir. Full article

Accurate and timely precipitation forecasting is critical for climate risk management, agriculture, and hydrological regulation. However, this task remains challenging due to the dynamic evolution of atmospheric systems, heterogeneous environmental factors, and frequent missing data in multi-source observations. To address these issues, we propose DAHG, a novel long-term precipitation forecasting framework based on dynamic augmented heterogeneous graphs with reinforced graph generation, contrastive representation learning, and long short-term memory (LSTM) networks. Specifically, DAHG constructs a temporal heterogeneous graph to model the complex interactions among multiple meteorological variables (e.g., precipitation, humidity, wind) and remote sensing indicators (e.g., NDVI). The forecasting task is formulated as a dynamic spatiotemporal regression problem, where predicting future precipitation values corresponds to inferring attributes of target nodes in the evolving graph sequence. To handle missing data, we present a reinforced dynamic graph generation module that leverages reinforcement learning to complete incomplete graph sequences, enhancing the consistency of long-range forecasting. Additionally, a self-supervised contrastive learning strategy is employed to extract robust representations of multi-view graph snapshots (i.e., temporally adjacent frames and stochastically augmented graph views). Finally, DAHG integrates temporal dependency through long short-term memory (LSTM) networks to capture the evolving precipitation patterns and outputs future precipitation estimations. Experimental evaluations on multiple real-world meteorological datasets show that DAHG reduces MAE by $\stackrel{\sim }{3}$% and improves R² by $\stackrel{\sim }{0}$.02 over state-of-the-art baselines (p < 0.01), confirming significant gains in accuracy and robustness, particularly in scenarios with partially missing observations (e.g., due to sensor outages or cloud-covered satellite readings). Full article

The purpose of this work is twofold: first, it aims to derive an exact analytical form of the Pareto index based on the already developed model of the economy as a scale-free network comprising a given amount of either wealth or income (total number of links, each link representing a non-zero amount or quantum of income or wealth) distributed among its variable number of actors (nodes), all of whom have equal access to the system), and second, it aims to employ the derived analytical form of the Pareto index to determine the degree to which the observed inequality in wealth and in income as measured by the respective empirical values of the Pareto index is inherent in the economic system rather than the result of externally imposed factors invariably reflecting a lack of equal access. The derived analytical form of the Pareto index for wealth or for income is described by an exponential function whose exponent is the inverse of the average number of wealth or of income per actor (one-half of the average number of links per node) in the economic model. This exponent features prominently in the scale-free model of the economy and has a numerical value of 0.69 when the Pareto index attains a numerical value of 2, which signifies the optimal, albeit still unequal, distribution of wealth or of income in the economy under the condition of equal access. Because of the correspondence of the scale-free model of the economy to a physical system comprising quantum particles such as photons in thermodynamic equilibrium or state of maximum entropy in accordance with the laws of statistical mechanics, the inverse of the exponent is proportional to the temperature of the economic system, and a new parameter introduced to describe in a comprehensible manner the deviation in the economic system from its optimal distribution of wealth or income. A comparison of the empirical wealth and income Pareto indexes based on economic data for the four largest economies in the word, i.e., USA, China, Germany, and Japan, which account for over 50% of the global GDP, versus the corresponding optimal values per the scale-free model of the economy reveals interesting trends that can be explained away by the prevailing degrees of equal access, as manifested by inadequate education, health care, and housing, as well as the existence of rules and institutions favoring certain actors over others, particularly with regard to the accumulation of wealth. It has also been determined that the newly introduced parameters in the scale-free model of the economy of temperature as well as the quanta of wealth and of income should be expressed in power purchase exchange rates for meaningful comparisons among national economies over time. Full article

Terrain referenced navigation (TRN) determines position by comparing terrain height measurements with digital elevation maps (DEMs). However, terrain fluctuations create multimodal observation distributions, introducing significant nonlinearity that challenges fusion positioning algorithms. To address this, we propose a novel data fusion approach: batch cyclic posterior selection particle filter (BCPS-PF), applied to TRN. Our algorithm consists of two primary mechanisms. First, the batch cycle particle generation mechanism continuously generates particles conforming to the prior distribution. This is achieved by decomposing the state transition function and the state noise model during the prediction step. Particles from the previous time step are transformed via the state transition function, and noise sequences generated by the state noise model are added, forming batch cycle particles. Second, a particle selection mechanism filters particles to match the posterior distribution. This involves an update step in the fusion process, utilizing a rejection sampling technique. The batch cycle mechanism can be terminated by limiting the number of particles, and state estimation is derived by calculating the mean of these particles. Simulations demonstrate that our method improves positioning accuracy by over 10% compared with existing methods. Full article

This study proposes a discrete multi-agent Q-learning framework for the online tuning of PID controllers in continuous dynamic systems with limited observability. The approach treats the adjustment of each PID gain ($k_p$, $k_i$, $k_d$) as an independent learning process, in which each agent operates within a discrete state space corresponding to its own gain and selects actions from a tripartite space (decrease, maintain, or increase its gain). The agents act simultaneously under fixed decision intervals, favoring their convergence by preserving quasi-stationary conditions of the perceived environment, while a shared cumulative global reward, composed of system parameters, time and control action penalties, and stability incentives, guides coordinated exploration toward control objectives. Implemented in Python, the framework was validated in two nonlinear control problems: a water-tank and inverted pendulum (cart-pole) systems. The agents achieved their initial convergence after approximately 300 and 500 episodes, respectively, with overall success rates of $49.6\%$ and $46.2\%$ in 5000 training episodes. The learning process exhibited sustained convergence toward effective PID configurations capable of stabilizing both systems without explicit dynamic models. These findings confirm the feasibility of the proposed low-complexity discrete reinforcement learning approach for online adaptive PID tuning, achieving interpretable and reproducible control policies and providing a new basis for future hybrid schemes that unite classical control theory and reinforcement learning agents. Full article

In the eastern part of India, specifically in the coastal districts of Odisha, the Puri central canalsystem’s Phulnakhara distributary command, which is split between the districts of Cuttack and Khurda, is where the study was taken up during 2020 and 2021. The flow modelling of the Phulnakhara distributary command, covering a 49.03 km² area, was done by Visual MODFLOW (VMOD). The command area’s conceptual model was created by assigning various input data, and the developed model was calibrated with 1-year data (2020) and validated with 1-year data (2021) on a fortnightly basis for simulating the groundwater flow using VMOD. Both steady state and transient state circumstances were used to calibrate the hydraulic conductivity and storage coefficient for the various layers in 2020. The calibrated hydraulic conductivity values vary from 1.16 × 10⁻³ ms⁻¹ to 4.86 × 10⁻⁴ ms⁻¹, and the calibrated values (2.00 × 10⁻² m⁻¹ to 4.00 × 10⁻⁶ m⁻¹) of specific storage varied from the first to third layer in both state scenarios. The validated model could forecast the groundwater condition and the flow head for the following ten years, assuming a 0.5% annual drop in recharge by increasing the pumping rate five, six, and seven times throughout the validation period (2021). The modelling study suggested that the command area will not remain safe for 10 years from the point of future groundwater development. The model performance showed strong agreement between simulated and observed groundwater heads, with R² values ranging from 0.68 to 0.91 and NSE values between 0.64 and 0.88. Predictive simulations indicated groundwater drawdowns of 4.82 m, 5.72 m, and 6.11 m under 5×, 6×, and 7× pumping scenarios, respectively, over the next decade, highlighting a significant risk of depletion unless conjunctive use strategies are adopted. Full article

In recent years, the widespread adoption of photovoltaic (PV) panels for electricity generation has provided significant momentum toward sustainable energy goals. However, it has been observed that the accumulation of dust and contaminants on panel surfaces markedly reduces efficiency by blocking solar radiation from reaching the surface. Consequently, dust detection has become a critical area of research into the energy efficiency of PV systems. This study proposes SolPowNet, a novel Convolutional Neural Network (CNN) model based on deep learning with a lightweight architecture that is capable of reliably distinguishing between images of clean and dusty panels. The performance of the proposed model was evaluated by testing it on a dataset containing images of 502 clean panels and 340 dusty panels and comprehensively comparing it with state-of-the-art CNN-based approaches. The experimental results demonstrate that SolPowNet achieves an accuracy of 98.82%, providing 5.88%, 3.57%, 4.7%, 18.82%, and 0.02% higher accuracy than the AlexNet, VGG16, VGG19, ResNet50, and Inception V3 models, respectively. These experimental results reveal that the proposed architecture exhibits more effective classification performance than other CNN models. In conclusion, SolPowNet, with its low computational cost and lightweight structure, enables integration into embedded and real-time applications. Thus, it offers a practical solution for optimizing maintenance planning in photovoltaic systems, managing panel cleaning intervals based on data, and minimizing energy production losses. Full article

Background/Objectives: The drug development response to the unique pharmacology of fentanyl, which drives the current opioid epidemic, has primarily focused on increasing naloxone doses and employing longer-acting antidotes. While having lower withdrawal liability, the commonly perceived disadvantage of naloxone is its reduced effectiveness against re-narcotization or “fentanyl rebound,” due to a significant mismatch between its half-life (t_1/2) and that of fentanyl. Methods: We conducted a pharmacokinetic profile (PK) crossover study in fentanyl-sedated dogs to assess naloxone (NX) and its lipophilic prodrug (NX90) with regard to fentanyl PK and re-narcotization risk. Results: Our findings showed that naloxone redistributed fentanyl into the plasma, with correlating (R² = 0.9121) fentanyl and naloxone plasma levels when seven plasma samples per dog for each treatment (including placebo) were analyzed. This redistribution led to reductions in fentanyl’s volume of distribution at steady state (V_ss: 11.8 ± 1.7, 8.4 ± 2.4, and 8.7 ± 2.6 L/kg), mean residence time (MRT: 19.9 ± 1.8, 18.6 ± 7.2, and 16.2 ± 8.8 min), and half-life (t_1/2: 14.3 ± 1.9, 13.0 ± 4.9, and 11.2 ± 6.1 min) after the administration of a placebo, NX, and NX90, respectively. Additionally, we observed that the delay in the transient re-sedation (re-narcotization) of the dogs correlated (R² = 0.794) with naloxone’s exposure (AUC_inf). These data suggest that (i) the displacement of fentanyl into a metabolically active compartment and (ii) the delay in re-narcotization risk are both independent of naloxone’s half-life and are likely to be more effectively achieved with higher doses of naloxone. Conclusions: Combined with the lower risk of precipitating protracted withdrawal, these findings support the clinical use of higher-dose naloxone over longer-acting antidotes for reversing fentanyl-related overdoses. Full article

The increased penetration of power electronics interfaced resources in modern power systems is unlocking new opportunities and challenges. New concepts like multiport converters can further enhance the efficiency and power density of power electronics-based solutions. The triple active bridge is an isolated multiport converter with soft switching and high voltage gain that can integrate different sources, storage, and loads, or act as a building block for modular systems. However, the triple active bridge suffers from power flow cross-coupling, which affects its dynamic performance if it is not removed or mitigated. Unlike the extensive literature on two-port power converters, studies on modeling and control comparison for multiport converters are still lacking. Therefore, this paper presents and compares different modeling and decoupling control approaches applied to the triple active bridge converter, highlighting their benefits and limitations. The converter operation and modulation are introduced, and modeling and control strategies based on the single phase shift power flow control are detailed. The switching model, generalized full-order average model, and the reduced-order model derivations are presented thoroughly, and a comparison reveals that first harmonic approximations can be detrimental when modeling the triple active bridge. Furthermore, the model accuracy is highly sensitive to the operating point, showing that the generalized average model better represents some dynamics than the lossless reduced-order model. Furthermore, three decoupling control strategies are derived aiming to mitigate cross-coupling effects to ensure decoupled power flow and improve system stability. To assess their performance, the TAB converter is subjected to power and voltage disturbances and parameter uncertainty. A comprehensive comparison reveals that linear PI controllers with an inverse decoupling matrix can effectively control the TAB but exhibit large settling time and voltage deviations due to persistent cross-coupling. Furthermore, the decoupling matrix is highly sensitive to inaccuracies in the converter’s model parameters. In contrast, linear active disturbance rejection control and sliding mode control based on a linear extended state observer achieve rapid stabilization, demonstrating strong decoupling capability under disturbances. Furthermore, both control strategies demonstrate robust performance under parameter uncertainty. Full article

Autonomous navigation in GPS-denied, unstructured environments such as murky waters or complex seabeds remains a formidable challenge for robotic systems, primarily due to sensory degradation and the computational inefficiency of conventional algorithms. Drawing inspiration from the robust navigation strategies of marine species such as the sea turtle’s quantum-assisted magnetoreception, the octopus’s tactile-chemotactic integration, and the jellyfish’s energy-efficient flow sensing this study introduces a novel neuromorphic framework for resilient robotic navigation, fundamentally based on the co-design of marine-inspired sensors and event-based neuromorphic processors. Current systems lack the dynamic, context-aware multisensory fusion observed in these animals, leading to heightened susceptibility to sensor failures and environmental perturbations, as well as high power consumption. This work directly bridges this gap. Our primary contribution is a hybrid sensor fusion model that co-designs advanced sensing replicating the distributed neural processing of cephalopods and the quantum coherence mechanisms of migratory marine fauna with a neuromorphic processing backbone. Enabling real-time, energy-efficient path integration and cognitive mapping without reliance on traditional methods. This proposed framework has the potential to significantly enhance navigational robustness by overcoming the limitations of state-of-the-art solutions. The findings suggest the potential of marine bio-inspired design for advancing autonomous systems in critical applications such as deep-sea exploration, environmental monitoring, and underwater infrastructure inspection. Full article

Background: Heart failure (HF), as the end stage of various cardiac diseases, alters blood flow to key organs responsible for drug clearance. This can lead to unpredictable and often suboptimal drug exposure, creating a critical need for quantitative tools to guide precise dosing in this vulnerable population. Methods: This study aimed to establish a whole-body physiologically based pharmacokinetic (PBPK) model for characterizing drug pharmacokinetics in both healthy subjects and patients across the HF severity spectrum. Eight commonly used drugs (digoxin, furosemide, bumetanide, torasemide, captopril, valsartan, felodipine and midazolam) for treating HF and its comorbidities were selected. Following successful validation against clinical data from healthy subjects, the PBPK model was extrapolated to HF patients. Pharmacokinetics of the eight drugs in 1000 virtual HF patients were simulated by replacing tissue blood flows and compared using clinical observations. Results: Most of the observed concentrations were encompassed within the 5th–95th percentiles of simulated values from 1000 virtual HF patients. Predicted area under the concentration–time curve and maximum plasma concentration fell within the 0.5~2.0-fold range relative to clinical observations. Sensitivity analysis demonstrated that intrinsic renal clearance, unbound fraction in blood, muscular blood flow, and effective permeability coefficient significantly impact plasma exposure of digoxin at a steady state. Oral digoxin dosing regimens for HF patients were optimized via the validated PBPK model to ensure that steady-state plasma concentrations in all HF patients remain below the toxicity threshold (2.0 ng/mL). Conclusions: A PBPK model was successfully developed to predict the plasma concentration–time profiles of the eight tested drugs in both healthy subjects and HF patients. Furthermore, this model may also be applied to guide digoxin dose optimization for HF patients. Full article