Search Results (11,239)

The heart is the body’s core pump. Heart failure impairs the heart’s ability to pump blood, leading to circulatory disorders. The artificial heart (blood pump) is an important mechanical circulatory support device that can partially or completely substitute cardiac pumping function, potentially improving hemodynamic performance and alleviating symptoms of heart failure. A combination of computational fluid dynamics simulation and hydraulic performance testing was used to study key parameters of the impeller, including blade count, blade wrap angle, impeller flow path, and diversion cone height. The goal was to reduce hemolysis risk and enhance pumping efficiency. Increasing the blade count raised the head, with optimal efficiency achieved at seven blades. A larger blade wrap angle decreased the head but improved efficiency. Synchronizing the flow path and diversion cone height at 4.1 mm maximized the head. Under various rotational speeds, the studied hemolysis index remained well below 0.1 g/100 L. Both experimental and simulation data were validated against each other, meeting the required error tolerances. The studied blood pump meets the design specifications. At an operating condition of 5 L/min flow rate and 2800 rpm, the pump achieves the required head and hemolysis criteria with a margin of safety. Full article

Fiducial marker implantation for tumor localization in radiotherapy is effective but invasive and carries complication risks. To address this, we propose a marker-less tumor tracking framework to explore the feasibility of a cross-patient deep learning model, aiming to eliminate the need for per-patient retraining. A novel degradation model generates realistic simulated data from digitally reconstructed radiographs (DRRs) to train a Restormer network, which transforms clinical fluoroscopic images into clean, DRR-like images. Subsequently, a DUCK-Net model, trained on DRRs, performs tumor segmentation. We conducted a feasibility study using a clinical dataset from 7 lung cancer patients, comprising 100 distinct treatment fields. The framework achieved an average processing time of 179.8 ms per image and demonstrated high accuracy: the median 3D Euclidean tumor center tracking error was $1.53$ mm, with directional errors of $0.98\pm 0.70$ mm (LR), $1.09\pm 0.74$ mm (SI), and $1.34\pm 0.94$ mm (AP). These promising results validate our approach as a proof-of-concept for a cross-patient marker-less tumor tracking solution, though further large-scale validation is required to confirm broad clinical applicability. Full article

Cryosat-2 SARin altimetric FBR data facilitates an opportunity to investigate phase differences between inland water radar reflections at the two antennae. With the antennae positioned cross-track, SARin was designed for the recovery of slope over ice margins, but here, it was used to recover off-pointing over inland waters. The ability to measure non-nadir off-pointing is verified using ocean data near the Amazon estuary to determine the satellite roll angle. Over inland waters, off-pointing requires correction to the nadir range and the geographic location of the reflectance. By using an SRTM-based water mask, the number of inland water reflectance increases significantly when off-pointing is considered. Comparisons between altimetric and river heights utilise gauge data at Tabatinga on the Solimões–Amazon. A least-squares adjustment yielded a river slope of −0.03506 ± 0.00003 m/km and a mean velocity of 1.803 ± 0.014 m/s over a river stretch of nearly 290 km. RMSE differences between the gauge and altimetry improve from 0.423 m to 0.404 m when off-pointing is taken into account for nadir inland water returns, showing the asymmetric effect of off-pointing. If all potential off-pointings are considered, the number of measurements increases by 66%, but the RMSE of 0.524 m is higher due to additional errors in the off-pointing corrections. Full article

Recirculating Aquaculture Systems (RAS) represent a high-density, controlled-environment fish farming method that requires constant monitoring of critical water quality parameters to ensure high water quality and fish stock health. Manual monitoring is labor-intensive and prone to error, creating a significant risk of catastrophic loss. This work presents the design and implementation of an automated monitoring system built on a Raspberry Pi platform that integrates multiple sensors (temperature, pH, conductivity, water level, and pumps’ functionality) to provide continuous, real-time data acquisition. A key feature is a software-based outlier rejection algorithm that enhances data integrity, and the code is freely available on the GitHub platform for further development. The collected data has been published on the ThingsBoard IoT platform for visualization and historical analysis via the HTTPS protocol. Furthermore, the system implements a proactive alerting mechanism using the Pushover notification service to deliver instant mobile alerts when parameters deviate from predefined thresholds. Commercial solutions cost in the order of thousands of euros, have high maintenance and operational costs, and pose integration and compatibility challenges. This solution provides a reliable, scalable, and cost-effective method for maintaining optimal conditions in a RAS, with hardware costs of less than EUR 150. Full article

This study primarily investigates the mechanical performance of prestressed anchor foundations. Based on the assumptions of continuity, homogeneity, and isotropy of the anchor foundation and anchoring materials, a simplified elastic analysis model was developed. Using the superposition principle, the working stresses under vertical loads and bending moments were calculated, allowing for the determination of the maximum working stresses within the anchors and the foundation. Additionally, the distribution of bond strength of the prestressed tendons was analyzed, and the concept of effective anchorage length was introduced. The reliability of the model was validated through prototype testing, with the measured free segment strain values showing a high degree of consistency with theoretical calculations, with errors within 6.5%. Empirical data on ultimate bearing capacity and bond characteristics were also obtained. By integrating numerical calculations with experimental results, the performance of the anchoring system under extreme and specialized loading conditions was analyzed. The experimental results indicated that the failure modes of all anchor foundations were characterized by bond failure at the interface between the anchor and the surrounding rock mass. Based on the experimental data, a reasonable anchorage length satisfying design strength requirements was proposed. The findings provide a theoretical foundation and practical guidance for the design and application of prestressed anchor foundations in structures such as wind turbine towers. Full article

When designing power electronic systems, it is crucial to correctly estimate the junction temperature of semiconductor devices, particularly power MOSFETs, under actual operating conditions. Thermal resistance is a parameter that characterizes the ability of these devices to dissipate internally generated heat under steady-state conditions. Determining the value of this parameter under specific cooling conditions requires dedicated measurements. This paper considers the widely used indirect electrical method of measuring thermal resistance. The influence of the dynamic properties of the measurement system, including the A/D converter, on the measurement error of the thermal resistance of power MOSFETs was analyzed. Using the constructed measurement system, it was demonstrated that, depending on the semiconductor material of the tested transistors, different error values were obtained, even with the same system configuration. The largest errors were observed for transistors made of silicon carbide. It was further shown that, with the applied A/D converter module, the measurement error can be limited to a few percent if recording of the thermal sensitive electrical parameter (TSEP) begins soon enough after the transients caused by the switchover from heating to TSEP measurement have fully decayed. Full article

Innovation is central to the Zero Hunger agenda, yet its distributional links to agricultural performance and policy in Eastern Europe remain unclear. This study investigates whether national innovation performance, proxied by the Global Innovation Index, is associated with agriculture’s macroeconomic weight and with public budget orientation in Bulgaria, Czechia, Hungary, Poland, Romania, and Slovakia across the past decade and a half. Using panel quantile regression with country fixed effects and bootstrapped standard errors, we estimate effects at the lower, median, and upper parts of the outcome distributions for three indicators: agriculture value added share of gross domestic product, the agriculture orientation index for government expenditures, and the agriculture share of government expenditure. Results show a robust negative association between innovation and the agricultural share of gross domestic product that strengthens toward the upper quantiles, consistent with structural transformation that reallocates value added toward higher-productivity sectors. For the orientation index, innovation is unrelated at the lower and median parts but becomes positive in mid–upper regimes, fading again at the extreme upper tail. No systematic relationship emerges for the budget share. Land endowment is positively associated with agricultural weight, while population size is negatively associated. We conclude that economy-wide innovation aligns with structural change, whereas shifting agricultural budget shares requires targeted, sector-specific policy instruments. Full article

Active exosuits provide various assistive force profiles but are limited by battery life, weight, and complex maintenance requirements. Passive exosuits, by contrast, are economical and lightweight while also offering unlimited usage times; however, due to their fixed stiffness levels, they can provide only a limited set of optimized assistive force profiles for different movements. To address these issues, this paper proposes a new variable elastic band for semi-passive exosuits. It comprises rubber bands and webbings connected in parallel, with the elongation of the rubber bands restricted according to the webbing length. By connecting these segments in series, a range of elasticities can be generated. Experimental results confirmed that the band could generate different stiffness levels, which were accurately predicted with an average coefficient of determination (R²) of 0.9985 and an average root mean square error of 0.8993. Additionally, based on tests involving participants wearing the device, the variable elastic band effectively modulated the assistive force profile. These findings overcome the previous limitations of passive components, opening the door to future research on enhancing the efficiency of passive systems and enabling further customization. Full article

Quantum machine learning (QML) integrates quantum computing with classical machine learning. Within this domain, QML-CQ classification tasks, where classical data is processed by quantum circuits, have attracted particular interest for their potential to exploit high-dimensional feature maps, entanglement-enabled correlations, and non-classical priors. Yet, practical realizations remain constrained by the Noisy Intermediate-Scale Quantum (NISQ) era, where limited qubit counts, gate errors, and coherence losses necessitate frugal, noise-aware strategies. The Data Re-Uploading (DRU) algorithm has emerged as a strong NISQ-compatible candidate, offering universal classification capabilities with minimal qubit requirements. While DRU has been experimentally demonstrated on ion-trap, photonic, and superconducting platforms, no implementations exist for spin-based quantum processing units (QPU-SBs), despite their scalability potential via CMOS-compatible fabrication and recent demonstrations of multi-qubit processors. Here, we present a pulse-level, noise-aware DRU framework for spin-based QPUs, designed to bridge the gap between gate-level models and realistic spin-qubit execution. Our approach includes (i) compiling DRU circuits into hardware-proximate, time-domain controls derived from the Loss–DiVincenzo Hamiltonian, (ii) explicitly incorporating coherent and incoherent noise sources through pulse perturbations and Lindblad channels, (iii) enabling systematic noise-sensitivity studies across one-, two-, and four-spin configurations via continuous-time simulation, and (iv) developing a noise-aware training pipeline that benchmarks gate-level baselines against spin-level dynamics using information-theoretic loss functions. Numerical experiments show that our simulations reproduce gate-level dynamics with fidelities near unity while providing a richer error characterization under realistic noise. Moreover, divergence-based losses significantly enhance classification accuracy and robustness compared to fidelity-based metrics. Together, these results establish the proposed framework as a practical route for advancing DRU on spin-based platforms and motivate future work on error-attentive training and spin–quantum-dot noise modeling. Full article

Critical nitrogen concentration (Nc) and accumulation (Na) throughout the entire growth period are key indicators for diagnosing N status and implementing precision N management in cut chrysanthemum. However, direct measurement of these two parameters is both time-consuming and destructive, and establishing accurate predictive models is fundamental to their practical application. From May 2021 to July 2022, five N-gradient experiments (ranging from 14 to 574 mgf·plant⁻¹) were conducted on the cut chrysanthemum cultivar ‘Nannong Xiaojinxing’. Predictive models for Nc and Na were developed using environmental light and temperature data during growth as driving variables. The results showed that the aboveground dry matter (DM) prediction model, which utilized the cumulative photo-thermal effect (PTE) derived from these environmental factors, demonstrated superior accuracy compared to models relying on conventional driving variables. Subsequently, the Nc and Na prediction models were established with DM as the driving variable. These models indicated that at a DM level of 1 g·plant⁻¹, Nc and Na values were 4.53% and 45.30 mg·plant⁻¹, respectively. The Na reached a maximum of 236.50 mg·plant⁻¹ at the flower harvesting stage, representing the minimum N accumulation required for optimal floral quality. Using the dry matter model as a process-based model, we successfully developed predictive models for Nc and Na driven by PTE. Validation using independent experimental data confirmed the models’ high predictive accuracy, with coefficients of determination of 0.9378 and 0.9612, and low errors—root mean square errors of 0.2736% and 19.18 mg·plant⁻¹, and normalized RMSE of 10.79% and 14.94%, respectively. These models provide a foundation for implementing precision N management and reducing fertilizer application in cut chrysanthemum production. Full article

Irreducible polynomials are widely used in modern cryptography; however, algorithms for finding such polynomials remain quite complex and require significant computational resources. In this study, a new approach to finding irreducible equations over Galois fields $GF\left(p\right)$ is proposed. It is shown that such irreducible equations can be obtained by solving a system of linear equations over the base Galois field, generated by any element of the field $GF\left(p^K\right)$ that is distinct from the elements of the base field and from elements corresponding to lower-degree extensions. The connection of the proposed approach with algorithms based on the Frobenius automorphism is established. The case corresponding to the field $GF\left(3\right)$ and matrices over this field is examined in detail. It has been shown that the proposed method makes it possible to obtain complete sets of irreducible polynomials over a given Galois field. It has also been demonstrated that generating such sets is of particular interest for the development of new methods of digital monitoring of the information space, which are based on analogies with error-correcting coding techniques. Full article

Stroke is a leading cause of long-term disability worldwide, necessitating efficient motor function assessment to guide personalized rehabilitation. The Fugl–Meyer Assessment for the Upper Extremity (FMA-UE) is a clinical gold-standard tool, but it is time consuming and requires trained clinicians, which limits its frequency of use and accessibility. While wearable sensors and deep learning offer promising avenues for remote assessment, accurately estimating detailed sub-scores of specific motor functions remains a significant challenge. This work introduces a deep learning framework for automated estimation of FMA-UE total and subdivision scores. Data was collected from 15 participants using four inertial measurement units (IMUs) positioned on the arm and trunk. Each participant performed seven specialized functional motions designed for comprehensive joint synergy involvement within ten minutes. A therapist-rated FMA-UE provided true scores. The proposed model leverages the integration of an LSTM-based autoencoder and mixup augmentation to enhance generalization and robustness. Evaluated through a leave-one-subject-out cross-validation (LOSOCV), the estimator demonstrated strong performance, achieving R² values exceeding 0.82. Pearson’s correlation coefficient r was more than 0.90, and the normalized root-mean-square errors (NRMSE) were below 0.14 for all subparts (A–D). Crucially, the total FMA-UE score was estimated with an NRMSE of 0.0678. These results show that a concise, sensor-based assessment can reliably predict detailed motor function scores. Full article

A parametric machine learning tool with many applications is the radial basis function (RBF) network, which has been incorporated into various classification and regression problems. A key component of these networks is their radial functions. These networks acquire adaptive capabilities through a technique that consists of two stages. The centers and variances are computed in the first stage, and in the second stage, which involves solving a linear system of equations, the external weights for the radial functions are adjusted. Nevertheless, in numerous instances, this training approach has led to decreased performance, either because of instability in arithmetic computations or due to the method’s difficulty in escaping local minima of the error function. In this manuscript, a three-stage method is suggested to address the above problems. In the first phase, an initial estimation of the value ranges for the machine learning model parameters is performed. During the second phase, the network parameters are fine-tuned within the intervals determined in the first phase. Finally, in the third phase of the proposed method, a local optimization technique is applied to achieve the final adjustment of the network parameters. The proposed method was evaluated on several machine learning models from the related literature, as well as compared with the original RBF training approach. This methodhas been successfully applied to a wide range of related problems reported in recent studies. Also, a comparison was made in terms of classification and regression error. It should be noted that although the proposed methodology had very good results in the above measurements, it requires significant computational execution time due to the use of three phases of processing and adaptation of the network parameters. Full article

Optical tracking of unknown space objects requires both spatial accuracy and temporal stability to enable high-resolution identification through narrow field-of-view sensors. Traditional performance indices such as RMS error and persistence time (PT) have been used for controller tuning, but they each capture only a subset of the requirements for successful optical identification. This paper proposes a new composite metric, the Persistence-Weighted Tracking Index (PWTI), which combines spatial precision and segment-level continuity into a single measure. The metric assigns a frame-level score based on positional error and accumulates weighted scores over the longest continuous in-threshold segment. Using PWTI as the optimization objective, a genetic algorithm (GA) is employed to tune the PID gains of a frame-by-frame offset correction controller. Comparative simulations under various observation scenarios demonstrate that the PWTI-based approach outperforms RMS- and PT-based tuning methods in both alignment accuracy and consistency. The results validate the proposed metric as a more suitable performance indicator for optical identification tasks involving unknown or uncataloged targets. Full article

Background: Computed tomography (CT)-based image-guided surgery (IGS) is of great importance in functional endoscopic sinus surgery (FESS) and requires IGS-specific imaging protocols to ensure high intraoperative accuracy. This study aimed to compare photon-counting CT (PCCT), dual-energy dual-source CT (DECT), and spectral detector CT (SDCT) of the paranasal sinuses with respect to image quality, IGS accuracy and radiation dose. Methods: A formalin-fixed cadaver skull was examined using PCCT, DECT and SDCT at 100 kV tube voltage with descending tube currents (mAs). The setup of electromagnetic IGS was evaluated using a visual analog scale. Accuracy was analyzed endoscopically using defined anatomical landmarks. Diagnostic image quality as well as bone and soft tissue noise were assessed qualitatively using a 5-point Likert scale and quantitatively by determination of signal-to-noise ratio. Radiation dose was evaluated using the dose length product. Results: While PCCT datasets could be registered and navigated accurately down to 10 mAs (1.5 mm error at 10 mAs), both DECT and SDCT exhibited significantly increased inaccuracies below 40 mAs (4.35/5.15 mm for DECT/SDCT at 25 mAs). Using PCCT therefore enabled a 45% radiation dose reduction at the minimally required dose length product using PCCT. Quantitative and qualitative image quality were superior for PCCT compared to DECT and SDCT. Conclusions: PCCT provides excellent accuracy of anatomical landmarks in IGS with superior image quality of the paranasal sinuses in low-mA scans and substantially reduced radiation exposure. Full article