Search Results (448)

In this paper, we study a system of 10 equations containing a parameter—a non-negative integer n—and associated with the problem of filling a special table with natural numbers. As a result, by developing a hybrid approach—first, by proving an a priori estimate for the solution of the system, which implies a finite set of solutions, thereby significantly narrowing the search space for a solution, and second, by performing a computer calculation of all remaining vectors satisfying the a priori estimate—we established that the system is not solvable for all values of the parameter n. We also prove a criterion for $(x_0,x_1,\dots ,x_9)\in {\mathbb{N}}^{10}$ to be a solution to the system for some value of the parameter n. Furthermore, we prove a fact that, in particular, implies that the set of values of the parameter n for which the system has at least 10 solutions is countable. Full article

The possibility of determining the phase and current errors of an existing or newly designed current instrument transformer on the basis of special characteristics of the core material is examined. One of the characteristics represents the dependence between the magnetic field intensity on the core sheet surface, measured at the instant when induction is at its peak, and the mean peak induction in the cross section of the sheet. The other characteristic represents the dependence between the field intensity value measured at the instant when induction passes through zero and the peak induction value. The characteristics must be determined for the sinusoidal shape of the induction curve. The secondary winding of the current instrument transformer should be uniformly distributed along the core. One must know the following: the number of turns in the primary and secondary winding, respectively, the resistance of the secondary winding and the resistance at the secondary winding output when the primary current is being converted. Indicated relations provide a clear formula for designing effective current transformers. The main contribution of this paper is to present the method for estimating the parameters of current transformer a priori, relying on characteristics of the core material. However, this formula combined with elements of artificial intelligence—nature-inspired optimization algorithms—results in a convenient tool for optimal core geometry design. The paper presents an extension of the method to a smart design approach with application of the Birch-inspired Optimization Algorithm (BiOA). Full article

BACKGROUND. Managing women in pregnancy or labour is becoming a serious challenge because of delayed conception age and higher morbidity. The main negative factor is increasing numbers of overweight and obese women. Fatty tissue significantly biases the detection of uterine contractions by tocography, which is routinely used in obstetrical wards. Thus, the FDA approved an alternative method called electrohysterography (EHG) and recommended it for women with an over-normal BMI. However, almost all published methods of labour prediction based on EHG signals use a “black-box model” approach, i.e., increasingly numerically complex signal features and classification algorithms that are chosen a priori, without any physiological rationale behind them. This makes using these algorithms difficult in obstetrical practice. AIM. The aim of the study was to show that a simple classifier based on a single and physiologically interpretable parameter can predict uterine contractions during labour with an accuracy comparable to advanced classifiers. METHODS. An obstetrical interpretable EHG parameter was introduced called the uterine activity index. To avoid the influence of confounding factors associated with preterm labour and imbalanced signal sets, this classifier was evaluated using the private, retrospective database of EHG signals registered for 45 women in the third trimester of a pregnancy, and 31 women in the second stage of labour with a normal BMI. The classifier, based on the logistic regression model, was tested using the bootstrap method. RESULTS. The bootstrapping mean (95% confidence interval) of the AUC ROC estimated for the 200 bootstrap samples was 0.96 (0.91–0.99). This accuracy was slightly better for EHG signals in comparison to predictions based on classical tocography. CONCLUSIONS. The obtained results confirm that a simple physiologically explained classifier can be considered in commercial applications of electrohysterography. However, its clinical significance should be evaluated through properly designed randomised clinical trials. Full article

Radar backscatter from large-scale scenarios plays a crucial role in remote sensing applications. However, due to the diversity and heterogeneity of the natural environment, traditional empirical methods which rely on simplified physics and a limited set of parameters, fail to adequately model land backscatter, thus exhibiting significant limitations. While purely data-driven deep learning (DL) methods offer flexibility, they often struggle to ensure physical consistency and effectively generalize to unseen scenarios. To address these issues, we propose a novel knowledge-aided (KA) DL-based method (called KADL) in this paper for predicting the radar backscatter from large-scale scenarios. The proposed KADL is implemented in three parts. First, based on multi-source remote sensing data, the dielectric properties of land surface, i.e., soil moisture and leaf area index (LAI) are incorporated as priori physical knowledge into the Multi-Feature Clutter Dataset (MFCD) to obtain initialized input. Second, a knowledge perception module (KPM) is introduced into the cascaded deep neural network (DNN) solver to exploit the representative features within the inputs. Third, an efficient knowledge-weighted fusion (KWF) strategy is developed to further enhance the discriminative features and simultaneously suppress the non-informative features. For better comparison, we refitted the specific empirical models based on the measured data and introduced an advanced nonhomogeneous terrain clutter model (termed ANTCM) derived from our previous work. Extensive experiments conducted on the measured data demonstrate that KADL achieves a root mean square error (RMSE) of 4.74 dB and a mean absolute percentage error (MAPE) of 8.7% on independent test data. Furthermore, KADL exhibits superior robustness, with a standard deviation of RMSE as low as 0.18 dB across multiple trials. All these results validate the superior accuracy, robustness, and generalization ability of KADL for large-scale backscatter prediction. Full article

Sustainable materials engineering necessitates the valorization of industrial by-products, such as coal fly ash, into functional, high-performance materials. This research addresses a core challenge in materials synthesis: establishing a deterministic technology for controlled porous structure formation to optimize the thermophysical properties of lightweight thermal insulation composites. The primary objective was to investigate the structural evolution kinetics during the high-intensity thermal processing of fly ash-based precursors to facilitate precise property regulation. We developed a novel, integrated process, underpinned by mathematical modeling of simultaneous bloating and non-equilibrium heat transfer, to evaluate key operational parameters within a vortex-layer reactor (VLR). This framework enables the a priori prediction of structural outcomes. The synthesized composite granules were subjected to comprehensive characterization, quantifying apparent density, total porosity, static compressive strength, and effective thermal conductivity. The developed models and VLR technology successfully identified critical thermal exposure windows and heat flux intensities of the heating medium required for the reproducible regulation of the composite’s porous architecture. This precise structure process control yielded materials exhibiting an optimal balance between low density (<400 kg/m³) and adequate mechanical integrity (>1.0 MPa). This work validates a scalable, energy-efficient production technology for fly ash-derived porous media. The established capability for predictive control over microstructural development provides a robust engineering solution for producing porous materials, significantly contributing to waste reduction and sustainable building practices. Full article

Hyperparameters allow metaheuristics to be tuned to a wide range of problems. However, even though formalized tuning of metaheuristic parameters can affect the quality of the solution, it is rarely performed. The empirical selection method and the trial-and-error method are the primary conventional parameter selection techniques for optimization heuristics. Both require a priori knowledge of the problem and involve multiple experiments requiring significant time and effort, yet neither guarantees the attainment of optimum parameter values. Of the studies that perform formal parameter tuning, experimental design is the most commonly used method. Although experimental design is feasible for systematic experimentation, it is also time-consuming and requires extensive effort for large optimization problems. The computational effort in this study refers to the number of experimental runs required for hyperparameter tuning, not the computational time for each run. This study proposes a simpler, faster method based on an optimized Latin hypercube sampling (OLHS) technique augmented with response surface methodology for estimating the best hyperparameter settings for a hybrid simulated annealing algorithm. The method is applied to solve the aircraft landing problem with time windows (ALPTW), a combinatorial optimization problem that seeks to determine the optimal landing sequence within a predetermined time window while maintaining minimum separation criteria. The results showed that the proposed method improves sampling efficiency, providing better coverage and higher accuracy with 70% fewer sample points and only 30% of the total runs compared to full factorial design. Full article

In this paper, we study a backward problem for a fractional Rayleigh–Stokes equation by using a quasi-boundary value method. This problem is ill-posed; i.e., the solution (if it exists) does not depend continuously on the data. To overcome its instability, a regularization method is employed, and convergence rate estimates are derived under both a priori and a posteriori criteria for selecting the regularization parameter. The theoretical results demonstrate the effectiveness of the proposed method in deriving stable and accurate solutions. Full article

Lithium-ion batteries have been extensively utilized as a high-power, rechargeable, and dischargeable energy storage medium. Accurate estimation of the battery state of charge (SOC) in the battery management system (BMS) is imperative for ensuring the safe and stable operation of electric vehicles. This paper proposes an SOC estimation method based on the equivalent circuit model as well as the ampere-time integration method with a physical informed neural network. The network enhances the estimation of SOC by introducing two mechanistic information sources: the equivalent circuit model (ECM) and the ampere-time integration method (Ah-I method). These are utilized as a priori knowledge to constrain the estimation of SOC. Initially, the Rint model is selected as the physical analysis model of the lithium-ion battery, and subsequently, the Ah-I method is chosen as the auxiliary model for SOC output estimation. A deep learning network is then employed to establish the mapping between the battery input parameters and the SOC output. Finally, the SOC is estimated by fusing the physical model and the data-driven model. The results demonstrate the efficacy of the method in accurately estimating the state of charge of lithium batteries, with a root mean square error within 1%. The validity of the research methodology was further validated through comparison with other approaches. Full article

Airplane cabin temperature is a critical environmental factor governing the safety and reliability of airborne equipment. Compared with measuring temperature, predicting temperature is more cost- and time-saving and can cover an extreme flight envelope. Physics-informed neural networks (PINNs) offer a promising prediction solution whose performance hinges on the availability of precise governing differential equations. However, building governing differential equations between flight parameters and cabin temperature is a great challenge, as it is comprehensively influenced by aerodynamic heat, avionic heat, and internal flow. To solve this, a new PINN framework based on “a priori monotonicity” is proposed. Underlying physical trends (monotonicity) from flight data are extracted to construct the loss function as a data-driven constraint, thus eliminating the need for any governing equations. The new PINN is developed to estimate the seven cabin temperatures of an unmanned aerial vehicle. The model was trained on data from four flight sorties and validated on another four independent sorties. Results demonstrate that the proposed PINN achieves a mean absolute error of 1.9 and a root mean square error of 2.6, outperforming a conventional neural network by approximately 35%. The core value of this work is a new PINN framework that bypasses the development of complex governing equations, which enhances its practicality for engineering applications. Full article

Clinical trial enrichment is the targeted recruitment of prospective individual patients with defined clinical characteristics who are likely to benefit from newly developed or repurposed drugs. This process is central to the success of clinical trials together with patient management and regulatory compliance. A main challenge in clinical trial enrichment lies in the recognition of a priori clinical parameters and information that informs drug efficacy or toxicity, particularly when intended for a broader unseen population. Although Artificial Intelligence (AI) approaches, especially large language models (LLMs), have been employed in many aspects of clinical trials, to our knowledge, there is no AI method that has been developed which offers a prospective prediction and assesses the extent to which a given therapeutic intervention benefits an unseen population. Here, we offer an outlook on how to build Artificial Clinic Intelligence (ACI), a generative AI (GAI)-powered modeling platform for modeling clinical trial enrichment. ACI generates synthetic patient data and models clinical trial enrichment to inform clinicians on key clinical parameters that are enriched in prospective patients prior to accrual. Full article

Exposure to stress during early developmental stages correlates with persistent alterations in multiple physiological systems, including the renal system. In rodents, maternal separation (MS) is a widely used experimental model to simulate postnatal adversity. Although this condition affects various renal parameters, a gap persists in knowledge regarding its impact on the functional unit of the kidney and the organization of the parenchyma. Thus, the objective of this systematic review was to analyze the effects of MS on the morphofunctional characteristics of the kidney in rodent models. We developed a protocol a priori following the SYRCLE and PRISMA guidelines and registered it in PROSPERO (CRD420251004703). We searched Web of Science, Scopus, Medline, Embase, BIREME-BVS, and SciELO without language or date restrictions, targeting experimental studies in rodents subjected to MS that evaluated structural, functional, or molecular alterations. Three independent reviewers performed data selection and extraction, and they assessed the risk of bias using the SYRCLE’s RoB tool. We included seven studies that met the eligibility criteria. At the structural level, studies reported cellular infiltrates positive for MPO, CD44, and TLR4, along with increased cortical and medullary microvascular density. Regarding renal function, the included studies described changes in ACE1 and ACE2 activity, oxidative stress, and enzymatic imbalance accompanied by a compensatory antioxidant response. At the molecular level, the studies reported variations in the expression of adrenergic receptors and the renin-angiotensin system. These findings suggest that MS may compromise the organization and functional integrity of the developing kidney, underscoring the need for studies that integrate structural and functional analyses in greater depth. Full article

This paper presents a systematic approach for rapid assessment of the performance and robustness of linear control systems through geometric analysis in the complex plane. By combining indirect performance indices within a defined zone of desired performance in the complex s-plane, a connection is established with direct performance indices, forming a foundation for the synthesis of control algorithms that ensure root placement within this zone. Analytical relationships between the complex variables s and z are derived, thereby defining an equivalent zone of desired performance for discrete-time systems in the complex z-plane. Methods for verifying digital algorithms with respect to the desired performance zone in the z-plane are presented, along with a visual assessment of robustness through radii describing robust stability and robust performance, representing performance margins under parameter variations. Through parametric modeling of controlled processes and their projections in the complex s- and z-domains, the influence of the discretization method and sampling period, as forms of a priori uncertainty, is analyzed. This paper offers original derivations for MISO systems, facilitating the analysis, explanation, and understanding of the dynamic behavior of real-world controlled processes in both the continuous and discrete-time domains, and is aimed at integration into expert systems supporting control strategy selection. The practical applicability of the proposed methodology is related to discrete control systems in energy, electric drives, and industrial automation, where parametric uncertainty and choice of method and period of discretization significantly affect both robustness and control performance. Full article

We do not know a priori chemical composition of a star. However, with more high resolution spectra becoming more abundant thanks to the development of space-born observations, atomic data including Stark broadening parameters for various spectral lines for elements in various ionisation stages are becoming more feasible. Particularly are important spectral lines of C-N-O peak in the distribution of abundances of chemical elements. For the calculation of Stark broadening parameters, spectral line full widths at half intensity maximum (FWHM) and shifts, we used semiclassical perturbation method. As the result, Stark widths and shifts for 36 spectral lines of neutral oxygen, broadened by the collisions with electrons, protons and helium ions, have been obtained and compared with other theoretical calculations. These data are of interest for a number of problems in astrophysics, plasma physics, as well as for inertial fusion and various plasmas in technology. Full article

Pelvis and trunk counter-rotation are key factors known to effect throwing arm kinematics in baseball pitching, where energy or momentum is transferred from the lower extremities through to the trunk during the pitching cycle. The purpose of this study was to retrospectively analyze previously recorded motion capture data of 19 skilled competitive pitchers to test the a priori hypothesis whether different stride lengths affect transverse pelvis and trunk biomechanics. A blinded randomized crossover design was used where pitchers threw two simulated games at ±25% from desired stride length (DSL), respective of overstride (OS) and under-stride (US). Variables of interest included pelvic–trunk separation (PTS) angle or degree of uncoupling and proximal plyometric effect (PPE) or ratio between trunk–pelvis angular velocities, as surrogate measures of rotational and elastic energy transfer. Paired t-tests were used to compare across stride conditions. A one-way ANOVA with a Bonferroni post hoc analysis demonstrated stride lengths differed statistically, (DSL vs. OS p = 0.006), (DSL vs. US, p < 0.001), and (US vs. OS, p < 0.001). Despite the statistically different stride lengths, fastball velocities tracked with radar were consistent. No significant differences within and across innings pitched between OS and OS conditions were found. The ±25% stride length changes influenced temporal parameters within the pitching cycle. Shorter stride elicited by early SFC reduced time during the Generation phase and extended the Brace-Transfer duration (p < 0.001). Statistically different transverse pelvis and trunk kinematics at hallmark events and phases consequently influenced pelvic–trunk separation and proximal plyometrics. During the Generation (PKH-SFC) and Brace-Transfer (SFC-MER) phases, the pelvis and trunk were significantly more externally rotated (p < 0.001) with shorter strides, concomitant with less separation at the instant of SFC and the Generation phase with greater peak proximal plyometrics effect ratios peak during throwing arm acceleration, indicative of greater contribution of trunk angular velocity (p < 0.05). Greater transverse trunk angular velocities relative to the pelvis late in double support necessitates the throwing arm to “catch up” from a position of greater arm lag, which compromises the dynamic and passive stabilizers. In conclusion, stride length alters pitching biomechanics and timing of peak pelvic–trunk separation and trunk angular velocity relative to the pelvis. Increased shoulder and elbow tensile stress is to be expected, consequently increasing risk for injury. Full article

High-entropy oxides, HEOs, represent a relatively new class of ceramic materials characterized by the incorporation of multiple cations, typically four or more, into a single-phase crystal structure. This extensive compositional flexibility allows for the introduction of specific chemical elements into a crystal lattice that would normally be unable to accommodate them, making it difficult to predict a priori their properties and crystal structures. Consequently, studying the phase stability of these single-phase materials presents significant challenges. This work examines the key parameters commonly employed to predict the stabilization of HEOs and introduces a unified framework for analyzing their stability. The proposed approach incorporates a normalized configurational entropy per mole of atoms and the relative volume occupied by cations into the mean atomic size deviation. By combining these parameters, the approach enables, as a first approximation, the identification of compositional ranges that favor the formation of single-phase and multi-phase HEO compounds with rock salt, spinel, fluorite, pyrochlore, and perovskite structures. Full article