Search Results (321)

In 2025, the oil and fat industry of the Republic of Kazakhstan showed steady growth, strengthening the country’s position as a major producer and exporter of vegetable oils. However, the production process generates large amounts of waste (up to 100 tons per day), and its recycling is an important part of the oil and fat industry’s economy. High-temperature processing of plant waste has proven to be a promising method for creating new materials for various industries. The aim of this study was to determine the influence of various technical parameters of carbonization (processing temperature and process environment) on the physical and chemical characteristics of materials obtained by carbonizing sunflower seed husks from the East Kazakhstan region at temperatures of 300, 400, 500, 600, 700 and 800 °C in an inert argon environment, as well as by processing the husks in an oxidizing environment at a temperature of 650 °C, for further use in elastomer compositions as new ingredients. Increasing the carbonization temperature in an inert environment led to an increase in the amorphous carbon content, surface porosity, and pH of the studied materials. Another parameter that showed a tendency to increase with increasing temperature in an inert environment was the BET specific surface area. In the case of using an oxidizing environment, the highest pH value was observed, and the formation of crystalline mineral phases was also observed. The differences in the phase states of the studied materials may play an important role in shaping the spatial stack of the polymer matrix when used in rubber compound formulations. Full article

One of the negative effects of the rapid increase in the number and capacity of photovoltaic power sources distributed in consumption areas by connecting to existing low-, medium-, and high-voltage distribution networks is the increased risk of amplifying the non-sinusoidal steady-state condition caused by high-power inverters. This amplification occurs if the peak values of harmonic impedance in the sections of the respective grid zone, which depend on the values of the equivalent shunt capacitances present in the grid, correspond to frequency values that coincide with or are close to the frequencies of the harmonic currents injected by the distributed sources. The risk is particularly high in cases of malfunction or failure of the filters within the installations associated with these sources. High shunt capacitance values are not only caused by the capacitive compensators used to increase the power factor of consumers or to improve the voltage level in the grid, but also by long power lines, particularly long underground lines. Risks increase as the proportion of underground power lines in the network area with sources of harmonic currents rises. This article refers to a real high-voltage grid area where the distribution operator plans to install high-power photovoltaic sources, the connection of which requires grid expansion through the construction of new substations and high-voltage power lines (110 kV). Based on the designer’s intention for the new high-voltage power lines to be underground and of relatively long lengths—which implies the presence of high natural capacitances—the authors conduct two case studies to predictively evaluate the harmonic impedance in the network sections resulting from the expansion, with the aim of identifying the frequencies at which parallel resonances may occur. The authors use two software tools for numerical analysis, Mathcad 14.0 and MATLAB–Simulink 2025A, and compare the results obtained for two design variants of the new high-voltage lines: overhead and underground, respectively. Using the mathematical model of long lines in steady state with uniformly distributed parameters, the analysis highlights that the highest values of harmonic impedance correspond to the overhead line design variant. The use of this design variant increases the risk of parallel resonances, not only by increasing the harmonic impedance values but also by widening the frequency ranges for which the impedance has high values. In both the overhead line and underground line variants, increasing the load leads to a reduction in the maximum values. Compared to the design variant using overhead lines, the variant with underground lines increases the risk of parallel resonances by increasing the number of frequency intervals for which the harmonic impedance has high values, but this increase is limited by narrowing these frequency intervals and reducing the harmonic impedance values. An important contribution of the article lies in arguing the need to extend the conventional node-based evaluation of harmonic impedance by treating harmonic impedance as a continuous spatial frequency characteristic of the transmission network. The impedances seen at nodes become the impedances seen in particular sections/sections of the network, identified by the value of the variable that specifies their position in space, more precisely the distance from the end of one of the feeders on which they are located. Full article

Developing digital twins for legacy hydropower units is difficult when turbine documentation, calibrated performance data, and integrated measurements are incomplete. This study presents a Computational Fluid Dynamics (CFD)-assisted reconstruction workflow for a Kaplan turbine at the Wały Śląskie Hydropower Plant and evaluates its use as a physics-informed foundation for a digital twin. The flow passage was reconstructed from archival documentation, direct measurements, and optical 3D scanning of the runner. A steady-state Reynolds-averaged Navier–Stokes model was then prepared in OpenFOAM v2506 for selected head levels, guide-vane openings, and runner-blade angles. The simulations determined hydraulic performance, flow-field structures, and combinatory characteristics of the double-regulated turbine. The computed hydraulic efficiency reached approximately 85% in the nominal-head range, and the highest-efficiency region formed a broad plateau rather than a sharp optimum. CFD-derived and measurement-derived combinatory trends were consistent, although absolute values remain limited by relative field measurements and uncalibrated Winter–Kennedy flow estimation, a differential-pressure-based method. The CFD results were reduced to compact response surfaces and integrated with reconstructed geometry into an advisory digital twin for operating-point assessment, visualization, documentation, and training. This study establishes a robust workflow for this specific Kaplan turbine case where reverse engineering, integrated with CFD analysis, generates high-fidelity surrogate models for hydropower digital twins, effectively addressing the challenge of incomplete legacy documentation. Full article

Artificial intelligence (AI) technologies, which are being actively developed in modern medicine today, increase the speed and quality of patient care. This article mainly seeks to demonstrate the use of various options of computer analysis of clinical images to solve practical problems of increasing the efficiency of routine diagnostics using retrospective analysis, as well as show the potential for its widespread implementation (due to the scalability of the architecture) in practical healthcare, exemplified by ultrasound (US) and magnetic resonance imaging (MRI) data analysis. This is an interuniversity study, its research protocol was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the local Ethics Committee of Orel State University named after I. S. Turgenev (Protocol No. 25 dated 16 November 2022). The software was developed using Python 3.7 and open neural network models. Statistical processing included an efficiency assessment for which IBM SPSS Statistics 20.0 was used. Detection errors in the analysis of 550 US cases did not exceed 6–8% and were associated with technical difficulties due to image quality. When studying 1030 MRI studies, only 0.19% of cases failed to obtain reliable image analysis results. The differences in the average values for the dimensional characteristics of the studied vessels were 0.11–0.12 mm. The effectiveness of AI in clinical tasks is presented. The improvement in segmentation accuracy was achieved through the use of step-by-step image optimization during the AI training stage. The evolution of technologies in medicine, aimed at digitalization and personalization, is intended to improve the quality and speed of studying images in practical work. Full article

Background: Unhealthy alcohol use may lead to arrhythmia and cardiomyopathy, but its impact on sudden death is not well understood. Objective: To investigate the association of unhealthy alcohol use with sudden death. Methods: We conducted a case-control study in Wake County, a large (~1 million inhabitants), diverse county in North Carolina. We screened and adjudicated victims of sudden, unexpected, out-of-hospital deaths in adults aged 18–64 years reported by emergency medical services between 2013 and 2015. We randomly selected sex- and age-matched control patients from a university health system from the same county and time period. Characteristics of sudden death victims and controls were ascertained via standardized chart reviews. Unhealthy alcohol use was identified via chart review and was defined as any evidence of excessive alcohol use, such as it being stated in the social history or medical history, alcohol abuse being listed as a possible contributor to death, or alcohol-related diagnoses. We used logistic regression to estimate odds ratios (ORs) for the association of unhealthy alcohol use and sudden death, adjusting for age, sex, race, and other psychiatric diagnoses, including depression, anxiety, schizophrenia, bipolar disorder, and substance use disorders other than tobacco and alcohol. We also calculated the E-value to estimate the impact of any unmeasured confounders. Results: We identified 399 sudden death victims, of whom 374 (94%) had alcohol use data available. Among these 374 included victims, 256 (68%) were male, and 239 (62%) were White, with a median age at death of 55 years (IQR 48, 60). The demographic characteristics of the 1114 matched controls were similar to those of sudden death victims. Unhealthy alcohol use was present in 115 (31%) sudden death victims and 27 (2%) controls. In analyses adjusted for demographics only, unhealthy alcohol use was associated with a higher incidence of sudden death, with an OR of 17.5 (95% CI 11.4, 27.8). When further adjusted for other psychiatric diagnoses, the OR was 11.2 (95% CI 7.1, 18.0). The calculated E-value was 21.8, meaning an unmeasured confounder would need to be associated with both unhealthy alcohol use and sudden death by 21.8-fold to explain away the observed OR. Conclusions: Unhealthy alcohol use was strongly associated with higher sudden death risk in working-age adults. Our calculated E-value indicates it is unlikely that any unmeasured confounders alone would account for the observed association. Our findings suggest that interventions to reduce unhealthy alcohol use may be an effective strategy to prevent sudden death in working-age adults. Full article

In precision gear manufacturing, longitudinal crowning on tooth flanks is commonly produced by applying diagonal feed in worm-type generating processes using tools such as variable-tooth-thickness hobs and dressable grinding worms. However, precise twist control remains difficult because the geometric parameters of the generating tool are strongly coupled with the machine feed settings in the underlying generating kinematics. In addition, direct numerical optimization becomes unreliable near the standard tool state, where the sensitivity of the diagonal-feed coefficient degenerates and conventional linearized solvers may lose effectiveness. To address these issues, this study proposes a multi-variable optimization framework for twist-constrained worm-type gear generation. An iterative singular value decomposition (SVD) scheme is developed to construct and update the sensitivity matrix, while a warm-start continuation strategy is introduced to overcome the local singularity and improve numerical robustness. Two closed-form expressions for the diagonal-feed coefficient are also proposed as practically useful initial estimates, corresponding respectively to the minimum SVD topographic residual and the minimum tooth-flank twist. Numerical validation over a 60-case parameter sweep shows maximum relative errors below 1.6% within the tested range. The proposed framework coordinates the tool-geometry design and diagonal-feed selection to generate tooth flanks with prescribed crowning characteristics while satisfying a specified twist requirement and limiting the required diagonal shift. Numerical examples show that the iterative framework reduces the root-mean-square (RMS) topographic error from 1.14 μm to 0.027 μm relative to the analytical setting of Hsu and Fong. These results indicate that the proposed method provides a reliable computational basis for twist control and process-parameter design in advanced CNC gear generation. From a manufacturing standpoint, because the three design criteria are accessed by adjusting only the diagonal-feed ratio on the machine, a single generating-tool design can serve a range of crowning and twist requirements without retooling, reducing setup and tooling efforts in production. Full article

Continuous ground-based microwave (MW) measurements with the RPG-HATPRO radiometer at the observational site of St. Petersburg State University located near the coastline of the Gulf of Finland have provided a large amount of data on the cloud liquid water path (LWP) of non-raining clouds. The 12-year (2013–2024) time series of the LWP values has been analysed and the diurnal evolution of the LWP has been assessed for each month of the year. The calculations have been made for the LWP in the range 0–0.4 kg m⁻² using different sampling subsets that include the so-called true and virtual LWP values. True LWP values correspond to measurements with clouds in the field of view of the radiometer, whereas virtual LWP values correspond to measurements with clouds or with clear sky in the field of view of the instrument and, therefore, virtual values can be zero (in clear sky cases). Based on the correlation analysis, time periods characterised by similar meteorological conditions and suitable for assessing the daily dynamics of LWP were identified. The LWP diurnal cycles in December, January, and February demonstrated a similar pattern with a maximum around local astronomical noon and with a minimum around midnight. For the remaining months except March and June, the maximum LWP is observed in the early morning and the minimum is observed in the afternoon. This cycle is characteristic of marine stratocumulus clouds. The diurnal cycles of the LWP in March and June, peaking in the afternoon and morning, respectively, are typical of convective continental clouds. Thus, the LWP diurnal cycle in the coastal zone of the Gulf of Finland may have characteristics of both marine and continental clouds. Parameters of the two-mode sinusoidal approximation of the diurnal cycle of the LWP in different seasons are presented. Full article

To investigate the contact mechanical performance of helicopter main gearbox rotor shaft bearings under a complex load spectrum, this study focuses on the contact stress and load-carrying characteristics of bearings operating under high-speed and heavy-load conditions. Based on the rotor shaft system of a helicopter main gearbox and Hertzian contact theory, quasi-static analyses were performed on four tapered roller bearings and one cylindrical roller bearing mounted on the shaft system conducted in Romax. The results indicate that the maximum contact stresses of the bearings do not exhibit sustained high-stress states under most operating conditions. The peak-stress conditions account for only extremely small time proportions in limited cases, namely 0.003429% and 0.025%. The contact stresses on both the inner and outer raceways exhibit a non-uniform distribution along the roller length, with local peak values appearing near the highly loaded roller-raceway contact regions. This suggests that during the design process of the helicopter main gearbox rotor shaft, special attention should be given to this region. The present results provide a theoretical basis for subsequent life-index verification and offer an effective analytical method for the design and validation of such critical components. Full article

Granular systems confined in a shallow box and subjected to vertical vibration provide an attractive geometry for studying fluidized granular media. In this configuration, grains acquire kinetic energy in the vertical direction through collisions with the confining walls, and this energy is subsequently transferred to the horizontal degrees of freedom via interparticle collisions. In recent years, the so-called $\mathsf{\Delta }$-model has been introduced as a simplified yet effective description of the dynamics of granular systems in such geometries. This review presents the results obtained from kinetic theory for the granular $\mathsf{\Delta }$-model. To model the energy transfer mechanism, a fixed velocity increment $\mathsf{\Delta }$ is added to the normal component of the relative velocity during collisions. In this way, the vertical motion is effectively integrated out while retaining the collisional energy injection characteristic of the confined setup. This mechanism compensates for the energy loss due to inelastic collisions and leads to stable homogeneous steady states that can be analyzed within the framework of kinetic theory. The Enskog kinetic equation is formulated for this model and first analyzed in homogeneous steady states, yielding the stationary temperature and the equation of state. The dynamics of inhomogeneous states is then investigated using the Chapman–Enskog method, from which the Navier–Stokes transport coefficients are derived. The theory is further extended to granular mixtures, in which particles may differ in mass, size, restitution coefficient, or in the value of $\mathsf{\Delta }$. In this case, the phenomenology becomes richer; for example, energy equipartition is violated even in homogeneous steady states. The mixture dynamics is studied through the corresponding Navier–Stokes equations, and the associated transport coefficients are obtained in the low-density regime. The analysis of the hydrodynamic equations shows that, in agreement with simulations, the homogeneous state is linearly stable. Moreover, the intrinsically nonequilibrium nature of the model leads to the violation of Onsager reciprocity relations in granular mixtures. The theoretical predictions exhibit in general good agreement with both molecular dynamics simulations and direct simulation Monte Carlo results. Full article

Accurate identification of disease-associated microRNAs (miRNAs) is crucial for elucidating pathogenic mechanisms and advancing therapeutic discovery. Although computational methods, particularly those based on biological networks, have become essential tools for predicting miRNA-disease associations, existing approaches often struggle to comprehensively learn from heterogeneous data and optimize feature representations. To overcome these limitations, we propose the Multi-view Hybrid Attention Graph Convolutional Network (MV-HAGCN). This framework constructs a comprehensive heterogeneous network by integrating multi-source biological information, simultaneously capturing miRNA similarity and disease similarity. We design a hierarchical attention mechanism to enable refined feature learning: first, the Efficient Channel Attention (ECA) module prioritizes information-rich input features, ensuring the model focuses on high-value biological characteristics. Subsequently, the Multi-Head Self-Attention Graph Convolutional Network operates on these refined features. Through iterative message passing and multi-head self-attention, it captures not only direct first-order relationships between nodes but also explicitly models and infers complex, indirect higher-order relationships within the network. This hierarchical design progressively refines feature representations, from channel-level recalibration to global structural dependency modeling, enabling the model to capture both local and high-order relational patterns. Furthermore, a dynamic weight learning strategy adaptively integrates multi-perspective similarity matrices, achieving superior feature complementarity and synergy. Finally, the high-order node representations learned through multi-layer graph convolutions are fed into a multi-layer perceptron for integration and nonlinear transformation, enabling precise prediction of potential miRNA-disease associations. Comprehensive evaluation through five-fold cross-validation on HMDD v2.0 and v3.2 benchmark datasets demonstrates that MV-HAGCN consistently outperforms existing state-of-the-art methods in predictive performance. Case studies targeting key diseases such as breast cancer, lung tumors, and pancreatic disorders revealed that the top 50 miRNAs associated with each of these three conditions were all validated in databases, confirming the practical value of this model in screening candidate miRNAs with high biological relevance. Full article

This review provides a comprehensive overview of the most adopted 3D surveying techniques in Cultural Heritage, offering practical guidance for the selection of appropriate methods when three-dimensional documentation of artworks is required. The analysis focuses on the most effective technologies for the 3D documentation of sites and objects of artistic value, with selection criteria primarily centred on non-invasiveness, given the uniqueness and cultural significance of the case studies, and the instrument flexibility, a crucial requirement for non-transportable items. A broad spectrum of 3D techniques is currently available for the multiscale diagnostic investigation of artworks, providing information at both macroscopic and microscopic levels. This review reports on the state of the art of such systems and evaluates the main characteristics of each technology in relation to its applicability in the heritage field. Particular attention is given to highlighting advantages and limitations, and to assessing performance in terms of resolution, gauge volume/area, acquisition time, and cost. In addition, the review discusses exemplary cases in which 3D methods are integrated with other analytical techniques to enable a more comprehensive understanding of the object under investigation. Finally, recent studies are examined to identify the most suitable approaches and the specific requirements for the digitization of real-world heritage assets. Full article

The Hough transform (HT) is widely used in computer vision, tomography, and neural networks. Numerous algorithms for HT computation have been proposed, making their systematic comparison essential. However, existing comparative methodologies are either non-universal and limited to certain HT formulations or task-oriented, relying on application-specific criteria that do not fully capture algorithmic properties. This paper introduces a novel unified methodology for the systematic comparison of HT algorithms. It evaluates key characteristics, including computational complexity, accuracy, and auxiliary space complexity, while explicitly accounting for the property of self-adjointness. The methodology integrates both implementation-level and theoretical considerations related to the interpretation of HT as a discrete approximation of the Radon transform. A set of mathematically justified evaluation functions, not previously described in the literature, is proposed to support our methodology. Importantly, the methodology is universal, applicable across diverse HT paradigms, encompasses pattern-based and Fourier-based fast HT (FHT) algorithms, and offers a comprehensive alternative to existing task-specific methodologies. Its application to several state-of-the-art FHT algorithms ($FHT2DT$, $FHT2SP$, $ASD2$, $KHM$, and Fast Slant Stack) yields new experimentally confirmed theoretical insights, identifies $ASD2$ as the most balanced algorithm, and provides practical guidelines for algorithm selection. In particular, the methodology reveals that for image sizes up to 3000, the maximum normalized computational complexity increases as follows: $FHT2DT$ (1.1), $ASD2$ (15.3), and $KHM$ (30.6), while the remaining algorithms exhibit at least 1.1 times higher values. The maximum orthotropic approximation error equals 0.5 for $ASD2$, $KHM$, and Fast Slant Stack; lies between 0.5 and 1.5 for $FHT2SP$; and reaches 2.1 for $FHT2DT$. In terms of worst-case normalized auxiliary space complexity, the lowest values are achieved by $FHT2DT$ (2.0), Fast Slant Stack (4.0, lower bound), and $ASD2$ (6.8), with all other algorithms requiring at least 8.2 times more memory. Full article

Magnetorheological (MR) semi-active suspensions offer clear advantages in improving ride comfort and handling stability, yet their engineering applications are often hindered by strong nonlinear hysteresis of the damper, the randomness of road excitations, and the reliance on manual tuning of controller parameters. To address these issues, this paper proposes an integrated framework of “experimental modeling–semi-active implementation–adaptive control.” First, characteristic tests of the MR damper are conducted, based on which a current-dependent Bouc–Wen forward model is established. Tianji’s Horse Racing Optimization (THRO) is then employed for parameter identification to reproduce the hysteresis behavior accurately. Second, a back propagation (BP) neural network-based inverse current model is developed to achieve rapid mapping from “desired damping force” to “driving current,” enabling semi-active actuation. Furthermore, a radial basis function (RBF) neural network is embedded into the active disturbance rejection control (ADRC) structure to estimate the system Jacobian online and to tune key extended state observer (ESO) gains in real time, forming the proposed RBF-ADRC strategy and thereby enhancing disturbance observation and compensation capability. Simulation results under pulse-road and Class-C random-road excitations show that, compared with the passive suspension, the proposed method reduces the root mean square error values of sprung-mass acceleration, suspension dynamic deflection, and tire dynamic load by 25.14%, 18.71%, and 11.61%, respectively, while also outperforming skyhook control and fixed-gain ADRC. Frequency-domain results further show stronger attenuation in the low-frequency band relevant to body vibration. Under pulse excitation, RBF-ADRC yields smaller peak and trough body accelerations and faster post-impact recovery. Under ±30% sprung-mass variations, it achieves the best worst-case and fluctuation-range robustness among the compared strategies and remains close to offline retuning. These results demonstrate that the proposed method improves both control performance and robustness while reducing the need for repeated manual calibration. Full article

To compete with fossil fuels, biofuels produced from renewable waste biomass must be cost-effective, adaptable to existing heat and power infrastructure, and possess desirable fuel properties and performance metrics matching those of fossil fuels, while having a much lower carbon footprint. However, handling and processing biowastes in thermochemical biorefineries is challenging owing to their high moisture content, low bulk density, poor grindability, low calorific value, and heterogeneous physicochemical properties. Torrefaction has emerged as an effective thermochemical technology for upgrading biowastes into torrefied biomass, which exhibits improved, homogeneous physicochemical properties, including higher calorific value, higher bulk density, better grindability, and hydrophobicity. This review synthesizes the current state of research on torrefaction, with particular emphasis on process parameters, reactor designs, commercial-scale implementations, and an analysis of its strengths, weaknesses, opportunities, and threats. The comparative advantages and limitations of different torrefaction reactors are highlighted, emphasizing how each reactor’s characteristics determine its suitability for specific circumstances and operating conditions. This article also considers the technical and economic challenges associated with scaling up torrefaction. The discussion on specific case studies on techno-economic analysis of torrefaction outlines the key barriers and provides incentives for researchers to consider when upscaling the technology. The strengths, weaknesses, opportunities, and threat analysis offers strategic insights for policymakers and industry stakeholders into possible actions to support torrefaction and its upscaling. Full article

Accurate identification of photovoltaic (PV) cell and module parameters remains a fundamental yet challenging task, particularly as model complexity increases from five to nine unknown parameters. In this study, the parameter extraction problem is rigorously formulated as a nonlinear optimization task and addressed using a novel hybrid metaheuristic algorithm, termed the Shuffled Frog Leaping–Shuffled Complex Evolution (SFL-SCE) method. The proposed approach synergistically integrates the population-based social learning mechanism of the Shuffled Frog Leaping Algorithm (SFL) with the robust global search and refinement capabilities of Shuffled Complex Evolution (SCE), thereby achieving an effective balance between exploration and exploitation. The SFL-SCE algorithm minimizes the root-mean-square error (RMSE) between measured and simulated current–voltage characteristics and is systematically applied to three widely used PV technologies: the RTC-France silicon solar cell, the polycrystalline Photowatt-PWP201 module, and the monocrystalline STM6-40/36 module. For each device, parameter identification is performed under one-diode, two-diode, and three-diode modelling frameworks, encompassing increasing levels of physical fidelity and computational complexity. Experimental data are employed throughout to ensure practical relevance and robustness. The performance of the proposed algorithm is comprehensively evaluated against its constituent algorithms (SFLA and SCE) as well as several state-of-the-art hybrid optimization techniques reported in the literature. Comparative results demonstrate that SFL-SCE consistently achieves superior accuracy, enhanced reliability, and faster convergence, as evidenced by lower minimum, mean, and maximum RMSE values, reduced standard deviation, and improved convergence behavior across all test cases. These findings confirm the effectiveness of the proposed hybridization strategy and establish SFL-SCE as a powerful and reliable tool for high-precision PV model parameter identification. Full article