The present analysis explores the mass and heat transport behavior of bioconvective Boger ternary nanofluid flow via a shrinking/elongating deforming cone with a magnetic field and porous medium. Moreover, the significance of nonlinear thermal radiation, activation energy, heat sink/source, and convective boundary conditions on the fluid flow is considered to assess the mass and heat transfer attributes. The present analysis is pertinent to systems that need improved thermal management, especially in applications like cooling technologies and energy devices that include complicated fluids and spinning equipment. In contrast to traditional research, the current study concurrently takes into account nonlinear thermal radiation, convective boundary conditions, and heat sink/source, all of which have a substantial impact on thermal transport but are frequently handled independently. Similarity transformations are employed to convert the governing partial differential equations (PDEs) into ordinary differential equations (ODEs). The finite difference method (FDM) is utilized to solve the reduced ODEs numerically. The study optimizes the heat transfer rate impacted by significant factors using the response surface methodology (RSM). The central composite design is employed in a variety of industrial applications to statistically analyze and optimize the heat transfer rate. A graphic depicts how different circumstances affect the different profiles. An increase in the solvent percentage is indicative of improved velocity profiles. As radiation and heat sink/source parameters amplify, the thermal profile also rise. An upsurge in the Biot number enhances the temperature profile. As the solvent fraction parameter advances, the skin friction diminishes by about 1.96 % and 3.31 %, indicating a reduction in wall shear attributable to increased elasticity.
Mycelium-stabilized straw insulation (MSSI) is a biodegradable, bio-based material considered a sustainable alternative to conventional thermal insulation products; however, reliable experimental data describing its thermal performance under temperature conditions relevant to building operation remain limited. The aim of this study was to determine the thermal conductivity of MSSI produced from wheat straw inoculated with Pleurotus ostreatus and to evaluate the influence of temperature on its thermal behaviour. Thermal conductivity measurements were performed using a FOX314 heat flow meter apparatus in accordance with ISO 8301. Six samples with bulk densities ranging from approximately 81 to 93 kg/m3 were tested at a mean temperature of 10 °C, while additional measurements on three representative samples were conducted over a temperature range from −10 °C to +20 °C. All specimens were subjected to long-term conditioning prior to testing to ensure mass stability. The measured thermal conductivity values ranged from 0.0378 W/(m·K) at −10 °C to 0.0425 W/(m·K) at +20 °C, with a mean value of 0.0416 W/(m·K) at 10 °C. The results demonstrate a clear increase in thermal conductivity with rising temperature, following an approximately linear trend. This study provides temperature-dependent thermal conductivity data for stabilized mycelium-straw insulation measured under steady-state conditions, addressing a gap in existing research. The obtained results reflect material behaviour under realistic building operating temperatures and support the potential application of MSSI as an environmentally sustainable thermal insulation material.
This paper presents a device sensitive to dielectric material properties, based on a square shaped split-ring resonator (SRR) metasurface operating in the terahertz (THz) frequency band. In the proposed structure, most of the single resonant element is anchored in a silicon substrate, while the remaining part is movable (a magnetic-material-based free-standing cantilever) and can be deformed by an external magnetic field. Owing to its variable geometry, these micro-electro-mechanical systems (MEMS) constitute a reconfigurable resonant element. The structure is designed and analyzed using finite element method (FEM) numerical modeling. The analytical model is presented using an equivalent electrical circuit model. The resonance behavior (at 0.38 THz and 1.13 THz under zero-deflection conditions) is investigated for dispersive, non-dispersive gaseous and liquid material sweep, and a parametric analysis is performed for various deflection levels. This model-based analysis shows that frequency shift of the tunable metasurface sensor, under external material influence and different deflection levels, has potential for sensing (based on detecting changes in the effective permittivity (ε eff)) and non-destructive testing applications. This analysis makes the path for future fabrication and experimental validation.
This study presents a comprehensive bibliometric review of the concept of harvesting energy produced by flow-induced vibration, aiming to map the scientific progress, knowledge structure, and evolving research themes in this field. Although research in this domain has significantly increased in recent years, a systematic quantitative overview of influential contributions, technology trends, and emerging directions remains limited. To address this gap, five key bibliometric analyses were conducted: (1) annual publication trends, including journal articles and conference papers; (2) identification of the most influential and highly cited studies; (3) mapping of thematic development and conceptual evolution; (4) analysis of systems most commonly associated with Flow-Induced Vibration Energy Harvesting (FIV-EH); and (5) classification of diverse harvesting mechanisms applied within existing studies. Data were retrieved from the Scopus and Google Scholar database on June 2, 2025. A total of 607 publications were included after applying the exclusion criteria. VOSviewer and Biblioshiny for R were used to analyze co-authorship networks, citation performance, and keyword co-occurrence. The findings reveal the lack of a standardized definition of FIV-EH, indicating conceptual fragmentation in the literature. Furthermore, the results highlight three major directions essential for advancing implementation in real applications: (1) increasing the adoption of renewable energy within the energy mix, (2) improving energy efficiency, and (3) reducing GHG emissions and air pollution. This study proposes a conceptual framework and provides insights that can guide future research, strengthen technological development, and support sustainable energy initiatives globally.
This paper presents a toggle-control algorithm for multiple scan-chains Built-In Self-Test (BIST) in order to reduce peak and average power consumption during scanning-in test patterns. The proposed technique includes two phases. The first phase, which aims to reduce peak and average power, is implemented by adding a counter circuit to count the number of toggles of any test pattern and prohibits more toggles at a pre-specified limit. When this limit is reached, the remaining bits of the test pattern will be fixed at 0 or 1 to prevent more toggles. This phase although decreases the shift power (peak and average), it degrades the fault coverage (FC) of the Circuit Under Test (CUT). The second phase aims to improve the FC by reordering the cells within each scan-chain in such a way to maximize FC, and thus reducing test application time. Experimental results on ISCAS’89 and ITC’99 benchmark circuit’s show that using the proposed design can save on average about 34.54 % of average power consumption, 43.90 % of peak power consumption, while reducing in the same time the test length of about 4.05 %, which means huge power and energy savings without degrading test application time, and with negligible overhead in hardware area.
This study aimed to discern the optimal machine learning algorithm for precise state-of-charge (SoC) prediction in lithium-ion batteries, specifically focusing on LiFePO4. The investigation involved training and evaluating a comprehensive dataset using diverse machine learning algorithms, including Linear Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), and Stochastic Gradient Descent (SGD). The results indicate that the Decision K-nearest neighbor, and Artificial Neural Network algorithms yield superior performance in predicting the SoC of Lithium-Ion batteries. Notably, the Decision Tree algorithm demonstrated the lowest error value of 7 × 10−9, followed by the Nearest Neighbor algorithm with an error value of 1.9 × 10−4, and the ANN algorithm with an error value of 0.01. These findings highlight the efficacy of machine learning algorithms in accurately predicting the SoC of Lithium-Ion batteries, which has significant implications for optimizing battery performance and efficiency. Furthermore, this study contributes to the advancement of novel and advanced machine learning algorithms for precise SoC prediction in LiFePO4 batteries.
Mobile Ad hoc Networks (MANETs) play a crucial role in wireless communications, particularly in areas without infrastructure, such as military operations, disaster zones, and emergency response situations. Despite the diverse applications of MANETs, their decentralized nature and dynamic topology present significant security challenges. AODV and other IETF-standard routing protocols assume the network is secure, making them vulnerable to attacks. This research presents a novel comparison approach for evaluating the impact of different network-layer attacks, namely black hole, grey hole, jellyfish, byzantine, and selfish node attacks, on the AODV protocol using the NS2 simulator. The key contribution is that it systematically measures the severity of attacks across various QoS parameters, including average throughput, packet delivery ratio, packet drop ratio, average end-to-end delay, routing load, and energy consumption. Unlike earlier studies that focus on isolated attacks, our method provides a more comprehensive understanding of how different attack patterns affect network performance under the same simulation conditions. The findings indicate that selfish node attacks produce less disruption, but grey hole and jellyfish attacks cause greater damage than black hole and Byzantine attacks. This provides us with fresh insights into prioritizing security measures in MANET setups.
In this study, the problem of viscous dissipation and radiation phenomena for Casson performance in a ternary hybrid nanofluid model convected by a solid sphere is investigated using numerical simulation and machine learning with SVM algorithm approaches. Constant wall temperature boundary conditions in the presence of the natural convection flow phenomena are also considered. Numerical simulation is performed using a Chebyshev spectral collocation method. A data set for different hybrid nanofluids (G-MgO-H2O + EG 50 %) and ternary hybrid nanofluids (G-ZrO2-MgO/H2O + EG 50 %) results has been constructed for the studied parameter effects. Results are presented graphically using violin box plot. It is observed that increasing the nanoparticle volume fraction, Eckert number, and radiation parameter improves the local skin friction and enhances the rate of heat transfer. Additionally, a significant effect of radiation parameter on velocity values is observed. Moreover, this study highlights the support vector machine model as an effective tool for accurately predicting heat transfer in nanofluid systems.
In the current research, hydration and setting behaviors of cement paste are investigated in the presence of sugar as a concomitant retarder and accelerator. OPC and Sugar, as a retarder were mixed at 0, 1, 1.5 and 2 % by weight of cement respectively with water to cement ratios of 0.35, 0.32, 0.30, 0.28 and likewise at 26 %. Setting time was measured with a Vicat and hydration heat was monitored by means of thermocouples. It was found that the optimal 1 % sugar content can delay the final setting time by approximately 350 min at w/c = 0.35, and this value is significantly higher than those documented in literature, indicating that sugar is an effective and inexpensive retarder demand. Meanwhile, the proper dosage at w/c = 0.32 advanced the initial setting time to 172 min, which also proved that sugar delayed early hydration. Results of the heat hydration analysis revealed for the solution 1.5 % sugar and w/c of 0.30 that a maximum temperature of 33.89 with difference higher than control up to 3.3 degrees C, which showed that there is interrelation between dosage and thermal kinetics. Also, the bleeding and surface cracking were decreased by sugar which contributed to the one with a better microstructural significance as well as smoother paste faces.
Birds are widely recognized as vital bioindicators of ecosystem health for high sensitivity to environmental changes, and automated bird call analysis using deep learning has demonstrated significant potential for large-scale ecological monitoring; however, its effectiveness is often limited by environmental noise in real-world acoustic recordings. This paper presents an experimental investigation and evaluation of NR-BirdNet, a noise-robust deep learning framework designed for bird call analysis under acoustically challenging conditions. The proposed NR-BirdNet framework employs a hybrid and joint use of Convolution Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) architecture to jointly capture the spectral and temporal characteristics of bird vocalizations, demonstrating improved robustness over existing denoising frameworks and hybrid architectures. Two experimental scenarios are considered: (i) a baseline evaluation using noise-free recordings, achieving a classification accuracy of 98.75 % with limited misclassifications among acoustically similar species, and (ii) a noise-corrupted evaluation incorporating environmental interference, where accuracy decreases to 92.50 %, reflecting the impact of background noise, signal overlap, and low signal-to-noise ratios. NR-BirdNet enhances reliability in realistic, noise-prone environments. The results confirm that environmental noise significantly degrades classification performance and increases learning complexity. The contribution of this study is the development of NR-BirdNet, a noise-robust hybrid CNN-BiLSTM framework, with the advantage of robust, high-accuracy bird call classification in noisy real-world conditions.
The industrial sector seeks to develop enhanced methods to improve thermal transfer efficiency and product quality especially in alteration of reservoir wettability by add nanoparticles to the fluid flow process. Mixing Go + Al2O3 + Ag particles at nanoconcentrations in the raw fluid (kerosene oil) affects the fundamental properties of the oil. This study focuses on the alteration in fluid properties with respect to temperature and nanoparticles concentrations during the flow of the ternary hybrid nanofluid (Go + Al2O3 + Ag/Kerosene oil) affected by magnetic force and thermal effects from viscosity dissipation, energy transfer by radiation, and chemical activity. The governing model for the permeability of porous disc surfaces is the Darcy-Forchheimer model. The fundamental equations of the problem were formulated with the previous assumptions. The intricate differential equations were converted into simpler ones utilizing similarity transformations. The Chebyshev pseudospectral (CPS) method was applied to reach numerical solutions. The numerical results gave an idea of the extent to which the physical parameters of the problem affect the system velocities, temperature, nanoparticle concentration, and microorganism concentration aleph. Results uncover that the size of the nanoparticle plays a vital role; an increase within the volume of the nanoparticle leads to an upgrade in thermal conduction, which builds the rate of heat transfer. This will be an important indication that the oil recovery operation will be increased and accelerated. The heat transfer between the molecules of the ternary hybrid nanofluid was significantly improved over that of the single and binary nanofluids. The skin friction evaluated on the lower disc is 2.8 % greater than the one evaluated on the upper disc, while the opposite occurs with the number of Sherwood and the number of moving densities. In other word the force on the surface of the lower disk that resists motion because of the fluid's viscosity is 2.8 % greater than the force on the surface of the upper disk. A larger value denotes a stronger frictional force. This resistance, also known as skin friction, is a type of drag brought on by the fluid's interaction with the object's surface.
The braking system is a critical safety component in land vehicles, relying on efficient deceleration during operation. Among its elements, the brake disc plays a central role in dissipating the heat generated by frictional forces during braking. Selecting an optimal disc material with strong thermal performance is therefore essential. This study employs ANSYS steady-state simulations to compare the thermal behavior of three brake disc materials: gray cast iron, titanium grade 5, and an aluminum metal composite. The results show that gray cast iron demonstrates superior heat-dissipation capability - dissipating approximately 8-10 % more heat flux than the other materials tested - making it the most suitable option for small- and medium-speed vehicle brake discs. These findings provide practical insights for automotive engineers and manufacturers seeking to design more reliable and thermally efficient braking systems.
Wood is a natural material widely used in construction. Unlike artificial building materials, wood is characterized by natural defects (flaws) that can negatively affect its performance under load. This study proposes a model for calculating the strength and deformations of bending wooden structures based on material deformation diagrams, allowing the consideration of natural wood defects, such as knots. The model accounts for the physical nonlinearity of wood deformation under compression and the presence of materials with different deformation diagrams within the analyzed cross-section, enabling stress redistribution at all stages of loading, up to failure. Theoretical calculation results obtained using proposed method were compared with the own experimental test results and with experimental data, obtained by others. The findings of the experimental and theoretical study confirm the feasibility of applying the diagrammatic approach to calculate the bending strength of wooden structures with defects (knots). This approach involves modeling the deformation of knots under compression using a parabolically linear diagram with a maximum ordinate equal to the resistance of the main wood perpendicular to the grain, while neglecting the tensile performance of knot wood. In addition to evaluating the strength and deformability of wooden elements in buildings under construction or in use, the proposed consideration of wood defects in the form of knots will facilitate the justified rejection of defective wooden elements during sorting.
The limited efficiency of proton exchange membranes under low-humidity conditions remains a major challenge for next-generation fuel cells. Recent studies have explored deep eutectic solvents (DESs) as alternative proton-conducting media to overcome hydration-dependent limitations. In this work, we investigate the effect of solvent hydrophobicity on molecular structure and hydronium ion transport in polyacrylate-based membranes, using three distinct DESs: a hydrophilic system of choline chloride and ethylene glycol (1:2), an amphiphilic system of choline chloride and decanoic acid (1:1), and a hydrophobic system of menthol and lauric acid (1:1), employing density functional theory (DFT) calculations and classical all-atom molecular dynamics (MD) simulations. DFT calculations were used to optimize molecular structures and evaluate electrostatic potential distributions, frontier orbital energies, and hydrogen-bonding capabilities, revealing that the hydrophilic system exhibits the strongest interactions with hydronium ions via stabilized charge delocalization and orbital overlap. MD simulations further elucidated the structural organization of solvent-polymer systems under hydrated conditions, with analyses confirming preferential coordination of hydronium ions with specific functional groups depending on solvent polarity. Interaction energy and diffusion coefficient calculations demonstrated that both water and hydronium mobility are highest in the hydrophilic system, while transport is progressively restricted in amphiphilic and hydrophobic systems. These findings provide fundamental insights into how solvent polarity modulates proton transport, guiding the design of high-performance fuel cell membranes.
Strengthening of reinforced concrete (RC) beams has been an important focus due to deterioration, strength degradation, code compliance, change of use, and increased loading demands over time. This study investigates the flexural enhancement of RC beams using external steel elements with various end conditions. Nine beams were tested under three points loading: three control and six strengthened specimens. Strengthened beams were grouped into two categories. In the first, external rebars were attached only in the maximum flexural zone; in the second, they were extended to the full beam length. To prevent delamination, external bars were anchored with 90 degrees hooks and fixed by welding and epoxy. Results showed that strengthened beams achieved higher ultimate capacities compared to control beams. Beams strengthened only in the maximum moment region exhibited a 21.5 % increase in flexural strength, while those reinforced along the entire span reached a 92.6 % increase. The findings confirm that extending external steel reinforcement throughout the beam length provides a significant improvement in flexural capacity and can be adopted as an effective strengthening technique for RC structures.
Force control in robotic actuators is crucial for stability, safety, and adaptability in contact environments. Series elastic actuators (SEAs) offer advantages such as improved force control, shock absorption, and reduced noise but introduce challenges related to bandwidth and precision. This paper presents a force control strategy based on sliding-mode control (SMC) to enhance robustness and stability in dynamic environments. By treating environmental dynamics as disturbances, the approach ensures stable interaction. A chattering-free implementation is proposed, leveraging simplified task models to maintain high performance. Theoretical stability is established within the sliding mode framework, with safety ensured through the careful design of the sliding variable. Analysis demonstrates the method’s ability to generalize across various interaction scenarios, including stiff contacts, purely inertial loads, and soft materials. The adaptability arises from model parameters with interchangeable physical meanings, allowing seamless application across different environments. However, as environmental stiffness decreases, precision in convergence may degrade. To address potential precision loss in low-stiffness environments, a sliding-mode robustification technique is introduced. Simulations validate the proposed controller’s performance, demonstrating convergence consistency with theoretical expectations. The findings highlight the method’s effectiveness in achieving stable, adaptive force control across diverse conditions.
This paper introduces a new approach for parallel loading of distribution transformers with different impedance voltages and power ratings while considering all other relevant paralleling conditions. This approach is constructed to assist design and maintenance engineers in making informed decisions when a required transformer with specific impedance, voltage, and power rating is unavailable. The primary objective of this paper is to minimize partial stoppages and energy supply shortages resulting from the unavailability of appropriate transformers. Additionally, the study aims to enhance operational reliability in the event of unexpected failures in one or more transformers operating in parallel. The proposed approach optimizes new load sharing for transformers when a new or temporary unit replaces a failed one. The analysis aims to balance the costs of production losses and lifestyle disruptions resulting from energy shortages. These impacts are evaluated against the reduction in total transformer capacity and the operational constraints introduced by the newly installed transformer with a different impedance voltage. The proposed method’s effectiveness is demonstrated through a numerical example involving four different scenarios. The paper concludes with results, conclusions, and recommendations based on the proposed approach.
Conventional extraction of natural dyes requires more energy and time. This research investigated a rapid and low-cost extraction method for red yeast rice, utilizing a pressurized extraction process with a coffee maker machine. The application of the extract for dyeing cotton fabrics was also investigated. The effects of extraction time, particle size, and temperature on the color value (CVU/gds) were investigated. The particle size varied at 16, 18, and 20 mesh, the extraction temperature varied at 85, 90, and 95 degrees C, while the extraction time varied at 3, 6, and 9 s. The dyeing process variables studied were reapplication and dilution of the extract solution. Reapplication was varied at the 1st, 2nd, and 3rd dyeing, while dilutions were varied by 1, 2, and 3 times. The highest color value is 15.3105 CVU/gds, obtained at a 20 mesh particle size and 85 degrees C of extraction. At a significance level of alpha = 0.05, reapplication up to 3 times had no significant effect on color strength (K/S); the K/S of 0.1813 decreased to 0.1521. At the same time, the dilutions significantly decreased the K/S, resulting in a K/S of 0.0129. The wet and dry rubbing fastness test was rated from very good to excellent (4.5-5), whereas the washing fastness test was rated as poor (1-1.5). The subsequent study should investigate the extraction process using higher pressure and organic solvents, as well as determine suitable mordants and higher temperatures for fabric dyeing.
The increasing frequency of extreme hydrological events highlights the critical importance of reliable dam safety assessment and sediment management in large river systems. Three-dimensional numerical modeling of overflow and sediment transport processes is essential for ensuring the reliability of hydraulic structures and understanding the dynamics of reservoir systems. This study focuses on the Shardara Reservoir, located in the Turkestan region of Kazakhstan. The primary objective is to develop a high-resolution 3D numerical model to assess potential variations in water discharge and to analyze the hydrodynamic behavior of the reservoir, including the evaluation of dam breach risks. To simulate multiphase flows, the Volume of Fluid (VOF) method was employed in conjunction with the Pressure-Implicit with Splitting of Operators (PISO) algorithm to solve the Navier-Stokes equations, ensuring computational stability and accuracy under complex flow conditions. Unmanned Aerial Vehicles (UAV) were used to acquire high-resolution topographic data and conduct detailed monitoring of the hydraulic infrastructure. The integration of real-world topography significantly improved the accuracy of boundary conditions and hydrological parameters, enabling precise identification of high-risk zones. The proposed modeling approach is novel in its incorporation of actual terrain features and engineered structures for simulating overflow and dam breach scenarios within this regional context. Numerical experiments demonstrated how changes in discharge affect flow velocity and sediment transport dynamics. The assessment of erosion and sediment transport processes was conducted using a weakly coupled approach, in which flow velocity fields and hydrodynamic regime characteristics were first computed, followed by the analysis of potential erosion and deposition zones based on the Froude criteria. The simulation results showed good agreement with experimental and observational data, confirming the reliability and predictive capability of the developed model.
The article describes the way of metallurgical wastes processing technology of lead production waste slag processing with the subsequent obtaining of lead and zinc by heat treatment. The analysis of existing technologies was carried out and the relevance of industrial wastes processing was revealed, which proved the necessity of this research. Modern instrumental methods were used in the research, confirming the correctness of the experimental work and the results. On the basis of the experiments carried out with metallurgical wastes it become clear, that they contained toxic metals, being a dangerous source of environmental pollution and at the same time a target material. The technology of lead production slag processing by using a drum furnace with subsequent production of non-ferrous metals is recommended. The thermodynamic patterns of the processes occurring during the processing of lead production slag have been studied and thermodynamic parameters have been determined, especially the value of enthalpy, entropy and Gibbs energy in a heterogeneous system. The developed technology makes it possible efficiently to process the industrial waste slag of lead production. The developed technology is also aimed at reducing of the accumulated industrial waste, which in turn makes it possible to regulate the environmental situation in the region.
