This study compares the dynamic initiation fracture toughness of five fibre-reinforced concrete materials with different types of fibre reinforcement. The base material was steel fibre-reinforced ultra-high-performance concrete (S). Four additional materials were created by incorporating various secondary fibre reinforcements into the base mix: aramid (SA), polyvinyl alcohol (SPVA), carbon (SG), and polypropylene (SP). Dynamic fracture initiation toughness was determined by conducting mediumspeed tensile tests on double-edge notched specimens. These tests were performed with a modified Instron Ceast 50 J instrumented Charpy tester, using a striking velocity of 3.8 ms−1 and a total impac energy capacity of 7.5 J. The polypropylene fibre-reinforced concrete (SP) exhibited the highest average dynamic initiation fracture toughness (171.4 kPam0.5) and also demonstrated the greatest impact toughness.
The increasing need for security measures in urban environments has highlighted the importance of effective anti-vehicle barriers. This paper presents the design, numerical simulations, and testing of an anti-vehicle barrier tailored for rapid deployment in cities. The proposed design strikes a balance between the need for high security and transportation and urban space constraints, while minimizing visual impact, and allowing pedestrian access. The effectiveness of the barrier against vehicular threats is evaluated through numerical simulations and real crash tests. The results offer practical insights for designing such protective barriers.
This article provides an in-depth analysis of the use of non-traditional secondary raw materials as a partial replacement for silica sand in the autoclaved aerated concrete (AAC). Three types of foundry sand from different sources and three varieties of waste glass (WG) were selected and subjected to experimental evaluation at substitution levels of 5 %, 10 %, and 15 %. The experimental verification reveals that using foundry sand and WG results in physico-mechanical and mineralogical properties comparable to, and in certain cases superior to, conventional AAC formulations. Conversely, brown and mixed WG led to the formation of atypical mineral phases, which significantly influenced the characteristics of AAC. Of the evaluated materials, foundry sand sourced from non-ferrous casting operations emerged as the most promising alternative, particularly at a 10% substitution, where enhanced strength and an optimised microstructure were observed. The article presents a structured experimental methodology, segmented into three stages: the selection and preparation of secondary raw materials, a comprehensive assessment of their physico-mechanical properties, and an advanced microstructural characterisation. The findings underscore the feasibility of using these secondary raw materials in AAC manufacturing without compromising material performance.
Reducing energy consumption and CO2 emissions has recently become a priority for the cement industry. The most effective approach appears to be replacing Portland clinker with high levels of supplementary cementitious materials (SCMs). The general approach is to use clinker with a high alite content and high reactivity, especially during the initial hydration phase. TiO2 is one of the common minor oxides in industrial clinker, typically present at about 0.3 wt. %. Previous studies have shown that TiO2 concentrations around 1% improve strength. This study investigates the performance of a TiO2-doped clinker (1 wt. %) in binary and ternary blends with calcined clay and limestone, focusing on its effect on hydration, rheology, and microstructure. The reactivity of cement pastes over 72 hours was determined using isothermal calorimetry, while rheological parameters and the thixotropy index were assessed during the first 45 minutes of hydration. The phase composition development was monitored using QXRD after 2, 7 and 28 days, and microstructure was examined using SEM-EDS (SE) after 2 and 28 days of hydration. The use of TiO2-doped clinker in LC3 shows a promising potential for sustainable cement production, owing to the synergistic effect of minor elements introduced into clinker minerals during firing and the high reactivity of limestone combined with calcined clay.
Combined sewer overflows (CSOs) represent a significant challenge in urban drainage systems, particularly during heavy rainfall events when their hydraulic capacity is exceeded. Effective separation of suspended solids in CSO structures is essential to reduce the pollutant load discharged into receiving waters. This study investigates the sensitivity of separation efficiency to various design parameters of a tube CSO using computational fluid dynamics (CFD). The chamber, designed as a part of a multifunctional hydraulic object of a real-world sewer network project, was analysed using numerical modelling, employing the Volume of Fluid (VOF) model and Lagrangian particle approach. The influence of two key design aspects, the total chamber length and overflow slot geometry configurations, was examined under different flow rate conditions. The results show a strong and consistent correlation between increased chamber length and improved separation efficiency, confirming the importance of sufficient residence time for directing the solids to the pumps. In contrast, modifications to slot geometry resulted in only minor and inconsistent changes in performance, suggesting that its impact may be dependent on specific flow dynamics and chamber configuration.
This study focuses on the influence of raw limestone materials on the fresh-state properties and early hydration behaviour of alkali-activated systems composed of blast furnace slag and metakaolin. Three types of limestone powders were examined in relation to their rheological impact, structural rebuilding (via oscillatory strain-amplitude sweeps and 3iTT tests), calorimetric behaviour, and thermal stability (TG/DTG). The results show that, at early stages, limestone primarily acts as an inert filler, but its particle morphology and impurity content significantly affect the system’s rheology and reaction kinetics. Limestone powder with high calcite content slightly accelerates structural regeneration and improves dispersion, while limestone powders containing clay minerals increase plasticity and stiffness due to water retention and enhanced interparticle interactions. Such clay-bearing powders can, however, also slow structural rebuilding and reduce flowability under shear. Calorimetry and thermal analysis confirmed that limestone powder addition, especially in low-calcium systems, promoted the formation of hybrid gels (C-(A)-S-H and N-A-S-H). Clay-related alumina also significantly affected both the early-age rheology and the reaction kinetics.
Portland cement clinker contains C3A as one of its key phases. During firing, it forms a melt that is essential for the formation of C3S and C2S. Commonly, two polymorphs of C3A are present: cubic and orthorhombic, and their presence influences the kinetics of cement hydration. This study focused on the effects of Na2O and Rb2O dopants on the polymorphic modifications of C3A. It was found that Na2O in concentrations of 0–2.5 % promotes the formation of the cubic polymorph, while higher concentrations, above 2.5 %, lead to a transition to the orthorhombic polymorph. For Rb2O, incompatibility with calcium was observed, resulting in the formation of new phases and the leaching of rubidium oxide at high firing temperatures. In terms of hydration, samples containing Na2O exhibited higher reactivity due to the presence of orthorhombic C3A, whereas Rb2O slowed down hydration, which remains an issue for further research.
Polymeric materials are widely used in military and defence applications, including personal and vehicle ballistic protection, protective masks and suits, vehicle components, and ammunition transport and storage packaging. In these applications, polymers are exposed to harsh environmental conditions and extreme temperatures, which can significantly affect their long-term performance. In this study, commercially available polymeric materials based on polyethylene (PE), polypropylene (PP) modified with thermoplastic elastomer (TPE) at different PP/TPE compositions, polyetheretherketone (PEEK), and polyamide (PA) were studied. The materials were exposed to selected chemical agents, namely fuels (gasoline) and decontaminants (hypochlorite decontamination mixture), as well as to climatic conditions including high (+100 °C) and low (−40 °C) temperatures, high humidity and UV radiation. The aim of this study was to determine the durability physical and mechanical properties of the polymers in the selected environments with increasing exposure time (from one to six weeks). Hardness (Rockwell and Shore methods), tensile strength, elongation, and tensile modulus were observed. The results showed that exposure to gasoline and UV radiation caused the most pronounced changes. Among the tested materials, PE exhibited the highest overall resistance, showing only minor degradation even under the most aggressive conditions, such as gasoline exposure and UV radiation.
The study investigates the effect of synthetic fibre addition on the heat and ballistic resistance of steel fibre-reinforced reactive powder concrete (RPC). A comprehensive experimental programme was conducted involving prismatic (40 mm × 40 mm × 160 mm) and cylindrical (150 mm in diameter, 40mm in height) specimens subjected to a range of elevated temperatures and ballistic impacts. Steel fibre-reinforced RPC specimens containing additional synthetic fibre reinforcement, namely polyethene terephthalate (PET), polyvinyl alcohol (PVA), and aromatic polyamide, which varied in geometry, were cast. The specimens were subjected to elevated temperatures ranging from 200 °C to 800 °C (in 200 °C increments with dwell time of 2–6 hours), and their residual compressive and flexural strength under quasi-static loading was then evaluated. Ballistic resistance was evaluated through depth of penetration (DOP) tests, which involved the impact of 7.62 × 54R B32 armour-piercing incendiary (API) projectiles at a striking velocity of 850 ms−1. Differential efficiency factor (DEF), structural integrity, and impact crater dimensions were determined. The results show that the addition of PET and PVA fibres significantly improved the RPC’s heat resistance, with PET fibres providing the highest residual strength and integrity after prolonged high-temperature exposure, while aramid fibres did not improve the thermal performance. Both PET and PVA fibres also markedly reduced the ballistic damage area and maintained high ballistic resistance after heating. The findings highlight the potential of steel-synthetic fibre hybridisation (especially with PET fibres) to design advanced RPC materials capable of withstanding combined ballistic and thermal threats, making them suitable for critical infrastructure and protective structures in extreme multi-hazard environments.
Oral cancer detection is essential, especially in areas with high occurrence rates, is essential for better early diagnosis and individualised treatment plans. SV-OnionNet is a deep learning framework presented in this article that aims to improve the classification accuracy of oral cancer diagnosis. While maintaining important structural details, the method lowers noise in medical pictures by integrating an adaptive Non-Linear Means (NLM) filter. Spatial features are improved by the Label-Guided Attention (LGA) module, which guarantees constant labelling and improves feature extraction. By enabling accurate pixel-level segmentation of lesions, Seg-UNet provides increased classification reliability. The Support Vector Machines (SVM) deep learning classification model used in the SV-OnionNet architecture preserves spatial relationships for improved feature learning, replacing traditional fully linked layers (LKN). The Competitive Search Optimization (CSO) algorithm fine-tunes model parameters, therefore optimising feature selection and classification. The evaluation on the Mouth and Oral Diseases dataset demonstrated exceptional accuracy, precision, recall, and specificity, with the proposed classification achieving a 99.94% accuracy. These findings emphasise the effectiveness of SV-OnionNet in improving the diagnostic accuracy and reliability. The study highlights the potential of integrating deep learning techniques with optimisation strategies to advance oral cancer detection. Future research will focus on expanding datasets and exploring additional optimisation methods to further improve the classification performance.
Friction stir welding (FSW) is an eco-friendly, sustainable, solid-state process that is increasingly being used to join metallic, non-metallic, polymer, and composite materials to create high-quality welds with minimal flaws. The tool pins’ profiles govern material flow, heat generation, and weld integrity. The literature shows that threaded, taper threaded, triangular, and hybrid pins enhance mixing, grain refinement, hardness, and tensile strength, while cylindrical or smooth pins often cause defects. Microstructural investigations confirm that complex pin geometries promote finer grains and higher strength and hardness. A fracture analysis of welded samples reveals that shift in failure location from the nugget to the thermo-mechanically affected zone (TMAZ) depends on the geometry of the tool pin. Despite these advances, only a few studies have examined different materials, and standardised evaluation of tool geometries is lacking. Using computational and machine learning methods for predictive modelling, expanding applicability to lightweight alloys in aerospace and automotive manufacturing, and developing hybrid and adaptive pin profiles are the upcoming research priorities.
. The increased use and commercialisation of portable battery powered electronic gadgets has made low-power chip designs essential for extending battery life. Low-power electronic circuits also play an important role in the emerging wireless communication systems. Designing appropriate memory circuits is the key for the aforementioned cases, as memory occupies most of the chip area. In this Access Memory) with enhanced stability is proposed. Simulations are carried out for the proposed CNFETSRAM cell, and its performance is compared with the conventional structures in terms of power, delay, stability, and power delay product by varying PVT (process-voltage-temperature) parameters. According to the results, the hold, read and write stability of the proposed CNFET SRAM improved by 49%, 85% and 56%, respectively, as compared to existing memory cells. Furthermore, the hold or leakage power is minimised by up to 99 % compared to conventional SRAMs. The simulation results confirm that the proposed solution is an appropriate memory structure for MIMO systems, meeting the requirements for very large-scale integration (VLSI) circuits with low leakage and high stability.
This study focuses on identifying hidden potential suicide attempts by traffic accidents (PSA-TA) in the Czech Republic by combining exploratory data analysis (EDA), classification algorithms (KNN, XGBoost), and interpretability (SHAP) to design a predictive model capable of distinguishing PSA-TA from ordinary fatal road traffic accidents (FRTA). The results of the analysis show that cases of suicidal behaviour exhibit specific characteristics. Typically, these involve collisions with a fixed obstacle outside residential areas, at times of low traffic, without the use of safety features, involving a single vehicle, and often involving male drivers. Based on these characteristics, the model identified 13 cases from 2024 that are likely to bear the hallmarks of intentional behaviour, even though they were officially recorded as ordinary FRTA. The results of the study confirm that advanced analytical tools can be used to detect hidden suicidal behaviour ex post, thereby contributing to more accurate statistics, forensic assessment of accidents, and the development of targeted preventive policies in the areas of traffic safety and mental health. We also outline ethical aspects and key limitations of administrative coding, including potential misclassification.
Reliable extraction of the cartridge case after firing is essential to the proper functioning of a gas-operated gun. This study presents a predictive model for the extraction force of a 7.62 & times;39 mm steel cartridge case, based on the maximum chamber pressure and the contact friction coefficient (between the cartridge case and the chamber). A Central Composite Design (CCD) with two factors was used to generate the simulation data from finite element models developed in ANSYS. A reduced quadratic regression model was constructed and statistically validated, showing a high predictive capability (R-2 = 0.969, adjusted R-2 = 0.953). The model reveals that friction has a stronger influence on the extraction force than the maximum pressure, and that their interaction is non-linear. Experimental validation at the design centre, using a custom-built extraction test rig, yielded an average measured force of 39.10 N, closely matching the predicted value of 38.90 N (error = 0.51 %). The proposed model is a fast and reliable tool for the design and optimisation of ammunition and extractor mechanisms in small arms.
. The road network in Iraq, particularly in the central and southern regions, suffers from frequent rutting due to harsh conditions, high temperatures, and the limited resistance of Regular Asphalt (RA) mixtures to repeated loading. These deformations reduce the service life of the pavement and increase maintenance costs. This study investigates the potential of improving asphalt mixture performance by incorporating Natural Asphalt (NA) sourced from the Abu Al-Jeer springs in Anbar Governorate. RA is the petroleum asphalt, which was obtained from the Dora oil refinery. The NA underwent two forms of treatment: thermal processing and blending with RA at varying ratios (20%, 40%, 60%, and 80%). Repeated load tests were performed on six different mixtures at 40 degrees C, with an applied pressure of 0.138 MPa for 6 000 loading cycles. The results showed that mixtures containing treated NA exhibited significantly greater resistance to permanent deformation and in the Resilient Modulus (Mr). Notably, the mix containing 80% of NA showed a 72% reduction in permanent microstrain compared to the mixes containing RA, indicating a significantly improved mechanical performance. These findings suggest that NA is a viable and eco-friendly alternative for improving asphalt mixture performance in hot climates such as Iraq.
Contactless palmprint recognition is a widely used method of personal identification. Its performance relies primarily on the feature extraction stage, where intra-variability (pose, scale, and illumination) must be considered. This study presents a novel and challenging contactless palmprint representation called the Deep Statistical Image Features (DSIF), which combines the Discrete Wavelet Transform (DWT) with the Principal Component Analysis Network (PCANet). The methodology uses the following steps: First, the DWT of levels 1 and 2 is applied to extract different sub-band images. Next, the PCANet algorithm is applied to the palmprint image and the low-frequency sub-band images. Then, histograms are extracted and concatenated. Finally, the reduced representation is constructed using Whitened Principal Component Analysis (WPCA). The key contribution of this study is its feature extraction methodology, which uses multiresolution analysis instead of multi-patch decomposition in order to obtain pertinent information from various image resolutions. The proposed method uses the entire IIT-Delhi contactless database to construct the model, which is then tested on two other contactless palmprint databases, CASIA and Tongji. The method achieved rank-1 identification rates of 99.80 % on CASIA, 98.77 % on Right Tongji, and 99.07 % on Left Tongji, results that are impressive compared to current approaches and methods.
This paper presents the design and analysis of a three-phase photovoltaic inverter based on a Boost-Buck-Discharge microinverter architecture. It converts low DC voltages (24–240 V), typical of PV panels, into high-quality three-phase AC with minimal THD. The topology integrates a boost converter elevating voltage to 240 V, a buck-discharge stage generating rectified sinusoidal waveforms, and a full-bridge inverter producing pure sinusoidal outputs. A step-up transformer ensures standardised voltages of 225 V RMS (single-phase) and 390 V RMS (line-to-line) with galvanic isolation. Sliding mode control is applied to buck-discharge circuits to ensure robust and stable operation, validated via Lyapunov analysis. Results show THD below 3 % for all tested resistive and inductive loads, confirming efficient multilevel conversion and suitability for decentralised renewable energy systems requiring reliable three-phase DC-AC transformation.
The intensification of clinker production is one of the strategies for reducing energy consumption and CO2 emissions associated with cement manufacturing. This study explores the use of various mineralisers and fluxes, specifically lithium, copper, and sulphur, added to the raw meal for clinker burning. These components can originate from both traditional and alternative fuels and raw materials. The influence of these elements on clinker melt formation, phase composition, and microstructure was investigated in the laboratory. Raw meals prepared from common cement materials were doped with chemically pure compounds Li2CO3, CuO, and (NH4)2SO4 in graded amounts. The thermal processes of the raw meals were monitored using differential thermal analysis coupled with a thermogravimetric analysis. The phase composition and microstructure of the resulting clinkers were analysed using X-ray powder diffraction and light microscopy. All dopants were found to lower the melt formation temperature, with lithium having the most significant effect. The dopants also caused changes in the phase composition and microstructure of the clinker, particularly affecting the size and shape of the alite crystals and the volume of the belite unit cell.
This paper investigates the dynamic disturbances affecting weapon systems mounted on unmanned ground vehicles (UGVs), which pose a significant challenge in maintaining aiming accuracy when moving across uneven terrain. These disturbances arise from terrain-induced vibrations, complex hull movements, suspension-induced vibrations, and cross-inertial interactions between the gun barrel and the turret, particularly under asymmetrical road excitations. This research aims to develop a comprehensive mathematical model describing the dynamic disturbances caused by mass imbalances and cross-inertial effects in weapon-UGV systems and to analyse the influence of asymmetrical and non-uniform road surfaces on weapon system vibrations. The proposed nonlinear dynamic model is constructed using Euler rotation matrices and coordinate transformation methods, incorporating suspension-induced disturbances. An uneven road model with varying roughness heights between the left and right sides and asymmetrical profiles was introduced, including sequential semi-sinusoidal, trapezoidal, and rectangular ridge shapes to represent battlefield-like terrain conditions. The governing equations were solved in MATLAB-Simulink to evaluate weapon vibrations, angular deviations, and disturbance torques. The simulation results showed that asymmetrical road excitation significantly amplified the disturbances to the weapon system during aiming. A scaled UGV model was used to conduct experiments on vehicle body vibrations while moving over a rough terrain section, assessing the effect of suspension and uneven road surfaces on the weapon system. The results demonstrate that the developed dynamic disturbance model provides a solid basis for future stabilisation and compensation control strategies. It improves the firing accuracy of weapon systems mounted on unmanned ground vehicles operating in real-world conditions.
Heterogeneity of many building materials complicates numerical modelling of structural behaviour. The material randomicity can be manifested by different values of the material parameters of each material specimen. To capture the inherent variability of heterogeneous materials, the model parameters describing the material properties are considered as random variables and their identification consists in solving a stochastic inversion problem. The stochastic inversion is based on searching for a probabilistic description of the model parameters that provides the model response distribution corresponding to the distribution of the observations. The paper presents two different formulations of the stochastic inversion problem. The first formulation arises from the Bayesian inference of uncertain statistical moments of a prescribed parameters’ distribution, whereas the second is centered on a nonlinear transformation of the random model parameters from the observed data distribution.
