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.

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 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.

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 paper, CNFET (Carbon Nanotube Field Effect Transistor)-based low-leakage SRAM (Static Random 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.

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 °C, with an applied pressure of 0.138MPa 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.

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.

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.

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 7.62×39 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 (R2 = 0.969, adjusted R2 = 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.

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.

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.