Abstract: Radar signal coherent integration technology is a critical method to improve the performance of detection systems. However, existing techniques face challenges regarding real-time performance and the flexibility of multi-pulse coherent accumulation. In this paper, a dynamically configurable multi-pulse multi-frame real-time coherent integration system based on FPGA is designed and implemented, and the dynamic configuration of the number of pulses and the number of frames stored for each pulse is realized through the host computer. The experimental results show that the output signal delay of coherent integration is 33 microseconds at 40 pulses, and the energy gain reaches 16 dB at 40 pulses, which provides a dynamically configurable hardware platform and solution for real-time coherent integration of high-frame-count, multi-pulse radar signals.

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Background Disc degeneration is an increasingly common problem in modern society and is often a precursor to a herniated disc. Contributing factors include physical exertion, overuse, the natural aging process, and disease and injury. Over time, the fibrous ring of the disc develops cracks and small tears, allowing fluid from the nucleus pulposum to escape. As a result, the ability of the disc to absorb shock decreases, potentially leading to a bulging or herniated disc. In this work, previously initiated investigations are extended, and additional thermoplastic polyurethane (TPU) filaments are examined with respect to their suitability for additive manufacturing as potential disc replacement materials. Materials & Methods To remain comparable, the additive manufacturing in this work is also carried out with Fused Deposition Modeling (FDM) 3D printers and as a Ø50 mm x 10mm disc. The Gyroid was varied from 10 mm³ for the coarsest structure to 4 mm³ for the finest structure. The wall thickness of the Gyroid was also varied from 0.5 to 1.0 mm, as were the outer walls of the disc, whose wall thickness was varied from 0.4 to 0.8 mm. Four different TPU filaments (Extrudr FlexSemiSoft, GEEETECH TPU, SUNLU TPU and OVERTURE TPU) were used. This resulted in 36 different settings per filament. The 3D printed discs were analyzed using an Olympus SZ61 stereomicroscope. A tensile test according to DIN EN ISO 527-1 was performed on the 3D printed samples 5A. The aim was to investigate the difference between the different TPU filaments. To test the mechanical properties of the 3D printed discs, a uniaxial compression test was performed with at least three samples of each setting. The body was compressed to 50% of its total height and the force required was recorded as a force-deformation curve. To be comparable to a previous project, a maximum force of 4000–7500 N was used. Results Of the 36 different discs tested for each filament, only a maximum of three were within the target range of maximum force. Microscopy revealed that all wall thicknesses were within the target range with only minor variations. The tensile strengths of Geetech, SunLu, and SemiSoft were not significantly different and were in a similar range of 10-11 MPa, with Overture deviating significantly at 9 MPa. The tensile moduli exhibited a comparable distribution: 25-30 MPa for Geetech, SunLu, and SemiSoft, and 17.5 MPa for Overture. Conclusion For all of the filaments tested, it was possible to additively produce suitable discs that were within the specified range of 4000-7500 N at 50% compression. This would ensure that these discs would withstand the stresses they would be subjected to in a potential human disc replacement application. Thus, we were able to confirm the suitability of these four filaments, as well as the Gyroid structures, for use as a disc replacement.

Abstract: This study proposes an analytical framework that converts operational records into structured research hypotheses, offering a systematic approach to understanding failure mechanisms. The framework combines descriptive statistics, time-series analysis, linear regression, and machine-learning techniques to identify patterns, irregularities, and residual model behavior. Within the scope of this research, the framework was implemented as executable Python code and tested on a representative downtime category observed in a real industrial machine. The application demonstrated how analytical outputs can be translated into inductive, deductive, and abductive hypotheses that support deeper exploration of failure dynamics. The findings indicate the need for a comprehensive, multifaceted analytical approach to interpreting and forecasting machine downtimes, emphasizing that combining quantitative insights with the development of reasoning-based hypotheses increases the explainability and methodological rigor of the results..

Abstract: Nowadays, decarbonization of the shipping industry has become the top priority of the maritime community. In an effort to reduce emissions from shipping, numerous tech-nological and design solutions are being investigated; Waste Heat Recovery (WHR) by marine engines is one of the most important and widespread ones. This paper investi-gates the utilization of a carbon dioxide Supercritical Brayton Cycle (SBC) for WHR of a LNG carrier. SBC is an innovative, promising technology for power generation with unprecedented performance and a small form factor, due to the properties of the working fluid. A thermodynamic model is developed and programmed in MATLAB using the CoolProp free library. By means of this model, the performance of simple and recuperated SBC (RSBC) for WHR of a specific marine engine at its full load operation is assessed and the optimum compressor pressure ratio for power maximization of the RSBC is selected. The combined system Diesel-RSBC exhibits an increase of about 2.9% in thermal efficiency and a similar reduction in specific fuel oil consumption, com-pared to the sole power production by the Diesel engine, at its full load operation. Sig-nificant performance benefits are also demonstrated at part-load operation of the main engine. To assess how the benefits scale with the main engine power, seven similar marine engines of different power are considered, revealing a possible relationship between the optimal pressure ratio and SBC efficiency with the engine’s exhaust gas temperature.

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The main objective of this study is to investigate the influence of cognitive stress (mental workload) on some physiological parameters and reactions of a set of experimental subjects. The aim is to check whether these indicators, observed simultaneously, can distinguish the state of rest from the state of mental tension and whether they can distinguish tasks of different difficulty. An assessment of the state of rest in the study protocol is also performed. The experiments implemented a multimodal, non-invasive BCI for tracking physiological responses during cognitive task performance. Five parallel measured parameters are used: electroencephalography (EEG), heart rate (HR), galvanic skin response (GSR), facial surface temperature, and oxygen saturation (SpO₂). The results show that HR is a fast and reliable marker for detecting psychological load, the normalized phase GSR is good for detecting higher loads, EEG α/θ can be used for central validation, facial temperature is shown to be a slowly changing but reliable context indicator and SpO₂ preservation can be used as a measure of stability.

Abstract: This study experimentally investigates the cooling performance of a single-opening wind catcher model under varying orientations and wind speeds. The wind catcher was connected to a horizontal cavity representing an indoor space, with a rear outlet simulating a window opening. Electric resistors were installed at the catcher shaft and in the middle of the cavity length to simulate the building’s heat loads. Experiments were conducted in a wind tunnel, where K-type thermocouples were employed to record temperature variations for both closed and open cavity end. Five wind speeds (4–9 m/s) and five orientations (0°–180°) were examined. Under the closed-cavity configuration, the maximum temperature reduction (cooling) of 4 °C occurred at an orientation of 180°, at which the catcher opening was positioned on the leeward side. This orientation created a low-pressure region at the catcher’s inlet, located within the wake of the model, which combined with a favorable vertical temperature gradient enhanced suction-driven cooling. In the open-cavity configuration, cooling was observed for all orientations and wind speeds. The greatest temperature reduction of 9 °C occurred at the 135° orientation, whereas other orientations produced temperature drops from 2 °C to 6 °C.

Abstract: Assessing the stress–strain state around interacting mining excavations using the finite element method (FEM) is computationally expensive for parametric studies. This study evaluates tabular machine-learning surrogate models for the rapid prediction of full stress–strain fields in jointed rock. A dataset of 1000 parametric FEM simulations using the elastoplastic Hoek–Brown constitutive model was generated to train Random For-est, LightGBM, CatBoost, and Multilayer Perceptron (MLP) models based on geometric features. The results show that the best models achieve R2 scores of 0.96–0.97 for stress components and 0.99 for total displacements. LightGBM and CatBoost provide the op-timal balance between accuracy and computational cost, offering speed-ups of 15 to 70 times compared to FEM. While Random Forest yields slightly higher accuracy, it is re-source-intensive. Conversely, MLP is the fastest but less accurate. These findings demonstrate that data-driven surrogates can effectively replace repeated FEM simula-tions, enabling efficient parametric analysis and intelligent design optimization for mine workings.

Abstract: This study examines how the injection rate affects fracture complexity during hydraulic fracturing using water as the fracturing fluid. Experiments were conducted on concrete, impermeable sandstone, and brittle bituminous coal across a wide range of injection rates. The results showed that both low (< 1.0mL/min) and high (≥5.0mL/min) injection rates produced simple, planar fractures with limited branching. In contrast, an intermediate rate of 2.2mL/min consistently generated the most complex fracture networks. Fracture complexity was evaluated using 3D-scanned surface roughness quantified by the Joint Roughness Coefficient (JRC), which reached its highest values at the optimum injection rate. Although water is sometimes considered less effective than unconventional fluids, the findings demonstrate that it performs well in moderately permeable rocks with sufficient tensile strength. However, water was less effective in brittle coal and highly impermeable sandstone, where more compressible, low-viscosity fluids such as supercritical CO2 or nitrogen may be advantageous. Overall, three fracture behavior regimes were identified: a low-rate regime producing simple fractures, a mid-rate optimal regime producing complex networks, and a high-rate regime where fracture complexity decreased. Thin fractures dominated because controlled injection conditions focused on observing fracture behavior rather than merely inducing fracture, as reflected by the relatively high post-fracture injection pressure.

Abstract: The presented paper discusses and analyzes the methods for restoration of damaged stay rings and vanes of large Francis turbines. The focus is set on the crack damages that were also observed in stay vanes of Francis turbines in “Chaira” Pumped Hydro Energy Storage (PHES) plant, Hydro Unit 4 (HU4), where cracks occurred in all ten stay vanes. Additionally significant cavitation patches where noticed overall the front surface of the turbines stay vanes. Two approaches are examined in the study. The first approach for repairing adds material by welding over the damaged places, which are then polished. Although welding of damaged areas is a widespread method, some adverse effects are observed, mainly because of changes in the characteristics of the base metal and the impact of the high temperatures. The second approach is based on the removal of the damaged material till elimination of the cracks. Virtual models for computer simulation of both methods are prepared. These models are examined by structural mechanics and CFD analyses and compared. Final recommendations are proposed.

Abstract: This paper presents a comprehensive linear dynamic analysis of a four-degree-of-freedom (4-DOF) lumped mass shear building model, numerically implemented within the OpenSeesPy framework. The numerical model parameters, including mass distribution, stiffness, and damping characteristics, are derived directly from prior experimental testing of physical frame models. The study evaluates the structural response under a statistically significant suite of 50 earthquake ground motion records, carefully selected and scaled to match the Eurocode 8 target spectrum for Soil C and a peak ground acceleration of 0.1 g, reflecting the specific seismic hazard conditions of Osijek, Croatia. Time-history analyses utilizing the Newmark-Beta integration scheme reveal critical insights into the dynamic behaviour, specifically the influence of base flexibility on fundamental period elongation and the distinct response characteristics across different storeys. Statistical processing of the results, visualized through response spectra fractiles and correlation profiles, demonstrates that while lateral displacements exhibit stable first-mode dominance, floor accelerations display high dispersion and significant higher-mode amplification, known as the whipping effect, particularly at the roof level. The findings underscore the limitations of deterministic analyses and highlight the necessity of probabilistic frameworks for accurately assessing seismic demand in low-to-mid-rise structures.

Abstract: Ultrasound applications are currently a very intense field of research, always in expansion, constantly finding new uses for them in all scientific fields. The basic process investigated in this research is that of obtaining wires by wire-drawing technology, which consists of successively passing a semi-finished wire through a sequence of dies until the desired final diameter is obtained. This plastic deformation process usually takes place at room temperature. The phenomena that can still be studied in this process and improved is that of reducing the friction between two parts that are in relative motion named also “ultrasonic lubrication”. Based on this phenomenon, technology for obtaining metal wires by wire drawing can be improved by increasing the wiredrawing speed. The article presents the design and calculation of the ultrasonic transducer used to activate the drawing die, the FEM to determine the optimal vibration frequencies and vibration modes used in activation of the die. To demonstrate the effect of reducing the coefficient of friction during wire drawing, a test stand was used in which the pulling force is achieved by a pneumatic system and the die is one of those used in a wire production factory. By ultrasonic activation of the die, an increase in the working speed was obtained. The percentage increase in the drawing speed is different, depending on the initial traction speed.

Abstract: Electric vehicle drivetrains reveal tonal gearbox noise once masked by combustion engines. Among the most persistent are ghost orders narrowband spectral components not aligned with nominal mesh harmonics linked to periodic gear tooth surface waviness. This review synthesizes research on the w aviness to transmission error to ghost order pathway focusing on detection modeling and validation methods relevant to EVs. Measurement techniques range from single flank transmission error tests and torsional order tracking to high speed full flank metrology with advanced waviness analysis enabling earlier identification of tonal risk. Modeling approaches include quasi static loaded tooth contact analysis with sinusoidal superposition multi body dynamics with micro geometry driven transmission error modulation and hybrid finite element multi body dynamics workflows that integrate measured topography. Findings show that circumferentially coherent waviness even at sub micron amplitudes can produce audible ghost tones in low damping EV drivetrains especially when coinciding with structural resonances. However predictive accuracy remains limited by inconsistent waviness terminology incomplete data transfer from metrology to simulation and scarce EV representative validation under varying loads and speeds. The emerging trend is toward integrated design manufacturing simulations where measured flank surface data directly informs noise prediction models. Standardized waviness metrics routine use of measured topographies in contact models and psychoacoustic or machine learning aided tonal assessment are identified as priorities for improving ghost order prediction and mitigation in EV gearbox design and production flow.

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This work investigates a smart manufacturing approach to monitor gear noise, vibration, and harshness (NVH) in high-speed electric drivetrain gears. We focus on how micro-geometry errors introduced by the honing process can imprint waviness on gear teeth that causes persistent gear whine (including non-integer “ghost” noise orders). Compounding this challenge, vibrations can propagate through factory structures, making source identification difficult when multiple machines operate in proximity. We propose a cloud-based Industrial IoT architecture: a dense network of low-cost accelerometers synchronized via Precision Time Protocol (PTP IEEE 1588) collects vibration data across the plant. Each measurement is tagged via Data Matrix Code (DMC) and work-order integration to link it to the specific gear and process. Big Data infrastructure (time-series database, object storage) combined with real-time stream processing enables anomaly detection (using models like Isolation Forest and XGBoost) and root-cause analysis with explainable AI (SHAP values). A feasibility study outlines requirements (accuracy, latency, security) and compares design options (wired vs wireless sensors, PTP vs NTP sync, MQTT/OPC UA protocols, edge vs cloud processing). We present a 12-month pilot implementation plan and a conceptual system architecture. The solution aims to reduce scrap and rework, lower warranty risks, enable predictive maintenance, and support smart factory initiatives by providing early-warning NVH quality insights for each produced gear.

Abstract: Growing cooling demand and environmental concerns surrounding mechanical vapour compression systems motivate research into alternative technologies capable of converting low-grade heat into useful cooling. Silica-gel/water single-stage dual-bed adsorption chillers (ADCs) are promising candidates, however, their design must balance conflicting performance targets. This study proposes a regression-assisted multi-objective optimisation framework for low-grade-heat ADC, combining statistically validated surrogate models with the Ant Lion Optimiser and its multi-objective variant. Three co-equal objectives, coefficient of performance (COP), cooling capacity (Q_cc) and waste-heat recovery efficiency (η_e) are jointly maximised to map an operational envelope for sustainable cooling. Two-dimensional Pareto-optimal solutions exhibit a one-dimensional ridge in which η_e declines, and COP and Q_cc increase simultaneously. Within the explored bounds, non-dominated ranges span COP=0.675–0.717, Q_cc=18.3–27.5 kW and η_e=0.118–0.127, with a practical compromise near COP ≈ 0.695, Q_cc ≈ 24 kW and η_(e )≈ 0.122–0.123. While mass flow rate decisions increase Q_cc at the expense of η_e, a one-at-a-time sensitivity analysis with re-optimisation identifies the hot- and chilled-water inlet temperatures and exchanger conductance as the dominant decision variables and maps diminishing-return regions. The proposed framework can effectively use low-grade heat in future low-carbon buildings and processes and supports the configuration of ADC systems.

Abstract: Amid growing environmental concerns, resource depletion, and the pressing challenges of industrial waste management, this study investigates the potential of magnesium slag (MS) as a sustainable alternative binder in the production of one-part geopolymer con-cretes (OPGC). The objective is to reduce reliance on conventional cementitious materials while promoting the valorization of industrial by-products in construction practices. For this purpose, ten different mixtures were designed by replacing ground granulated blast furnace slag (GGBS), the conventional aluminosilicate precursor, with MS, an innovative aluminosilicate precursor, at replacement levels of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% by weight, using a solid activator. The fresh and hardened properties of these mixtures were systematically evaluated through slump, setting time, density, ultrasonic pulse velocity (UPV), and strength tests, while microstructural characterization was also conducted using scanning electron microscopy (SEM) coupled with ener-gy-dispersive X-ray spectroscopy (EDX) to further investigate the geopolymerization process, elemental distribution, and the role of MS in binder formation in OPGC. The results revealed that MS incorporation significantly influenced both workability and mechanical performance, and it was confirmed that MS actively participates in geo-polymerization and can be effectively utilized up to a certain threshold. Replacement levels up to 30% were found to maintain acceptable mechanical performance, providing evidence that MS is a promising precursor for developing sustainable OPGC.

Abstract: This study presents the Structural–Typological–Value Sensitivity Model (STVSM), a multi-dimensional framework for evaluating vulnerability in historic buildings where fragility cannot be explained by structural indicators alone. Existing models prioritise load-bearing behaviour but overlook typological discontinuity, spatial fragmentation and erosion of cultural or architectural value. STVSM addresses this through three weighted sub-indices—structural vulnerability (SV), typological degradation (TV) and heritage value (HV)—each calibrated using expert-derived micro–macro coefficients. Field-based deterioration scores (0–1) are multiplied by these final weights to produce SV, TV and HV values, then merged into a Conservation Priority Index (CPI).The model is applied to twenty-five buildings in three heritage contexts: Cumalıkızık traditional houses, vernacular dwellings in Balıkesir–Karesi and nineteenth-century Greek Orthodox churches in Bursa. The churches yield the highest CPI values due to roof loss, wall deformation and spatial discontinuity, reinforced by cultural significance. Vernacular houses show moderate structural deterioration but marked typological distortion linked to later additions and façade alterations. Cumalıkızık houses present heterogeneous conditions, combining preserved structures with material decay.By quantifying structural behaviour, typological integrity and heritage value within a single analytical system, STVSM offers a transparent and repeatable basis for conservation prioritisation across diverse historic building stocks.

Abstract: Rain erosion testing of wind turbine blade coatings is still based almost entirely on kinetic test parameters while ignoring the temperature–salinity domains that control field damage. This short communication quantifies how far current rain erosion test conditions diverge from offshore environmental temperature–salinity envelopes. Using seas surface climatology for four offshore regions (North Sea, US Atlantic shelf, Taiwan Strait–South China Sea, Bass Strait) and temperature and water composition parameters from 11 water-based erosion studies, we show the environmental sea surface temperature (SST) spans 5 – 17 °C in the North Sea and 8 – 24 °C in the US Atlantic, rising to 20 – 32 °C in the Asia–Pacific and Bass Strait, with sea surface salinity (SSS) typically 31 – 35.5 PSU. In contrast, all reported droplet erosion tests were run between 18 and 29 °C; three of 11 used only freshwater (~ 0 PSU) and the remainder a single seawater-like level (3.0 – 3.5% NaCl, ≈30 – 35 PSU). No study combined marine salinity with cold (<10 °C) or tropical (≥28 – 30 °C) temperatures, despite evidence of markedly higher damage in saline media and up to an order of magnitude increase in polyurethane rates near the glass transition region.

Abstract: Building Information Modelling (BIM) has changed the landscape of the architectural, engineering and construction industry in the recent past decades. However, BIM is not well-researched in most of developing countries such as Malawi; few or no studies address BIM adoption in the country. Therefore, this study seeks to examine the status of BIM in the Malawi construction industry through a well-structured survey. A non-probability and purposive sampling were adopted in this study. A total of 265 questionnaires were sent out. A total of 143 questionnaires were filled that is a 54% response rate. The research has revealed that construction experts are aware of BIM though the level of up take is quite low. Architects in Malawi are the most knowledgeable, seconded by land surveyors and then engineers. Similarly, it was revealed that majority of construction experts in Malawi have less than two years’ experience in the use of BIM. And have applied BIM in less than two projects using basic software such as AutoCAD, seconded by Civil 3D. Hence, this is clear evidence that BIM usage is in the initial stage and is just picking up in the country. And the research has shown that the majority of experts in Malawi are on level 1 of BIM usage which is the first stage of BIM adoption and is characterized by use of 3D models and output representation. Therefore, there is need for robust sensitization on the benefits of BIM and the training to improve its uptake in the construction industry in the country.

Abstract: Reliable detection of the onset of accelerated degradation is essential for safe and cost-effective operation of lithium-ion batteries. This paper proposes a Bayesian changepoint model designed for ``smart sensing'' in battery management systems (BMS), where intelligence is applied to standard voltage–current–temperature (V–I–T) telemetry rather than new sensing hardware. We define a simple, interpretable health indicator (HI)--the ratio of charge time to discharge time--computed directly from cycle-level BMS-compatible features. A probabilistic, piecewise-linear model with a single changepoint is fitted using Hamiltonian Monte Carlo, yielding posterior distributions for the onset of accelerated degradation and for pre/post-change slope behaviour. The method provides calibrated uncertainty intervals that can be integrated into risk-aware BMS decision processes, addressing the limitations of deterministic breakpoint heuristics that supply only point estimates. Using an open dataset of 18650 lithium-ion cells, the model consistently identifies a mid-life transition in the HI trajectory and demonstrates good predictive adequacy through posterior predictive checks. The approach is computationally lightweight, transparent, and directly compatible with embedded implementations. By transforming standard BMS telemetry into an uncertainty-aware degradation signal, the proposed framework supports the development of intelligent and deployable sensing strategies for next-generation battery systems.

Abstract: This paper numerically and experimentally establishes a connection between shear deformation of the AISI 304 steel surface layer and the sliding velocity of a diamond indenter in multi-pass nanostructuring burnishing. Results of finite-element simulation of the process fully correspond to the experimental data obtained when changing the sliding velocity from 40 to 280 m/min after one and five tool passes. The experiment’s burnishing force was assumed to be 150 and 175 N, and feed was 0.025 mm/min. After surface machining, the maximum microhardness reached 400 HV0.05 at the depth of 30 µm from the surface after five indenter passes with the sliding velocity values of 40 and 200 m/min and burnishing force of 175 N.