Search Results (2,415)

This paper presents a complete workflow for the evaluation of GaN transistors in voltage source inverters. With the associated high switching speed of transistors based on GaN, it is important to consider some critical points in the design phase as well as in the measurement setup in order to be able to utilise and verify the advantages of GaN properly. For this reason, the presented circuit board’s design focuses on a minimised power loop inductance. Simulation models, an analytical approach and measurement results with the aim of determining this inductance are compared with each other. A good compliance results between the presented methods. Additionally, the description of a test bench is given, which enables the performance of the opposition method. This setup allows the measurement of the designed H-bridge’s arising losses and the GaN-transistor’s switching behaviour. In comparison to the conventional double pulse method, this approach enables results that are more accurate for determining losses. Full article

Efficient object detection is crucial to real-time monitoring applications such as autonomous driving or security systems. Modern RGB cameras can produce high-resolution images for accurate object detection. However, increased resolution results in increased network latency and power consumption. To minimise this latency, Convolutional Neural Networks (CNNs) often have a resolution limitation, requiring images to be down-sampled before inference, causing significant information loss. Event-based cameras are neuromorphic vision sensors with high temporal resolution, low power consumption, and high dynamic range, making them preferable to regular RGB cameras in many situations. This project proposes the fusion of an event-based camera with an RGB camera to mitigate the trade-off between temporal resolution and accuracy, while minimising power consumption. The cameras are calibrated to create a multi-modal stereo vision system where pixel coordinates can be projected between the event and RGB camera image planes. This calibration is used to project bounding boxes detected by clustering of events into the RGB image plane, thereby cropping each RGB frame instead of down-sampling to meet the requirements of the CNN. Using the Common Objects in Context (COCO) dataset evaluator, the average precision (AP) for the bicycle class in RGB scenes improved from 21.08 to 57.38. Additionally, AP increased across all classes from 37.93 to 46.89. To reduce system latency, a novel object detection approach is proposed where the event camera acts as a region proposal network, and a classification algorithm is run on the proposed regions. This achieved a 78% improvement over baseline. Full article

As the demand for energy-efficient homes continues to rise, the importance of advanced mechanical ventilation systems in maintaining indoor air quality (IAQ) has become increasingly evident. However, challenges related to energy balance, IAQ, and occupant thermal comfort persist. This review examines the performance of mechanical ventilation systems in regulating indoor climate, improving air quality, and minimising energy consumption. The findings indicate that demand-controlled ventilation (DCV) can enhance energy efficiency by up to 88% while maintaining CO₂ concentrations below 1000 ppm during 76% of the occupancy period. Heat recovery systems achieve efficiencies of nearly 90%, leading to a reduction in heating energy consumption by approximately 19%. Studies also show that employing mechanical rather than natural ventilation in schools lowers CO₂ levels by 20–30%. Nevertheless, occupant misuse or poorly designed systems can result in CO₂ concentrations exceeding 1600 ppm in residential environments. Hybrid ventilation systems have demonstrated improved thermal comfort, with predicted mean vote (PMV) values ranging from –0.41 to 0.37 when radiant heating is utilized. Despite ongoing technological advancements, issues such as system durability, user acceptance, and adaptability across climate zones remain. Smart, personalized ventilation strategies supported by modern control algorithms and continuous monitoring are essential for the development of resilient and health-promoting buildings. Future research should prioritize the integration of renewable energy sources and adaptive ventilation controls to further optimise system performance. Full article

Aluminium alloy 6082 is widely used in the automotive and aerospace industries due to its high strength-to-weight ratio. However, its structural integrity can sometimes be affected by an early fatigue failure. This study investigates the fatigue performance of extruded 6082-T6 samples through a series of fatigue tests conducted at varying stress levels. The material showed significant variability under identical fatigue conditions, suggesting the presence of microstructural defects. Scanning electron microscopy with energy-dispersive spectroscopy (SEM/EDS) and scanning transmission electron microscopy (S/TEM) were used to identify the nature and location of the defects and evaluate the underlying mechanisms influencing the fatigue performance. Computer tomography (CT) also confirmed the presence of oxide inclusions on the fracture surface and near the edges of the samples. These oxide inclusions are distributed throughout the material heterogeneously and in the form of broken oxide films, suggesting that they might have originated during the material’s early processing stages. These oxides acted as stress concentrators, initiating microcracks that led to catastrophic and unpredictable early failure, ultimately reducing the fatigue life of micro-oxide-containing samples. These results highlight the need for better casting control and improved post-processing techniques to minimise the effect of oxide presence in the final components, thus enhancing their fatigue life. Full article

This study assessed the feasibility of producing lightweight aggregates (LWAs) using diatomite waste (DW) as a clay substitute. The research aimed to reduce the consumption of natural resources and minimise the environmental impacts caused by the disorderly disposal of DW. Chemical, physical, and mechanical tests were carried out on six formulations of mixtures containing 50% to 100% DW, sintered between 1100 and 1250 °C, resulting in 24 samples. The aggregates had a particle density between 1.14 and 2.13 g/cm³, a maximum bloating index of 5.7%, a crushing strength of up to 11.14 MPa, and a mass loss of up to 8.7%. Minimum porosity of 2.8 percent and water absorption of 2.0 percent were observed. Sixteen samples met the criteria required for commercial applications, demonstrating that replacing clay with DW is technically feasible. The high porosity of DW was found to influence the density of the LWAs. The findings of this study highlight the environmental sustainability of using DW as an alternative raw material, contributing to circular economy strategies in the construction sector. Full article

Bioabsorbable polymer stents (BPSs) were developed to address the long-term clinical drawbacks associated with permanent metallic stents by gradually dissolving over time before these drawbacks have time to develop. However, the polymers used in BPSs, such as polylactic acid (PLA), have lower mechanical properties than metals, often requiring larger struts to provide the necessary structural support. These larger struts have been linked to delayed endothelialisation and an increased risk of stent thrombosis. To address this limitation, this study investigated the incorporation of high-strength basalt fibres into PLA to enhance its mechanical performance, with an emphasis on optimising the processing conditions to achieve notable improvements at minimal fibre loadings. In this regard, PLA/basalt fibre composites were prepared via twin-screw extrusion at screw speeds of 50, 200, and 350 RPM. The effects were assessed through ash content testing, tensile testing, SEM, and rheometry. The results showed that lower screw speeds achieved adequate fibre dispersion while minimising the molecular weight reduction, leading to the most substantial improvement in the mechanical properties. To examine whether a second extrusion run could enhance the fibre dispersion, improving the composite’s uniformity and, therefore, mechanical enhancement, all the batches underwent a second extrusion run. This run improved the dispersion, leading to increased strength and an increased modulus; however, it also reduced the fibre–matrix adhesion and resulted in a notable reduction in the molecular weight. The highest mechanical performance was observed at 10% fibre loading and 50 RPM following a second extrusion run, with the tensile strength increasing by 20.23% and the modulus by 27.52%. This study demonstrates that the processing conditions can influence the fibres’ effectiveness, impacting dispersion, adhesion, and molecular weight retention, all of which affect this composite’s mechanical performance. Full article

Innovative approaches in hot forging, such as the use of floating dies, which aim to minimise burr formation by controlling material flow, require precise management of die geometry distortions. These distortions, primarily caused by thermal gradients, must be tightly controlled to prevent malfunctions during production. This study introduces a comprehensive thermal analysis framework that captures the complete forging cycle—from billet transfer and die closure to forging, spray-cooling, and lubrication. Two advanced heat transfer models were developed: a pressure- and lubrication-dependent contact heat transfer model and a spray-cooling model that simulates fluid dispersion over die surfaces. These models were implemented within the finite element software FORGE-NxT to evaluate the thermal behaviour of dies under realistic operating conditions. These two new models, contact and spray-cooling, implemented within a full-cycle thermal simulation and validated with industrial thermal imaging data, represent a novel contribution. The simulation results showed an average temperature deviation of just 5.8%, demonstrating the predictive reliability of this approach. This validated framework enables accurate estimation of thermal fields in the dies, and offers a practical tool for optimising process parameters, reducing burr formation, and extending die life. Moreover, its structure and methodology can be adapted to various hot forging applications where thermal control is critical to ensuring part quality and process efficiency. Full article

The study aims to increase productivity in the cold room door manufacturing industry by addressing non-value-adding operations, identifying bottlenecks, and reducing processing time through digital twin (DT)-based simulation. The goal is to eliminate the need for supply chain outsourcing and increase overall efficiency. The research involves developing a DT of the existing production process for five distinct categories of cold room doors: flush door, single door, double door, face-mounted door, and sliding door. Simulation was used to uncover problems at multiple stations, encompassing curing, welding, and packing. Lean principles were used to identify the causes of inefficiency, and the process was improved using TRIZ principles. These changes produced a 42.90% improvement in productivity, a 20% dependence reduction on outsourcing and an increase of 10.5% added inventory to the shortage demand level. The approach presented is provided for a particular manufacturer of cold room doors, but the methods and techniques used are generally applicable to other manufacturing companies to support systematic innovation. Combining DT simulation, lean techniques and TRIZ principles, this study presents a strong approach to addressing the productivity challenges in manufacturing. The incorporation of these methods has brought considerable operational efficiency and has minimised dependency on external outsourcing. Full article

Cement production significantly contributes to CO₂ emissions (8% of worldwide CO₂ emissions) and global warming, accelerating climate change and increasing air pollution, which harms ecosystems and human health. To this end, this research investigates the fresh and hardened properties of sustainable concrete fabricated with three different replacement percentages (0%, 5%, and 10% by weight) of ordinary Portland cement (OPC) using rice husk ash (RHA). The hardened properties were evaluated at 14, 28, 60, 90, and 120 days of water curing. In addition, data-based models were developed, validated, and optimised, and the models were compared with experimental results and validated with the literature findings. The outcomes reveal that the slump values increased (17% higher) with the increased content of RHA, which aligns with the lower temperatures (12% lower) of freshly mixed concrete with RHA than the control mix (100% OPC). The slopes of the stress–strain profiles decreased at early ages and improved at longer curing ages (more than 28 days), especially for mixes with 5% RHA. The compressive strength decreased slightly (18% at 28 days) with increased percentages of RHA, which was minimised with increased curing ages (8% at 90 days). The data-based model accurately predicted the stress–strain profiles (coefficient of determination, $R^2$ ≈ 0.9950–0.9993) and compressive strength at each curing age, including crack progression (i.e., highly nonlinear region) and validates its effectiveness. In contrast, the optimisation model shows excellent results, mirroring the experimental data throughout the profile. These outcomes indicate that the 10% RHA could potentially replace OPC due to its lower reduction in strength (8% at 90 days), which in turn lowers CO₂ emissions and promotes sustainability. Full article

Predicting workload for tasks in manufacturing is a complex challenge due to the numerous variables involved. In small- and medium-sized enterprises (SMEs), this process is often experience-based, leading to inaccurate predictions that significantly impact production planning, order management, and consequently the ability to meet customer deadlines. This paper presents an approach that leverages machine learning to enhance workload prediction with minimal data collection, making it particularly suitable for SMEs. A case study application using supervised machine learning models for regression, trained in an open-source data analytics, reporting, and integration platform (KNIME Analytics Platform), has been carried out. An Automated Machine Learning (AutoML) regression approach was employed to identify the most suitable model for task workload prediction based on minimising the Mean Absolute Error (MAE) scores. Specifically, the Regression Tree (RT) model demonstrated superior accuracy compared to more traditional simple averaging and manual predictions when modelling data for a single product type. When incorporating all available product data, despite a slight performance decrease, the XGBoost Tree Ensemble still outperformed the traditional approaches. These findings highlight the potential of machine learning to improve workload forecasting in manufacturing, offering a practical and easily implementable solution for SMEs. Full article

The analysis of wastewater is essential in environmental chemistry, particularly for monitoring emerging contaminants and assessing ecological impacts. In this context, hyphenated chromatographic techniques are widely used, with liquid chromatography being one of the most common. However, gas chromatography coupled with mass spectrometry (GC-MS) remains a valuable tool in this field due to its sensitivity, selectivity, and widespread availability in most laboratories. This review examines the application of validated methods for wastewater analysis using GC-MS (MS), highlighting its relevance in identifying micropollutants such as pharmaceuticals, drugs of abuse, pesticides, hormones, and industrial by-products. The validation of analytical methods is crucial to ensuring the reliability and reproducibility of data and the accurate monitoring of contaminants. Key parameters, including sample volume, recovery efficiency, and detection and quantification limits, are discussed, evaluating different approaches to optimising the identification of different classes of contaminants. Additionally, this study explores advances in sample preparation techniques, such as solid-phase microextraction (SPME), dispersive liquid–liquid microextraction (DLLME), and solid-phase extraction (SPE), which enhance efficiency and minimise interferences in the analysis. Finally, future perspectives are discussed, including the integration of emerging technologies such as high-resolution mass spectrometry, the miniaturisation of GC systems, and the development of faster and more sustainable analytical methods. Full article

This paper investigates the application of non-calcined metal tartrate as a novel alternative pore former to prepare functional ceramic composites to fabricate solid oxide fuel cells (SOFCs). Compared to carbonaceous pore formers, non-calcined pore formers offer high compatibility with various ceramic composites, providing better control over porosity and pore size distribution, which allows for enhanced gas diffusion, reactant transport and gaseous product release within the fuel cells’ functional layers. In this work, nanocrystalline gadolinium-doped ceria (GDC) and Ni-Gd-Ce-tartrate anode powders were prepared using a single-step co-precipitation synthesis method, based on the carboxylate route, utilising ammonium tartrate as a low-cost, environmentally friendly precipitant. The non-calcined Ni-Gd-Ce-tartrate was used to fabricate dense GDC electrolyte pellets (5–20 μm thick) integrated with a thin film of Ni-GDC anode with controlled porosity at 1300 °C. The dilatometry analysis showed the shrinkage anisotropy factor for the anode substrates prepared using 20 wt. The percentages of Ni-Gd-Ce-tartrate were 30 wt.% and 40 wt.%, with values of 0.98 and 1.01, respectively, showing a significant improvement in microstructural properties and pore size compared to those fabricated using a carbonaceous pore former. The results showed that the non-calcined pore formers can also lower the sintering temperature for GDC to below 1300 °C, saving energy and reducing thermal stresses on the materials. They can also help maintain optimal material properties during sintering, minimising the risk of unwanted chemical reactions or contamination. This flexibility enables the versatile designing and manufacturing of ceramic fuel cells with tailored compositions at a lower cost for large-scale applications. Full article

Construction costs have increased significantly since the COVID-19 pandemic due to supply chain disruption, labour shortages, and construction material price hikes. The market is increasingly demanding innovative construction methods that can save construction costs, reduce construction time, and minimise waste and carbon emission. The prefabrication system has been used for years in industrial construction, resulting in better performance in regard to structure stability, the control of wastage, and the optimisation of construction time and cost. In addition, prefabrication has had a positive contribution on resource utilisation in the construction industry. There are various types of prefabricated wall systems. However, the majority of comparative studies have focused on comparing each prefabrication wall system against the conventional construction system, while limited research has been conducted to compare different prefabrication structures. This study examined four prominent prefabricated wall systems, i.e., precast walls, tilt-up walls, prefabricated steel-frame walls, and on-site-cut steel-frame walls, to determine which one is more suitable for the construction of industrial buildings to minimise cost, time delay, and labourer utilisation on construction sites, as well as to enhance structure durability, construction efficiency, and sustainability. One primary case project and five additional projects were included in this study. For the primary case project, data were collected and analysed; for example, a subcontractor cost comparison for supply and installation was conducted, and shop drawings, construction procedures, timelines, and site photos were collected. For the additional five projects, the overall cost data were compared. The main research finding of this study is that factory-made precast walls and tilt-up wall panels require similar construction time. However, on average, tilt-up prefabrication construction can reduce the cost by around 23.55%. It was also found that prefabricated frame walls provide cost and time savings of around 39% and 10.5%, respectively. These findings can provide architects, developers, builders, suppliers, regulators, and other stakeholders with a comprehensive insight into selecting a method of wall construction that can achieve greater efficiency, cost savings, and environmental sustainability in the construction of industrial and commercial buildings. Full article

Project delays remain a persistent challenge in the construction industry, having significant financial implications and contributing to disputes between project participants. Forensic Delay Analysis (FDA) has emerged as a specialised function that identifies the root causes of such delays, quantifies their duration, and assigns responsibility to the appropriate parties. While FDA is a widely practised process, it has yet to fully exploit the potential of emerging technologies. This study explores the integration of both existing and emerging technologies for enhancing FDA processes. A Design Science Research (DSR) approach is adopted, with data collection methods that involve the use of the literature, archival materials, case studies and survey methods. The research demonstrates how the use of technologies, such as database management systems (DBMSs), building information modelling (BIM), artificial intelligence (AI) and games engines, can improve the analytical efficiency, data management, and presentation of findings through a case study. The study showcases the transformative potential of these interventions in streamlining FDA processes, ultimately leading to more accurate and efficient resolution of construction disputes. The proposed process is exemplified by the development of a prototype: the Forensic Information Modelling Visualiser (FIMViz). The FIMViz is a practical tool that has received positive evaluation by FDA experts. The prototype and the enhanced FDA process model that underpins it demonstrate significant advancement in FDA practices, promoting improved decision-making and collaboration between project participants. Further development is needed, but the results could ultimately streamline the FDA process and minimise the uncertainties in FDA outcomes, thus reducing the incidence of costly disputes to the wider economic benefit of the industry generally. Full article

Electric vehicles are gaining attention and being adopted by new users every day. Their widespread use creates a new scenario and challenge for the energy system due to the high energy storage demands they generate. Forecasting these loads using artificial neural networks has proven to be an efficient way of solving time series problems. This study employs a multilayer perceptron network with backpropagation training and Bayesian regularisation to enhance generalisation and minimise overfitting errors. The research aggregates real consumption data from 200 households and 348 electric vehicles. The developed method was validated using MAPE, which resulted in errors below 6%. Short-term forecasts were made across the four seasons, predicting the total aggregate demand of households and vehicles for the next 24 h. The methodology produced significant and relevant results for this problem using hybrid training, a few-neuron architecture, deep learning, fast convergence, and low computational cost, with potential for real-world application. The results support the electrical power system by optimising these loads, reducing costs and energy generation, and preparing a new scenario for EV penetration rates. Full article