Search Results (32,600)

In this study, we propose a novel wideband aperture-coupled magneto-electric (ME) dipole antenna that achieves enhanced bandwidth by simultaneously leveraging ME resonance and aperture-coupled excitation. Building upon the conventional ME dipole architecture, the antenna integrates a pair of horizontal metal patches forming the electric dipole and a pair of vertical metal patches forming the magnetic dipole. A key innovation is the aperture-coupled feeding mechanism, where electromagnetic energy is transferred from a tapered microstrip line to the dipole structure through a slot etched in the ground plane. This design not only excites the characteristic ME resonances effectively but also significantly improves impedance matching, delivering a markedly broader impedance bandwidth. To validate the proposed concept, a prototype antenna was fabricated and experimentally characterized. Measurements show an impedance bandwidth of 84.48% (3.61–8.89 GHz) for $S_{11}$ ≤ −10 dB and a maximum in-band gain of 7.88 dBi. The antenna also maintains a stable, unidirectional radiation pattern across the operating band, confirming its potential for wideband applications such as 5G wireless communications. Full article

Floating offshore wind turbines (FOWTs) unlock superior wind resources and reduce operational barriers. The dynamics of FOWT platforms present added engineering challenges and opportunities. While the motion of the floating platform due to wind and wave disturbances can worsen power quality and increase structural loading, certain movements of the floating platform can be exploited to improve power capture. Consequently, active FOWT platform control methods using conventional and innovative actuation systems are under investigation. This paper develops a novel framework to design nonlinear control laws for six degrees-of-freedom platform motion. The framework uses simplified rigid-body analytical models of the FOWT. Lyapunov’s direct method is used to develop actuator-agnostic unconstrained control laws for platform translational and rotational control. A model based on the NREL-5MW reference turbine on the OC3-Hywind spar-buoy platform is utilized to test the control framework for an ideal actuation scenario. Possible applications using traditional and novel turbine actuators and future research directions are presented. Full article

The growing adoption of IoT applications has led to increased use of low-power microcontroller units (MCUs) for energy-efficient, local data processing. However, deploying deep neural networks (DNNs) on these constrained devices is challenging due to limitations in memory, computational power, and energy. Traditional methods like cloud-based inference and model compression often incur bandwidth, privacy, and accuracy trade-offs. This paper introduces a novel Decentralized Distributed Sequential Neural Network (DDSNN) designed for low-power MCUs in Tiny Machine Learning (TinyML) applications. Unlike the existing methods that rely on centralized cluster-based approaches, DDSNN partitions a pre-trained LeNet across multiple MCUs, enabling fully decentralized inference in wireless sensor networks (WSNs). We validate DDSNN in a real-world predictive maintenance scenario, where vibration data from an industrial pump is analyzed in real-time. The experimental results demonstrate that DDSNN achieves 99.01% accuracy, explicitly maintaining the accuracy of the non-distributed baseline model and reducing inference latency by approximately 50%, highlighting its significant enhancement over traditional, non-distributed approaches, demonstrating its practical feasibility under realistic operating conditions. Full article

This study investigates the application of the Analytic Hierarchy Process (AHP) as a decision-support mechanism for managing complex sustainability issues in industrial settings, specifically within the framework of circular economy principles. Focusing on a case from the brewery sector, developed under the EU ECOFACT initiative, this research evaluates ten distinct configurations for the must cooling process. These alternatives are assessed using environmental, economic, and technical criteria, drawing on data from life cycle assessment (LCA) and life cycle costing (LCC) methodologies. The findings indicate that selecting an optimal scenario involves balancing trade-offs among electricity and water consumption, operational efficiency, and overall environmental impacts. Notably, Scenario 3 emerges as the most balanced option, consistently demonstrating superior performance across the primary evaluation criteria. The use of AHP in this context proves valuable by introducing structure and transparency to a multifaceted decision-making process where quantitative metrics and sustainability objectives intersect. By integrating empirical industrial data with an established multi-criteria decision approach, this study highlights both the practical utility and existing limitations of conventional AHP, particularly its diminished ability to discriminate between alternatives when their scores are closely aligned. These insights suggest that hybrid or advanced AHP methodologies may be necessary to facilitate more nuanced decision-making for circular economy transitions in industrial environments. Full article

Natural polymers, polysaccharides, demonstrate piezoelectric behavior suitable for force sensor manufacturing. Carrageenan hydrogel film with α-iron oxide particles can act as a piezoelectric polysaccharide-based force sensor. The mechanical impact on the hydrogel caused by a falling ball shows the impact response time, which is measured in milliseconds. Repeating several experiments in a row shows the dynamics of fatigue, which does not reduce the speed of response to impact. Through the practical experiments, we sought to demonstrate how theoretical knowledge describes the hydrogel we elaborated, which works as a piezoelectric material. In addition to the theoretical basis, which includes the operation of the metal and metal oxide contact junction, the interaction between the metal oxide and the hydrogel surfaces, the paper presents the practical application of this knowledge to the complex hydrogel film. The simple calculations presented in this paper are intended to predict the hydrogel film’s characteristics and explain the results obtained during practical experiments. Carrageenan, as a low-cost and already widely used polysaccharide in various industries, is suitable for the production of low-cost force sensors in combination with iron oxide. Full article

The integration of computational intelligence techniques into pharmaceutical wastewater treatment offers promising opportunities to improve process efficiency and minimize operational costs. This study compares the predictive capabilities of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) models in forecasting the rejection efficiencies of caffeine and paracetamol using AFC 40 and AFC 80 nanofiltration (NF) membranes. Experiments were conducted under varying operating conditions, including transmembrane pressure, feed concentration, and flow rate. The predictive performance of both models was evaluated using statistical metrics such as the Coefficient of Determination (R²), Root Mean Square Error (RMSE), Marquardt’s Percentage Squared Error Deviation (MPSED), Hybrid fractional error function (HYBRID), and Average Absolute Deviation (AAD). Both models demonstrated strong predictive accuracy, with R² values of 0.9867 and 0.9832 for RSM and ANN, respectively, in AFC 40 membranes, and 0.9769 and 0.9922 in AFC 80 membranes. While both approaches closely matched the experimental results, the ANN model consistently yielded lower error values and higher R² values, indicating superior predictive performance. These findings support the application of ANNs as a robust modelling tool in optimizing NF membrane processes for pharmaceutical removal. Full article

Metaheuristics are powerful optimization techniques that are well-suited for addressing complex combinatorial problems across diverse scientific and industrial domains. However, their application to computationally expensive problems remains challenging due to the high cost and significant number of fitness evaluations required during the search process. Surrogate modeling has recently emerged as an effective solution to reduce these computational demands by approximating the true, time-intensive fitness function. While surrogate-assisted metaheuristics have gained attention in recent years, their application to complex scheduling problems such as the Permutation Flowshop Scheduling Problem (PFSP) under learning, deterioration, and maintenance effects remains largely unexplored. To the best of our knowledge, this study is the first to investigate the integration of surrogate modeling within the artificial bee colony (ABC) framework specifically tailored to this problem context. We develop and evaluate two distinct strategies for integrating surrogate modeling into the optimization process, leveraging the ABC algorithm. The first strategy uses a Kriging model to dynamically guide the selection of the most effective search operator at each stage of the employed bee phase. The second strategy introduces three variants, each incorporating a Q-learning-based operator in the selection mechanism and a different evolution control mechanism, where the Kriging model is employed to approximate the fitness of generated offspring. Through extensive computational experiments and performance analysis, using Taillard’s well-known standard benchmarks, we assess solution quality, convergence, and the number of exact fitness evaluations, demonstrating that these approaches achieve competitive results. Full article

To address the challenges posed by weak early fault signal features, strong noise interference, low diagnostic accuracy, poor reliability when using single information sources, and the limited availability of high-quality samples in practical applications for permanent magnet synchronous motor (PMSM) bearings, this paper proposes an early bearing fault diagnosis method based on Hippopotamus Optimization Variational Mode Decomposition (HO-VMD) and weighted evidence fusion of current–vibration signals. The HO algorithm is employed to optimize the parameters of VMD for adaptive modal decomposition of current and vibration signals, resulting in the generation of intrinsic mode functions (IMFs). These IMFs are then selected and reconstructed based on their kurtosis to suppress noise and harmonic interference. Subsequently, the reconstructed signals are demodulated using the Teager–Kaiser Energy Operator (TKEO), and both time-domain and energy spectrum features are extracted. The reliability of these features is utilized to adaptively weight the basic probability assignment (BPA) functions. Finally, a weighted modified Dempster–Shafer evidence theory (WMDST) is applied to fuse multi-source feature information, enabling an accurate assessment of the PMSM bearing health status. The experimental results demonstrate that the proposed method significantly enhances the signal-to-noise ratio (SNR) and enables precise diagnosis of early bearing faults even in scenarios with limited sample sizes. Full article

This study advances the application of the generalized Halmos’ two projections theorem to idempotent operators on Hilbert $C^{*}$-modules through a comprehensive study of sums involving adjointable idempotents and their adjoints. We establish fundamental properties including the closedness, orthogonal complementability, Moore–Penrose inverses, and spectral norms of such sums. For arbitrary (not necessarily adjointable) idempotent operators that admit a decomposition into linear combinations or products of two idempotents, we derive explicit representations for all such decompositions. A numerical example is given to show how our main theorem allows for the decomposition of idempotent matrices into linear combinations of two idempotent matrices, and two concrete examples on Hilbert $C^{*}$-modules validate the theoretical significance of our framework. Full article

Honey bees play a vital role in ecosystem stability, and the need to monitor colony health has driven the development of IoT-based systems in beekeeping, with recent studies exploring both empirical and machine learning approaches to detect and analyze key hive conditions. In this study, we present an IoT-based system that leverages sensors to record and analyze the acoustic signals produced within a beehive. The captured audio data is transmitted to the cloud, where it is converted into mel-spectrogram representations for analysis. We explore multiple data pre-processing strategies and machine learning (ML) models, assessing their effectiveness in classifying queenless states. To evaluate model generalization, we apply transfer learning (TL) techniques across datasets collected from different hives. Additionally, we implement the feature extraction process and deploy the pre-trained ML model on a deep edge IoT device (Arduino Zero). We examine both memory consumption and execution time. The results indicate that the selected feature extraction method and ML model, which were identified through extensive experimentation, are sufficiently lightweight to operate within the device’s memory constraints. Furthermore, the execution time confirms the feasibility of real-time queenless state detection in edge-based applications. Full article

The use of augmented reality (AR) tools and innovative learning environments in education have increased over the last few years due to the rapid advancement of technology. In this study, an AR mathematics learning intervention has been proposed which consisted of the creation of 3D multibase blocks to perform AR arithmetic calculations conducted through active methodologies in the future classroom lab (FCL). The aim of this study was to analyze pre-service teachers’ (PSTs) affective domain (emotion, self-efficacy, and attitude), engagement, motivation, and confidence. The sample consisted of 97 PSTs enrolled on the second year of the Primary Education degree, who were attending the “Mathematics and its Didactics” subject. The findings revealed a significant increase in PSTs’ satisfaction, fun, confidence, and pride, and a decrease in uncertainty, nervousness, and concern. Regarding PSTs’ self-efficacy, a significant improvement was observed in knowing the necessary steps to teach mathematical concepts and work in the FCL. No significant differences were found in attitude, engagement, and motivation; however, the PSTs showed a high disposition in all of them before starting the intervention. Additionally, the PSTs reported to be more confident, and it enhanced their knowledge in the use of 3D design and AR applications to create multibase blocks to support the teaching–learning content of arithmetic operations. Full article

Coronary artery bypass grafting (CABG) has evolved into a cornerstone treatment for coronary artery disease, with graft selection playing a critical role in long-term outcomes. Multiple arterial grafting (MAG) represents a significant advancement over single arterial grafting, utilizing conduits such as the internal thoracic artery and radial artery to enhance graft durability and patient survival. This review examines the outcomes, challenges, and controversies associated with MAG, highlighting its superior patency rates and reduced need for repeat revascularization procedures. While the technique provides long-term survival benefits, concerns such as the complexity of surgical techniques, increased operative time, and higher resource utilization underscore the importance of surgeon expertise and institutional infrastructure. Patient selection remains critical, as factors like age, comorbidities, and gender influence outcomes and highlight disparities in access to MAG. Emerging evidence addresses debates regarding optimal graft choice and balancing long-term benefits against short-term risks. Future directions focus on ongoing clinical trials, innovations in minimally invasive and robotic-assisted CABG, and technological advancements aimed at improving graft patency. Professional guidelines and best practices underscore the need for personalized approaches to optimize MAG’s potential. This article underscores the promise of MAG in redefining CABG care, paving the way for enhanced patient outcomes and broadened applicability. This article highlights the promise of MAG in transforming CABG care, leading to improved patient outcomes and expanded applicability. Full article

This study presents a photoresist-free patterning method for solution-processed indium zinc oxide (IZO) thin films using two photochemical exposure techniques, namely pulsed ultraviolet (UV) light and UV-ozone, and a plasma-based method using oxygen (O₂) plasma. Pulsed UV light delivers short, high-intensity flashes of light that induce localised photochemical reactions with minimal thermal damage, whereas UV-ozone enables smooth and uniform surface oxidation through continuous low-pressure UV irradiation combined with in situ ozone generation. By contrast, O₂ plasma generates ionised oxygen species via radio frequency (RF) discharge, allowing rapid surface activation, although surface damage may occur because of energetic ion bombardment. All three approaches enabled pattern formation without the use of conventional photolithography or chemical developers, and the UV-ozone method produced the most uniform and clearly defined patterns. The patterned IZO films were applied as active layers in bottom-gate top-contact thin-film transistors, all of which exhibited functional operation, with the UV-ozone-patterned devices exhibiting the most favourable electrical performance. This comparative study demonstrates the potential of photochemical and plasma-assisted approaches as eco-friendly and scalable strategies for next-generation IZO patterning in electronic device applications. Full article

Maintenance techniques are fundamental in the context of the safe operation of continuous process installations, especially in electrical energy-transmission and/or -distribution substations. The operating conditions of power transformers are fundamental for the safe functioning of the electrical power system. Predictive maintenance consists of periodically monitoring the asset in use, in order to anticipate critical situations. This article proposes a methodology based on data science, machine learning and the Internet of Things (IoT), to track operational conditions over time and evaluate transformer aging. This characteristic is achieved with the development of a synchronization method for different databases and the construction of a model for estimating ambient temperatures using k-Nearest Neighbors. In this way, a history assessment is carried out with more consistency, given the environmental conditions faced by the equipment. The work evaluated data from three power transformers in different geographic locations, demonstrating the initial applicability of the method in identifying equipment aging. Transformer TR1 showed aging of $3.24\times {10}^{-3}\%$, followed by TR2 with $8.565\times {10}^{-3}\%$ and TR3 showing $294.17\times {10}^{-6}\%$ in the evaluated period of time. Full article

With the rapid development of drone technology, target detection and estimation of radar parameters for maneuvering targets have become crucial. Drones, with their small radar cross-sections and high maneuverability, cause range migration (RM) and Doppler frequency migration (DFM), which complicate the use of traditional radar methods and reduce detection accuracy. Furthermore, the detection of multiple targets exacerbates the issue, as target interference complicates detection and impedes parameter estimation. To address this issue, this paper presents a method for high-resolution multi-drone target detection and parameter estimation based on the adjacent cross-correlation function (ACCF), fractional Fourier transform (FrFT), and deep learning techniques. The ACCF operation is first utilized to eliminate RM and reduce the higher-order components of DFM. Subsequently, the FrFT is applied to achieve coherent integration and enhance energy concentration. Additionally, a convolutional neural network (CNN) is employed to address issues of spectral overlap in multi-target FrFT processing, further improving resolution and detection performance. Experimental results demonstrate that the proposed method significantly outperforms existing approaches in probability of detection and accuracy of parameter estimation for multiple maneuvering targets, underscoring its strong potential for practical applications. Full article