Eng

Visual map-based robot navigation is a strategy that only uses the robot vision system, involving four fundamental stages: learning or mapping, localization, planning, and navigation. Therefore, it is paramount to model the environment optimally to perform the aforementioned stages. In this paper, we propose a novel framework to generate a visual map for environments both indoors and outdoors. The visual map comprises key images sharing visual information between consecutive key images. This learning stage employs a pre-trained local feature transformer (LoFTR) constrained with a 3D projective transformation (a fundamental matrix) between two consecutive key images. Outliers are efficiently detected using marginalizing sample consensus (MAGSAC) while estimating the fundamental matrix. We conducted extensive experiments to validate our approach in six different datasets and compare its performance against hand-crafted methods. Full article

Skid resistance of a road pavement surface is the force developed when a tyre is prevented from rotating and slides along the pavement surface. This property comes from the combination of the macro- and micro-texture of pavement. The skid resistance of an asphalt pavement is an important parameter influencing driving safety on a road since there is a proven relationship between skid resistance and accident parameters. This paper deals with the measurement principle of pavement skid resistance (surface friction) including longitudinal and transverse friction. A high number of measuring devices of skid resistance are also introduced, highlighting their advantages and limitations. Moreover, the measurement policies in the European Union and in Hungary are outlined. Pavement surface texture is investigated, dealing with the levels of surface texture, the most common measuring techniques, the macro-texture features of asphalt types, as well as the Hungarian regulation in the field. As a related topic, the aggregate properties and their implication in the relevant Hungarian specification are introduced briefly as well. Some outcomes of the EU’s COST Action 354 on the development of unified European macro-roughness and skid resistance performance indicators and indices are also presented. Full article

This paper presents a novel methodology for the early detection of oil palm bud degeneration based on computer vision. The proposed system uses the YOLO algorithm to detect diseased plants within the bud by analyzing images captured by a drone within the crop. Our system uses a drone equipped with a Jetson Nano embedded system to obtain complete images of crops with a 75% reduction in time and with 40% more accuracy compared to the traditional method. As a result, our system achieves a precision of 92% and a recall of 96%, indicating a high detection rate and a low false-positive rate. In real-time detection, the system is able to effectively detect diseased plants by monitoring an entire hectare of crops in 25 min. The system is also able to detect diseased plants other than those it was trained on with 43% precision. These results suggest that our methodology provides an effective and reliable means of early detection of bud degeneration in oil palm crops, which can prevent the spread of pests and improve crop production. Full article

Cold weather conditions pose significant challenges to the performance and durability of concrete materials, construction processes, and structures. This paper aims to provide a comprehensive overview of the material-related challenges in cold weather concrete construction, including slow setting, reduced curing rate, and slower strength development, as well as frost damage, early freezing, and freeze–thaw actions. Various innovative materials and technologies may be implemented to address these challenges, such as optimizing the concrete mix proportions, chemical admixtures, supplementary cementitious materials, and advanced construction techniques. The paper also examines the impact of weather-related challenges for personnel, equipment, and machinery in cold environments and highlights the importance of effective planning, communication, and management strategies. Results indicate that the successful implementation of appropriate strategies can mitigate the challenges, reduce construction time, and enhance the performance, durability, and sustainability of concrete structures in cold and freezing temperatures. The paper emphasizes the importance of staying updated about the latest advancements and best practices in the field. Future trends include the development of smart and functional concrete materials, advanced manufacturing and construction techniques, integrated design, and optimization of tools, all with a strong focus on sustainability and resilience. Full article

Multi-criteria decision-making (MCDM) methods have gained increased attention in sustainable engineering, where complex decision-making problems require consideration of multiple criteria and stakeholder perspectives. This review paper provides a comprehensive overview of the different MCDM methods, their applications in sustainable engineering, and their strengths and weaknesses. The paper discusses the concept of sustainable engineering, its principles, and the different areas where MCDM methods have been applied, including energy, manufacturing, transportation, and environmental engineering. Case studies of real-world applications are presented and analyzed, highlighting the main findings and implications for engineering practice. Finally, the challenges and limitations of MCDM methods in sustainable engineering are discussed, and future research directions are proposed. This review contributes to the understanding of the role of MCDM methods in sustainable engineering and provides guidance for researchers and practitioners. Full article

Tunnel Boring Machines (TBMs) have become prevalent in tunnel construction due to their high efficiency and reliability. The proliferation of data obtained from site investigations and data acquisition systems provides an opportunity for the application of machine learning (ML) techniques. ML algorithms have been successfully applied in TBM tunnelling because they are particularly effective in capturing complex, non-linear relationships. This study focuses on commonly used ML techniques for TBM tunnelling, with a particular emphasis on data processing, algorithms, optimisation techniques, and evaluation metrics. The primary concerns in TBM applications are discussed, including predicting TBM performance, predicting surface settlement, and time series forecasting. This study reviews the current progress, identifies the challenges, and suggests future developments in the field of intelligent TBM tunnelling construction. This aims to contribute to the ongoing efforts in research and industry toward improving the safety, sustainability, and cost-effectiveness of underground excavation projects. Full article

Process monitoring and forecasting are essential to ensure the efficiency of industrial processes. Although it is possible to model processes using phenomenological approaches, these are not always easy to apply and generalize due to the complexity of the processes and the high number of unknown parameters. This work aims to present a hybrid modeling architecture that combines a phenomenological model with machine learning models. The proposal is to enable the use of simplified phenomenological models to explain the basic principles behind a phenomenon. Next, the data-oriented model corrects deviations from the simplified model predictions. The research hypothesis consists of showing the benefits of integrating prior knowledge of chemical engineering in simplifying data-based models, enhancing their generalization and improving their interpretability. The gasification process of lignin biomass with supercritical water was used as a case study for this methodology and the variable to be observed was the production of hydrogen. The real experimental data of this process were augmented using Gibbs energy minimization with the Peng–Robinson equation of state, thus generating a more voluminous database that was considered as real process data. The ideal gas model was used as a simplified model, producing significant deviations in predictions (relative deviations greater than 20%). Deviations (∆H₂ = ${\mathrm{H}}_2^{\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{l}}-{\mathrm{H}}_2^{\mathrm{p}\mathrm{r}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{c}\mathrm{t}}$) were used as the target variable for the machine learning model. Linear regression models (LASSO and simple linear regression) were used to predict ∆H₂ and this variable was added to the simplified forecast model. This consisted of the hybrid prediction of the resulting hydrogen formation (${\mathrm{H}}_2^{\mathrm{p}\mathrm{r}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{c}\mathrm{t}}$). Among the verified models, the simple linear regression adjusted better to the values of ∆H₂ (R² = 0.985) and MAE smaller than 0.1. Thus, the proposed hybrid architecture allowed for the prediction of the formation of hydrogen during the gasification process of lignin biomass, despite the thermodynamic limitations of the ideal gas model. Hybridization proved to be robust as a process monitoring tool, providing the abstraction of non-idealities of industrial processes through simple, data-oriented models, without losing predictive power. The objective of the work was fulfilled, presenting a new possibility for the monitoring of real industrial processes. Full article

The rapid expansion of the Internet of Things (IoT) and Machine Learning (ML) has significantly increased the demand for Location-Based Services (LBS) in today’s world. Among these services, indoor positioning and navigation have emerged as crucial components, driving the growth of indoor localization systems. However, using GPS in indoor environments is impractical, leading to a surge in interest in Received Signal Strength Indicator (RSSI) and machine learning-based algorithms for in-building localization and navigation in recent years. This paper aims to provide a comprehensive review of the technologies, applications, and future research directions of ML-based indoor localization for smart cities. Additionally, it examines the potential of ML algorithms in improving localization accuracy and performance in indoor environments. Full article

This paper aims to perform a literature review and statistical analysis based on data extracted from 38 articles published between 2018 and 2023 that address hybrid renewable energy systems. The main objective of this review has been to create a bibliographic database that organizes the content of the articles in different categories, such as system architecture, energy storage systems, auxiliary generation components used, and software employed, in addition to showing the algorithms and economic and reliability criteria for the optimization of these systems. In total, 38 articles have been analyzed, compared, and classified to provide an overview of the current status of simulation and optimization projects for hybrid renewable energy systems, highlighting clearly and appropriately the relevant trends and conclusions. A list of review articles has also been provided, which cover the aspects required for understanding HRESs. Full article

Waste tire rubber is one of the most concerning environmental pollution issues. With the increasing demand for automobile production, the rate of waste tire generation has also increased. However, these tires often end up stockpiled and not properly disposed of. This non-biodegradable waste poses severe fire, environmental, and health risks. Due to the progressively severe environmental problems caused by the disposal of waste tires, the feasibility of using such elastic waste materials as an alternative to natural aggregates has become a research topic. The main objective of this research is to investigate the changes in the mechanical and durability properties of concrete with the inclusion of waste tire rubber at specific contents. A total of 80 cylinders measuring 100 mm × 200 mm were cast with waste tire aggregate as a partial replacement for natural coarse aggregate (5% and 10% by weight of natural coarse aggregate). A surface treatment of tire aggregate using a cement coating was performed to study its effect on concrete properties. This research indicates a noticeable reduction in the compressive and split tensile strength of concrete containing untreated waste tire rubber compared to normal concrete made with natural aggregates. However, an improvement was observed when the surface of tire aggregates was coated with cement grout. Additionally, it was noted that the slump value, water absorption, and porosity increased as the percentage of rubber increased. Nevertheless, unlike normal concrete, the failure pattern in tire-mixed concrete occurs gently and uniformly, indicating ductile behavior. Full article

Energy harvesters are autonomous systems capable of capturing, processing, storing, and utilizing small amounts of free energy from the surrounding environment. Such energy harvesters typically involve three fundamental stages: a micro-generator or energy transducer, a voltage booster or power converter, and an energy storage component. In the case of harvesting mechanical vibrations from the environment, piezoelectric materials have been used as a transducer. For instance, PZT (lead zirconate titanate) is a widely used piezoelectric ceramic due to its high electromechanical coupling factor. However, the integration of PZT into silicon poses certain limitations, not only in the harvesting stage but also in embedding a power management electronics circuit. On the other hand, in thermoelectric (TE) energy harvesting, a recent approach involves using abundant, eco-friendly, and low-cost materials that are compatible with CMOS technology, such as silicon-based compound nanostructures for TE thin film devices. Thus, this review aims to present the current advancements in the fabrication and integration of Si-based thin-film devices for TE energy harvesting applications. Moreover, this paper also highlights some recent developments in electronic architectures that aim to enhance the overall efficiency of the complete energy harvesting system. Full article

Sol–gel coatings can provide anti-fouling and erosion resistance while being safe to use in the marine environment. MAPTMS/ZPO multilayer coatings deposited on the AA2024-T3 aluminium surface using the dip-coating method at three different thicknesses (2, 4, and 6 µm) are investigated in this work. The coatings are characterised in terms of physical and mechanical properties, and these properties are investigated in comparison to previously obtained cavitation erosion resistance levels of the coatings. Additionally, the efficiency of the coatings against biofouling was assessed using Phaeodactylum tricornutum, a marine diatom. The influence of the formation of organic–inorganic hybrid materials (OIHMs) from the prepared sols on the physical and mechanical properties of the coatings were analysed. A variety of techniques, including attenuated total reflection Fourier-transform infrared spectroscopy (ATR-FTIR), water contact angle (WCA) measurements, pencil hardness testing, cross-cut adhesion testing, a roughness profilometer, and nano-indentation, were performed on the bare and coated substrates. The results indicated that the thickness, hydrophobicity, and adherence of the coatings are strongly affected by the roughness. The elastic strain failure (H/E) and resistance to plastic deformation (H³/E²) coefficients were higher than those of the bare substrate before and after the cavitation erosion test, indicating that the coating had a higher ability to withstand deformation in comparison to the substrate alone. Furthermore, the microscopic analysis of a marine diatom, Phaeodactylum tricornutum, revealed that coated surfaces exhibited a decreased rate of bacterial adhesion and biofilm formation. The data show that sol–gel formed coatings outperform uncoated AA2024-T3 in terms of hardness, elastic strain, plastic deformation, and biofouling resistance. These characteristics are attributed to the coatings’ mechanical and adhesive capabilities, as well as their tribological behaviour. Full article

The main contribution of this paper is the assets of the current and voltage ripples in a buck converter with an LED load. The results indicate that the ripples are different and that it is possible to reduce the passive filter concerning the model of the LED as a simple resistance. The paper presents the design and simulation of a buck converter as a power supply for an LED lamp. Modeling the LED as a resistor and a voltage source (SVRM), the equations to calculate the components of the circuit using the SVRM model are presented, where the Fourier series and phasors are used to calculate the output filter. The equations are validated with SPICE simulations. The results indicate that the SVRM model for the LED load affects the calculation of the output filter of the buck converter as well as the voltage and current ripples, making it a more precise design alternative to the proposed development. Full article

When converters are connected in parallel, a system with some benefits, including modularity and redundancy, is obtained. However, in these circumstances, circulating currents can appear that produce some adverse effects. In this work, a study of the low-frequency circulating currents that appear in three-phase inverters connected in parallel is performed. The study is focused on the effects produced by the parameter mismatch, namely inductance mismatches, power imbalance, and the use of different pulse with modulation (PWM) techniques. The nature of the circulating current produced by each of these factors were analyzed separately. Both simulation and experimental results are shown, which were obtained using a three-phase 10-kW prototype composed of two 5-kW inverters connected in parallel. Full article

Vectorcardiography (VCG) is a valuable diagnostic tool that complements the standard 12-lead ECG by offering additional spatiotemporal information to clinicians. However, due to the need for additional measurement hardware and too many electrodes in a clinical scenario if performed along with a standard 12-lead, there is a need to find methods to derive the VCG from the ECG. We have evaluated the use of Long Short-term Memory (LSTM) neural networks to learn the transformation from 12-lead ECG to VCG that is applicable across subjects and for each subject. We refer to these networks as generalized and personalized, respectively. We calculated the Root Mean Square Error (RMSE), R², and Pearson correlation coefficient to compare waveforms of derived and actual VCG. We also extracted and compared diagnostic parameters from VCG, namely the QRS-loop magnitude, T-loop magnitude, and QRS-T spatial angle, from actual and derived VCGs using the Pearson correlation coefficient and Bland Altman limits of agreement. The personalized models performed better than generalized models in waveform comparisons and in the error of extracted diagnostic parameters from VCG waveforms. The use of personalized transformations for the derivation of VCG from standard 12-lead has the potential to improve and augment the diagnostic yield and accuracy of a standard 12-lead interpretation. Full article

Artificial intelligence is gaining tremendous attractiveness and showing great success in solving various problems, such as simplifying optimal control derivation. This work focuses on the application of Neuroevolution to the control of Connected and Autonomous Vehicle (CAV) cohorts operating at uncontrolled intersections. The proposed method implementation’s simplicity, thanks to the inclusion of heuristics and effective real-time performance are demonstrated. The resulting architecture achieves nearly ideal operating conditions in keeping the average speeds close to the speed limit. It achieves twice as high mean speed throughput as a controlled intersection, hence enabling lower travel time and mitigating energy inefficiencies from stop-and-go vehicle dynamics. Low deviation from the road speed limit is hence continuously sustained for cohorts of at most 50 m long. This limitation can be mitigated with additional lanes that the cohorts can split into. The concept also allows the testing and implementation of fast-turning lanes by simply replicating and reconnecting the control architecture at each new road crossing, enabling high scalability for complex road network analysis. The controller is also successfully validated within a high-fidelity vehicle dynamic environment, showing its potential for driverless vehicle control in addition to offering a new traffic control simulation model for future autonomous operation studies. Full article

While some studies have investigated the particle trajectories and stagnation points beneath solitary waves with constant vorticity, little is known about the pressure beneath such waves. To address this gap, we investigate numerically the pressure beneath solitary waves in flows with constant vorticity. Through a conformal mapping that flats the physical domain, we develop a numerical approach that allows us to compute the pressure and the velocity field in the fluid domain. Our experiments indicate that there exists a threshold vorticity such that pressure anomalies and stagnation points occur when the intensity of the vorticity is greater than this threshold. Above this threshold, the pressure on the bottom boundary has two points of local maxima and there are three stagnation points in the flow, and below it the pressure has one local maximum and there is no stagnation point. Full article

Fracture-driven interaction FDI (colloquially called “Frac-hit”) is the interference of fractures between two or more wells. This interference can have a significant impact on well production, depending on the unconventional play of interest (which can be positive or negative). In this work, the surrogate model was used along with metaheuristic optimization algorithms to optimize the completion design for a case study in the Bakken. A numerical model was built in a physics-based simulator that combines hydraulic fracturing, geomechanics, and reservoir numerical modeling as a continuous simulation. The stress was estimated using the anisotropic extended Eaton method. The fractures were calibrated using Microseismic Depletion Delineation (MDD) and microseismic events. The reservoir model was calibrated to 10 years of production data and bottom hole pressure by adjusting relative permeability curves. The stress changes due to depletion were calibrated using recorded pressure data from MDD and FDI. Once the model was calibrated, sensitivity analysis was run on the injected volumes, the number of clusters, the spacing between clusters, and the spacing between wells using Sobol and Latin Hypercube sampling. The results were used to build a surrogate model using an artificial neural network. The coefficient of correlation was in the order of 0.96 for both training and testing. The surrogate model was used to construct a net present value model for the whole system, which was then optimized using the Grey Wolf algorithm and the Particle Swarm Optimization algorithm, and the optimum design was reported. The optimum design is a combination of wider well spacing (1320 ft), tighter cluster spacing (22 ft), high injection volume (1950 STB/cluster), and a low cluster number per stage (seven clusters). This study suggests an optimum design for a horizontal well in the Bakken drilled next to a well that has been producing for ten years. The design can be deployed in new wells that are drilled next to depleted wells to optimize the system’s oil production. Full article

Most of the larger hydropower plants in Western Europe, the former Soviet Union, North America and Japan were constructed between the 1940s and 1970s. This implies that the rehabilitation or repair of existing dams is a top priority, which entails new challenges for the dam engineering community. Since no two dams are the same, in cases in which abnormal behavior is suspected, an in-depth diagnosis of the state of the dam to define the causes and consequences of the damage is required. To illustrate the diagnostic process, an old concrete arch dam is presented which showed signs of reservoir water seepage through some construction joints, resulting in a buildup of calcium carbonate on the downstream face. After analyzing the available data, we put forward a hypothesis that the high temperature gradient promoted the opening of some construction joints on the upstream face during the first filling of the reservoir. Over time, water penetration expanded the cracks, reaching the downstream face. To prove our diagnosis, a chemo-thermo-mechanical finite element analysis was carried out in order to simulate the behavior of the dam during its construction and initial impoundment. Full article