Search Results (53)

In today’s business environment, emails are essential across all sectors, including finance and academia. There are two main types of emails: ham (legitimate) and spam (unsolicited). Spam wastes consumers’ time and resources and poses risks to sensitive data, with volumes doubling daily. Current spam identification methods, such as Blocklist approaches and content-based techniques, have limitations, highlighting the need for more effective solutions. These constraints call for detailed and more accurate approaches, such as machine learning (ML) and deep learning (DL), for realistic detection of new scams. Emphasis has since been placed on the possibility that ML and DL technologies are present in detecting email spam. In this work, we have succeeded in developing a hybrid deep learning model, where Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU) are applied distinctly to identify spam email. Despite the fact that the other models have been applied independently (CNNs, LSTM, GRU, or ensemble machine learning classifier) in previous studies, the given research has provided a contribution to the existing body of literature since it has managed to combine the advantage of LSTM in capturing the long-term dependency and the effectiveness of GRU in terms of computational efficiency. In this hybridization, we have addressed key issues such as the vanishing gradient problem and outrageous resource consumption that are usually encountered in applying standalone deep learning. Moreover, our proposed model is superior regarding the detection accuracy (90%) and AUC (98.99%). Though Transformer-based models are significantly lighter and can be used in real-time applications, they require extensive computation resources. The proposed work presents a substantive and scalable foundation to spam detection that is technically and practically dissimilar to the familiar approaches due to the powerful preprocessing steps, including particular stop-word removal, TF-IDF vectorization, and model testing on large, real-world size dataset (Enron-Spam). Additionally, delays in the feature comparison technique within the model minimize false positives and false negatives. Full article

Multi-source systems are gaining attention as an effective approach to seamlessly incorporate renewable energies within electrical networks. These systems offer greater flexibility and better energy management possibilities. The considered multi-source system is based on a 50 MW wind farm connected to battery energy storage and electrolyzers through modular multi-level DC/DC converters. Wind energy systems interface with the DC-bus via rectifier power electronics that regulate the DC-bus voltage and implement optimal power extraction algorithms for efficient wind turbine operation. However, integrating intermittent renewable energy sources with optimal microgrid management poses significant challenges. It is essential to mention that the studied multi-source system is connected to the DC loads (modular electrolyzers and local load). This work proposes a new regulation method designed specifically to improve the performance of the system. In this strategy, the excess wind farm energy is converted into hydrogen gas and may be stored in the batteries. On the other hand, when the wind speed is low or there is no excess of energy, electrolyzer operations are stopped. The battery energy management depends on the power balance between the DC load (modular electrolyzers and local load) requirements and the energy produced from the wind farm. This control should lead to eliminating the fluctuations in energy production and should have a high dynamic performance. This work presents a nonlinear control method using a backstepping concept to improve the performances of the system operations and to achieve the mentioned goals. To evaluate the developed control strategy, some simulations based on real meteorological wind speed data using Matlab are conducted. The simulation results show that the proposed backstepping control strategy is satisfactory. Indeed, by integrating this control strategy into the multi-source system, we offer a flexible solution for battery and electrolyzer applications, contributing to the transition to a cleaner, more resilient energy system. This methodology offers intelligent and efficient energy management. Full article

The paper analyzes the transport capabilities of energy-efficient resonant conveyors, with a particular emphasis on their dosing capabilities. They are driven by an additional mass—acting as a resonator—using a relatively small vibrator whose forcing power constitutes about 20% of the force that would be needed to drive a similar conveyor of classical construction and the same transport capacity, resulting in lower energy demand. These conveyors have been present since the 1950s, but their widespread use occurred with the proliferation of cheap and easily controllable frequency inverters. In the paper, using a relatively simple model that allowed for the determination of amplitude–frequency characteristics and the dependence of transport speed on the forcing frequency, the impact of the resonator mass value on the device’s operation was shown. It was demonstrated that the value of this mass should be similar to the mass of the transporting trough, which increases the durability of the drive as well as the durability of the suspension between the trough and the resonator. A larger resonator mass also positively affects the dosing capabilities of the device and its energy efficiency during the dosing process with frequent transport stops. Full article

The increased interest in artificial intelligence in manufacturing has driven the adoption of machine learning to optimize processes and improve efficiency. A key challenge in injection molding is the variability of recycled materials, which affects part quality and processing stability. This study presents a novel closed-loop process control approach for injection molding, leveraging machine learning to adaptively predict processing inputs and quality outcomes. The methodology was tested on five blends of recycled polypropylene (rPP), using artificial neural networks (ANNs), linear regression, and polynomial regression to model the relationships between material properties and process parameters. The dataset was split 80/20 into training and testing sets. The ANN model was implemented using TensorFlow and Keras, with six hidden layers of 32 neurons per layer, ReLU activation, and an Adam optimizer. Empirical tuning and early stopping were used to optimize performance and prevent overfitting. Predictions were evaluated based on mean absolute error (MAE), mean squared error (MSE), and percentage error. The results showed that yield stress, ultimate elongation, and part weight were accurately predicted within a 5% error for linear and polynomial regression models and within a 10% error for the ANN. However, modulus predictions were less reliable, with errors of ~11% for ANN and linear regression and ~40% for polynomial regression, reflecting the inherent variability of this property in rPP blends. Predictions of processing inputs had errors ranging from 3% to 25%, depending on the model and response variable. No single modeling approach was consistently superior across all responses, highlighting the complexity of the relationship between material properties, process parameters, and quality metrics. Overall, the work demonstrates that closed-loop process control, powered by machine learning, can effectively predict key quality parameters in injection molding of recycled materials. The proposed approach can improve process stability and material utilization, facilitating increased adoption of sustainable materials. Full article

Efflorescence, a time-dependent and water-driven phenomenon, is a major concern in alkali-activated materials (AAMs), impacting their practical use and preservation in a time-frozen state for post-characterisation. Although a method for stopping chemical reactions in conventional cements exists, it is time-consuming and not chemical-free. Therefore, this study explored the effects of low-power microwave-induced dehydration on efflorescence, mechanical performance, and structural integrity in AAMs, to create an alternative and more “user-friendly” dehydration method. For this purpose, several mixtures based on secondary raw (slag, fly ash, glass wool, and rock wool) and non-waste (metakaolin) materials were activated with a commercial Na-silicate solution in ratios that promoted or prevented efflorescence. Characterisation techniques, including Fourier-transform infrared spectroscopy and X-ray diffraction, showed that microwave dehydration effectively removed water without altering crystallinity, while mercury intrusion porosimetry and compressive strength tests confirmed increased porosity. In addition to being an efficient, time-saving, and solvent-free manner of stopping the reactions in AAMs, microwave irradiation emerged as an innovative, chemical-free method for evaluating curing finalisation and engineering foams in a stage when all other existing methods fail. However, the artificially provoked efflorescence in aged dehydrated AAMs connected the slipperiness of AAM with the instant extraction of Na, which raised the need for further research into alternative alkali replacements to evaluate the practical use of AAM. Full article

Thermal comfort is a key determinant ruling the quality of urban park visits that is mainly evaluated by equivalent meteorological factors and lacks evidence about its relationship with emotional perception. Exposure to green space was believed to be an available approach to increase thermal comfort, but this argument still needs verification to confirm its reliability. In this study, about ~15,000 streetscapes were photographed at stops along sidewalks and evaluated for green view index (GVI) and plant diversity index in five urban parks of Changchun, Northeast China. The faces of visitors were captured to analyze happy, sad, and neutral scores as well as two net positive emotion estimates. Meteorological factors of temperature, relative humidity, and wind velocity were measured at the same time for evaluating thermal comfort using equivalent variables of discomfort index (DI), temperature and humidity index (THI), and cooling power index (CP). At stops with higher GVI, lower temperature (slope: from −0.1058 to −0.0871) and wind velocity (slope: from −0.1273 to −0.0524) were found, as well as higher relative humidity (slope: from 0.0871 to 0.8812), which resulted in positive relationships between GVI and thermal comfort evaluated as DI (R² = 0.3598, p < 0.0001) or CP (R² = 0.3179, p < 0.0001). Sad score was positively correlated with THI (R² = 0.0908, p = 0.0332) and negatively correlated with CP (R² = 0.0929, p = 0.0294). At stops with high GVI, more positive emotions were shown on visitors’ faces (happy minus sad scores, 0.31 ± 0.10). Plant diversity had varied relationships with GVI in parks depending on age. Overall, our study demonstrated that using imagery data extracted from streetscapes can be useful for evaluating thermal comfort. It is recommended to plan a large amount of touchable nature provided by vegetation in urban parks so as to mitigate micro-climates towards a trend with more thermal comfort that evokes more positive emotions. Full article

A major issue with spark-ignition engines is their fuel inefficiency and negative environmental effects, especially in urban driving situations. This topic is of utmost importance considering the increasing apprehension over environmental contamination and the need for enhanced energy efficiency. The research’s originality resides in its strategy to tackling this issue without necessitating intricate engine changes, a manner not commonly used. The research uses a dual strategy that integrates both theoretical and experimental approaches. The theoretical component entails developing models to forecast the effects of different cylinder deactivation strategies on fuel consumption and emissions. Important factors to address in this theoretical model are the introduction of air into cylinders that are not in use and the stopping of fuel supply. The experimental component involves conducting bench experiments on a modified spark-ignition engine to verify the theoretical conclusions. These tests assess performance metrics, such as fuel economy and environmental effect, under different load situations. The study’s findings are encouraging. The study reveals that disabling a specific group of cylinders while permitting unrestricted air intake might result in significant improvements in fuel economy, anywhere from 1.5% to 10.5%, depending on the engine’s workload. Bench testing revealed a maximum improvement of 10.8%, which demonstrates the efficacy of this strategy. The study’s findings indicate that the use of the integrated power regulation approach greatly improves fuel efficiency and decreases the impact of the environmental consequences of spark-ignition engines, especially in situations of low load and idling. This technology demonstrates its feasibility as a solution that can be seamlessly incorporated into current engine designs with few adjustments, providing a practical and environmentally responsible option for enhancing vehicle performance. The results indicate that this approach has wide-ranging potential uses in the automotive sector, particularly for urban cars that often function in situations with modest levels of demand. By using this approach, manufacturers may attain enhanced fuel efficiency and diminish emissions, this contributing to the development of more sustainable urban transportation options. Full article

Growing concerns over resource depletion and air pollution driven by the rising dependence on fossil fuels necessitate the exploration of alternative energy sources. This study investigates the performance and emission characteristics of a diesel engine fueled by biodiesel blends (B10 and B20) derived from castor and corn feedstocks under low-load conditions (idle and minimal accessory loads). We compare the impact of these biofuels on engine power, fuel consumption, and exhaust emissions relative to conventional diesel, particularly in scenarios mimicking real-world traffic congestion and vehicle stops. The findings suggest that biodiesel offers environmental benefits by reducing harmful pollutants like carbon monoxide (CO) and particulate matter (PM) during engine idling and low-load operation. However, replacing diesel with biodiesel requires further research to address potential drawbacks like increased NO_x emissions and lower thermal efficiency. While a higher fuel consumption with biodiesel may occur due to its lower calorific value, the overall benefit of reduced contaminant emissions makes it a promising alternative fuel. Full article

Wood fuel from the agroforestry sector is one of the main strategies cited by the EU for reducing energetic dependance on foreign markets. Its sustainability, both economic and environmental, can be improved through the optimization of harvesting and chipping operations. This study was focused on the dynamic and energetic balance of the chipping phase carried out by a chipper operated by the power-take-off (PTO) of a medium-power tractor. Both machines were equipped with sensors for real-time monitoring of fuel consumption, PTO torque and speed, trunk diameter and working time during the comminution of 61 poplar trees grown in a medium rotation coppice system. The data analysis was carried out on the entire dataset (about 29,000 records) without considering their belonging to different trees. By means of proper data ordinations, it has been possible to define all the intervals in which the chipping stopped (e.g., between two trees) and to exclude them from the intervals of actual chipping. This has allowed forcomputation of operative and actual working time, as well as of the basic power required to operate the chipper and the power for actual chipping. Subsequently, the parameter values observed during actual chipping were related to the cutting diameters measured at the same instant. Subsequently, the dataset was divided according to seven diameter classes, and, for each class, the descriptive statistical indices of working time, work productivity, CO₂ emissions, energy requirement and fuel consumption were calculated. Eventually, the correlation between the variations in trunk diameter and other parameters was verified both on the whole dataset and based on the class average values. The analysis made it possible to identify the conditions of greatest efficiency for the chipper. More generally, the method could help to increase the accuracy of measurements aimed at characterizing the performance of chippers from the point of view of dynamic energy requirements as well as in relation to different wood species. Full article

Waste heat recovery from exhaust gas is one of the most convenient methods to save energy in internal combustion engine-driven vehicles. This paper aims to investigate a reduction in waste heat from the exhaust gas of an internal combustion engine of a serial Diesel–electric hybrid bus by recovering part of the heat and converting it into useful power with the help of a split-flow supercritical CO₂ (sCO₂) recompression Brayton cycle. It can recover 17.01 kW of the total 33.47 kW of waste heat contained in exhaust gas from a 151 kW internal combustion engine. The thermal efficiency of the cycle is 38.51%, and the net power of the cycle is 6.55 kW. The variation in the sCO₂ temperature at the shutdown of the internal combustion engine is analyzed, and a slow drop followed by a sudden and then a slow drop is observed. After 80 s from stopping the engine, the temperature drops by (23–33)% depending on the tube thickness of the recovery heat exchanger. The performances (net power, thermal efficiency, and waste heat recovery efficiency) of the split-flow sCO₂ recompression Brayton cycle are clearly superior to those of the steam Rankine cycle and the organic Rankine cycle (ORC) with cyclopentane as a working fluid. Full article

Feeding is a critical process in aquaculture, as it has a direct impact on the quantity and quality of fish. With advances in convolutional neural network (CNN) and vision transformer (ViT), intelligent feeding has been widely adopted in aquaculture, as the real-time monitoring of fish behavior can lead to better feeding decisions. However, existing models still have the problem of insufficient accuracy in the fish behavior-recognition task. In this study, the largemouth bass (Micropterus salmoides) was selected as the research subject, and three categories (weakly, moderately, and strongly hungry) were defined. We applied the deformable attention to the vision transformer (DeformAtt-ViT) to identify the fish hunger degree. The deformable attention module was extremely powerful in feature extraction because it improved the fixed geometric structure of the receptive fields with data-dependent sparse attention, thereby guiding the model to focus on more important regions. In the experiment, the proposed DeformAtt-ViT was compared with the state-of-the-art transformers. Among them, DeformAtt-ViT achieved optimal performance in terms of accuracy, F1-score, recall, and precision at 95.50%, 94.13%, 95.87%, and 92.45%, respectively. Moreover, a comparative evaluation between DeformAtt-ViT and CNNs was conducted, and DeformAtt-ViT still dominated the others. We further visualized the important pixels that contributed the most to the classification result, enabling the interpretability of the model. As a prerequisite for determining the feed time, the proposed DeformAtt-ViT could identify the aggregation level of the fish and then trigger the feeding machine to be turned on. Also, the feeding machine will stop working when the aggregation disappears. Conclusively, this study was of great significance, as it explored the field of intelligent feeding in aquaculture, enabling precise feeding at a proper time. Full article

Advances in information and signal processing, driven by artificial intelligence techniques and recent breakthroughs in deep learning, have significantly impacted autonomous driving by enhancing safety and reducing the dependence on human intervention. Generally, prevailing ADASs (advanced driver assistance systems) incorporate costly components, making them financially unattainable for a substantial portion of the population. This paper proposes a solution: an embedded system designed for real-time pedestrian and priority sign detection, offering affordability and universal applicability across various vehicles. The suggested system, which comprises two cameras, an NVIDIA Jetson Nano B01 low-power edge device and an LCD (liquid crystal system) display, ensures seamless integration into a vehicle without occupying substantial space and provides a cost-effective alternative. The primary focus of this research is addressing accidents caused by the failure to yield priority to other drivers or pedestrians. Our study stands out from existing research by concurrently addressing traffic sign recognition and pedestrian detection, concentrating on identifying five crucial objects: pedestrians, pedestrian crossings (signs and road paintings separately), stop signs, and give way signs. Object detection was executed using a lightweight, custom-trained CNN (convolutional neural network) known as SSD (Single Shot Detector)-MobileNet, implemented on the Jetson Nano. To tailor the model for this specific application, the pre-trained neural network underwent training on our custom dataset consisting of images captured on the road under diverse lighting and traffic conditions. The outcomes of the proposed system offer promising results, positioning it as a viable candidate for real-time implementation; its contributions are noteworthy in advancing the safety and accessibility of autonomous driving technologies. Full article

The rapid advancement of mobile laser scanner technology used for terrain mapping, among other things, imposes increasing requirements for scanning frequency and distance measurement accuracy. To meet these requirements, rangefinder modules are expected to operate with high echo signal dynamics and to allow accurate distance measurement even based on single-laser-pulse echo detection. Such performance can be potentially achieved using pulsed time-of-flight (ToF) laser rangefinders (LRF). In conventional ToF modules, however, the STOP signal (for time counter interruption) is generated using a straightforward fixed-threshold comparator method. Unfortunately, it corresponds to the so-called walk error, i.e., the dependence of the measured time of flight on the magnitude of the echo signal. In most ranging applications, however, the LRF detection channel can be exposed to an extremely large span of received echo power levels, which depend on the distance measured, type of target surface, atmospheric transmission, etc. Thus, the walk error is an inseparable element of the conventional ToF technique and creates a fundamental limit for its precision. This article presents a novel method of walk error compensation in real time. By using our authorial electronic circuit for measuring the magnitude of the echo signal, it is possible to effectively compensate for the walk error even when the echo signal brings the detection channel amplifiers into saturation. In addition, the paper presents a laboratory method for calibrating the walk error compensation curve. Full article

Ion bombardment is an important tool of materials processing, but usually leads to erosion of the surface and significant thickness reductions when thin layers are used. The growing use of polymer thin films in a variety of applications, from coatings and membranes to biomedical and electronic devices, calls for a deeper understanding of the thinning process induced by energetic ions espe-cially for very thin films. Here, thinning and surface morphology changes induced by high-energy ion bombardment in PMMA and PVC thin films were investigated, focusing on the role of the initial thickness of the films and the stopping power of the ions. We used thin films with initial thicknesses varying from 13 to 800 nm, and light and heavy ions as projectiles in the energy range of 2–2000 MeV, where the electronic stopping dominates. Thickness reductions as a function of fluence were monitored and thinning cross sections were extracted from curves. A supralinear scaling between the thinning cross sections and the electronic stopping power of the beams was observed, with a much enhanced thinning efficiency for the swift heavy ions. The scaling with the stopping power dE/dx is almost independent of the initial thickness of the films. At intermediate and large fluences, changes in the physicochemical properties of the irradiated polymers may modulate and decelerate the thinning process of the remaining film. The importance of this secondary process depends on the stopping power and the balance between erosion and the chemical transformations induced by the beam. We also observe a trend for the thinning efficiency to become larger in very thin films. Depending on the type of beam and polymer, this effect is more or less pronounced. PMMA films irradiated with 2 MeV H⁺ show the most systematic correlation between initial thickness and thinning cross sections, while in PVC films the initial thickness plays a minor role for all investigated beams. Full article

The cyclic appearance and disappearance of nucleation was observed in the boundary layer of drops of 1,3-propanediol, 1,2-propanediol, and glycerol, close to the boiling point and exposed to a cooler airflow. Although continuous nucleation has previously been widely observed, the cyclic nature of the phenomenon observed here is unusual. It was observed in experiments with free-falling drops and fixed drops in an upflow of air. To investigate this unexpected phenomenon further, the phenomenon was reproduced in two finite volume models. The first model used 1D potential flow solutions to approximate the airflow around the spherical windward face of the droplet. The second model used CFD to model the airflow. Both models used classical nucleation theory, the Stefan–Fuchs model of droplet growth by condensation, mass transfer by evaporation, diffusion, convection, and heat transfer by diffusion and convection. Despite several simplifications, the most important being the assumption that the drop has a uniform temperature, both models predict the frequency of nucleation to be better than the order of magnitude. These models also predict the experimentally observed power law dependence of nucleation frequency on air speed. It is proposed that the cyclic nature of the phenomenon is caused by the following process: the depletion of condensable vapour around the freshly nucleated aerosol due to condensation onto the aerosol results in reduced supersaturation, which stops further nucleation, and then the replenishment of this vapour by diffusion and convection from the parent drop, with nucleation of aerosol recommencing when the supersaturation has recovered sufficiently—then, the repetition of these steps in a cycle. It is proposed that the process depends mostly on the maximum saturation ratio in the boundary layer, which itself is determined by four key dimensionless numbers: the Lewis number, the Peclet number, the Reynolds number, and the ratio of the vapour pressure of the condensable compound at drop surface temperature to the vapour pressure of the same species at ambient temperature. A practical application of the phenomenon may be as a means of validation of thermo-fluid models, which include nucleation. Full article