Applied Sciences

Hydrogen fuel cell electric vehicles are emerging as a means of transportation using renewable and carbon-free energy due to global warming and air pollution. Hydrogen fuel cell electric vehicles are typically refueled at a wide range of temperatures (−40 °C to 85 °C) in hydrogen refueling stations in accordance with globally accepted standards. Currently, there is no traceable method by which to verify and calibrate the Coriolis mass flowmeters used at hydrogen refueling stations, except for a water calibration process as a conventional method for mass flowrate calibration. To verify the hydrogen flow metering to a suitable level of accuracy under the challenging condition of high pressures and a wide range of temperatures, necessary methodologies and calibration facilities are developed in the present study. A flow measurement characteristic test of the hydrogen mass flowmeter under identical density conditions of the refueled hydrogen was conducted using the high-pressure gas flow standard system of the Korea Research Institute of Standards and Science to assess the effects on the medium and pressure of the mass flowmeter in a density-matching approach. To investigate the pressure dependence of the mass flowmeter at a hydrogen refueling station, a high-pressure water flow test was conducted in the pressure range of 2 bar to 700 bar, which is a pressure-matching approach. Finally, the KRISS Hydrogen Field Test Standard based on the gravimetric principle was developed to verify the measurement accuracy of the mass flowmeter to be used at hydrogen refueling stations for the first time in Korea. Full article

Natural resources are becoming increasingly scarce, and the need to control their consumption and recycle their use is growing. Water is one of the essential resources for human survival. Therefore, there has been an increasing interest in ways to save, recycle and treat water supplies. Aquaculture is one of the most polluting activities as it produces a significant wastewater volume, which needs proper treatment before being discharged into the environment or recycled. Microalgae are a potential solution for wastewater treatment. Due to their numerous advantages, the use of microalgal biomass is being studied, and, at present, there is already a market and room for profit in the sale of microalgal components in various forms, such as animal and human supplements. From a biorefinery point of view, it is important to take advantage of all the qualities and benefits that microalgae have by combining their great capacity to treat wastewater and exploit the produced biomass, analysing its composition for subsequent valorisation, for example. In this study, Chlorella vulgaris was used to treat aquaculture wastewater from a trout farm aquaculture facility, and the treatment efficiency was evaluated. To valorise the resulting biomass, its composition was also assessed. C. vulgaris successfully grew in the effluent with growth rates of 0.260 ± 0.014 d⁻¹ and with average productivity of 32.9 ± 1.6 mg L⁻¹ d⁻¹. The achieved removal efficiencies were 93.5 ± 2.1% for total nitrogen, 98.0 ± 0.1% for nitrate-nitrogen and 92.7 ± 0.1% for phosphate-phosphorus. Concerning biomass composition, the lipids (15.82 ± 0.15%), carbohydrates (48.64 ± 0.83%), and pigment contents (0.99 ± 0.04% for chlorophyll a + b and 0.21 ± 0.04% for carotenoids) were similar to the values of similar studies. However, the protein content obtained (17.93 ± 1.21%) was lower than the ones mentioned in the literature. Full article

Probiotics are commonly added to yogurt to provide many health benefits for the consumer. A description is provided for some commonly used probiotics in yogurt. A GRAS (generally recognized as safe) list of probiotic bacteria that can be added to yogurt or similar types of products is provided. Additionally, prebiotics, synbiotics (combination of prebiotics and probiotics), postbiotics, paraprobiotics, and psychobiotics can be added to yogurt. Probiotic yogurt can come in various forms in addition to spoonable yogurt, and yogurt can be used as an ingredient in other food products. Many useful functional ingredients can be applied to probiotic yogurt. The safety of probiotics must be addressed, especially for critically ill patients and other susceptible populations. Probiotics must survive within yogurt throughout its entire shelf-life and within the gastrointestinal tract after consumption by the consumer to provide health benefits, and many techniques can be used to maintain survival of probiotics in yogurt. Furthermore, probiotics can be added to Greek yogurt acid whey. Many opportunities exist for adding a wide variety of probiotics to a wide variety of yogurt-based products. Full article

Diamond grinding wheels (DGWs) have a central role in cutting-edge industries such as aeronautics or defense and spatial applications. Characterizations of DGWs are essential to optimize the design and machining performance of such cutting tools. Thus, the critical issue of DGW characterization lies in the detection of diamond grits. However, the traditional diamond detection methods rely on manual operations on DGW images. These methods are time-consuming, error-prone and inaccurate. In addition, the manual detection of diamond grits remains challenging even for a subject expert. To overcome these shortcomings, we introduce a deep learning approach for automatic diamond grit segmentation. Due to our small dataset of 153 images, the proposed approach leverages transfer learning techniques with pre-trained ResNet34 as an encoder of U-Net CNN architecture. Moreover, with more than 8600 hyperparameter combinations in our model, manually finding the best configuration is impossible. That is why we use a Bayesian optimization algorithm using Hyperband early stopping mechanisms to automatically explore the search space and find the best hyperparameter values. Moreover, considering our small dataset, we obtain overall satisfactory performance with over 53% IoU and 69% F1-score. Finally, this work provides a first step toward diamond grinding wheel characterization by using a data-driven approach for automatic semantic segmentation of diamond grits. Full article

A reasonable braking timing sequence plays an important role in the braking stability of a semi-trailer train, but there is still a lack of objective and comprehensive scientific evaluations of braking stability as shown by the braking timing sequence. Aiming at this problem, through the analysis of relevant regulations and standards at home and abroad, an evaluation index hierarchy model of the semi-trailer train braking timing sequence is constructed. The fuzzy analytic hierarchy process is used to determine the weight of the evaluation index of each level of the braking timing sequence, and a comprehensive evaluation is obtained. In order to determine the optimal braking timing sequence of the train during braking, a simulation model was established and simulated. Through the combination of index weight and simulation data, the model can provide a theoretical basis for the subsequent study of the optimal braking timing sequence of semi-trailer trains running under different working conditions. Full article

With the promotion of artificial intelligence in agriculture and the popularization of plug tray seedling-raising technology, seedling raising and transplanting have become the most popular planting modes. Miss-seeding is one of the most serious problems affecting seedling raising and transplanting. It not only affects the germination rate of seeds but also reduces the utilization rate of the plug tray. The experimental analysis of traditional machine vision-based miss-seeding showed that because of uneven lighting, the plug tray was wrongly identified as a seed under bright light, but the seeds in the dark were not easy to identify. When using the seeding area to identify seeds and noise, sweet corn seeds in a small area can be easily screened out. This paper proposes a method using the ResNet network with an attention mechanism to solve the above-mentioned problems. In this paper, the captured image was segmented into the images of a single plug tray, and a residual attention network was built; the detection scheme of miss-seeding was also converted into a dichotomous picture recognition task. This paper demonstrates that the residual attention network can effectively recognize and detect the seed images of sweet corn with very high accuracy. The results of the experiment showed that the average accuracy of this recognition model was 98%. The feature visualization method was used to analyze the features, further proving the effectiveness of the classification method of plug tray seedlings. Full article

Many diet trends have emerged over the last few years, including plant-based diets with soya as an important component, while fermented milk beverages have been an integral part of the human diet for thousands of years. However, there is little research on the potential of using propionic bacteria for dairy or plant-based food fermentation. The aim of this study was to evaluate the effects of soy beverage addition and propionic bacterium application on the quality of dairy yoghurt products. Three variants of the products—based on cow’s milk, soya beverages, and mixtures of both—were prepared and then fermented with yoghurt bacteria, propionic bacteria or a mixture thereof. It was found that it is possible to obtain functional yoghurt products based on cow’s milk, as well as milk–soya blends, using a mixture of yoghurt and propionic cultures. The resulting milk, milk–soya, and soya yoghurt products displayed pH values in the correct range and a stable bacterial population during refrigerated storage for 21 days. The refrigerated storage time and the type of culture starter used significantly determined the quality characteristics of the milk, milk–soya and soya yoghurt products, such as their firmness, adhesiveness, and water-holding capacity. Full article

Water temperature in lakes is their basic property that determines many processes. Changes in average values are thoroughly investigated using a long-term approach. Knowledge of extreme situations such as temperature minimums is still insufficient. This paper analyses changes in the minimum temperature in 10 lakes in Poland in the period 1972–2021. The obtained results show variability over the course of the parameter, both at the annual and monthly scale. In the first case, half of the analysed set showed statistically significant increasing trends (on average 0.10 °C per decade). In the latter case, the greatest changes occurred in the months of the warm half-year (on average 0.57 °C per decade). The reported situation is caused by the individual conditions of particular lakes, i.e., their location, morphometric parameters, or the dynamics of the occurrence of ice phenomena. A successive increase in the minimum temperature in lakes has its consequences for biotic and abiotic processes. Exceeding specific thresholds results in the evident transformation of these ecosystems. Full article

In the future, many activities will be carried out in the Metaverse: hybrid offices and video-based education are just some examples. The way research is carried out could change, too. In this context, this work investigates the possibility of simulating Augmented Reality (AR) user studies on information presentation in a virtual environment. Organizing an industrial setup is complex; thus, most studies are executed in laboratories. However, lab experiments present limitations, e.g., the number and variety of participants and the availability of facilities. User studies may also be carried out by exploiting simulated AR, as an initial step for the Metaverse, where people are connected regardless of their location. This alternative could be used to carry out experiments on AR information presentation to solve common issues, such as the lack of physical equipment to perform component location tasks and the long time required to collect a large sample of users. Indeed, researchers could propose CAD models with information that simulates the same visual realism achieved with true AR. Moreover, multiple tests could be conducted in parallel by not relying on a limited amount of physical equipment per user. In this work, we developed and evaluated a desktop-simulated testing environment (DSTE) to conduct AR information presentation experiments remotely. We applied it in a pilot user study, revealing that the proposed DSTE was effective for the related research goals. Furthermore, 40 participants reported a positive user experience. The evaluation confirms that using a DSTE is promising for collecting and analyzing data from a wide range of people. Full article

The extraction of quantitative medical imaging features, or radiomics, has been an exponentially growing research field in recent decades. Nonetheless, more studies are investigating the limitations of the quantitative imaging features, especially the reproducibility of RFs across different scanning settings. In this experiment, we investigate the reproducibility of renal cell carcinoma (RCC) RFs between the non-contrast, arterial, and late phases contrast-enhanced computed tomography (CE-CT) scans; and the ability of ComBat technique to harmonize these RFs. In addition, we assessed the predictive performance of the RFs extracted from the different phases. A total of 69 CECT scans with the three different phases were analyzed. Original RFs were extracted from the segmented lesions on each phase using Pyradiomics toolbox. The agreement in RF values before and after harmonization was evaluated with the concordance correlation coefficient (CCC). Our results show that most RFs are not reproducible across different imaging phases. In addition, ComBat harmonization did not significantly increase the number of reproducible RFs in any of the three scenarios. Furthermore, RFs extracted from the arterial phase were, on average, the most predictive of overall survival in RCC patients. The findings can guide the analysis of retrospective RCC heterogeneous data acquired in different phases and add to the call for radiomics-specific harmonization techniques. Full article

In this work, the feasibility of a personalized therapy design is considered. We attempt to determine whether all of the obtained results of computer simulations should be presented to medical personnel. For this purpose, a two-drug displacement problem was used, which is the starting point of this research work. The relationships that can be used to characterize the progress as well as the efficiency of treatment in advanced cases can be modeled by a system of nonlinear dynamical equations with additional algebraic dependencies (differential-algebraic equations, DAEs). Then, to improve the efficiency of the therapy, an optimization task needs to be formulated and solved. The solution should meet all the assumed requirements and expectations. Therefore, a control vector parametrization (CVP) procedure for a DAE model is often suggested as an appropriate tool for solving the optimization-based therapy design tasks. In this work, a general iterative optimization framework is discussed in detail together with the proposed three levels of solution feasibility which try to decide if the iteratively obtained solution is trustworthy. The CVP optimization procedure with the designed levels of solution feasibility are implemented and tested. The obtained results are discussed from the perspective of their practical use in the treatment process. It is worth noting that solutions that are valuable from the perspective of creating new optimization algorithms may be rejected by the final recipient as devoid of application possibilities. Some of the presented solutions can be considered as a reference in further clinical research. Full article

This paper describes an experimental study on the instability and atomization of liquid cylinders after the impact of shock waves. Single row water column, in-line double rows water columns and alongside triple rows water columns were evaluated in a horizontal shock tube. The diameter of water column and the Mach number in the experiments were 2.0–4.14 mm and 1.10–1.25, respectively. The global instability along the axial direction of water cylinders was focused. Using a high-speed camera, the developments of spike height, bubble depth and turbulent mixing zone, width were measured. Some comparison was also made between the present experimental results and the existing theoretical model. Full article

In multi-source camera collaborative imaging research, it is known that the differences in size and resolution of the sensor chip, the angle of view and field of view when imaging, and the imaging characteristics of optical systems between cameras, makes image registration a topic that can never be avoided in data analysis and post-processing. Additionally, lacking common features between multi-source images means that the accurate registration of multi-modal images can only be completed manually. Aiming at the registration problem of the polarization parameter image and infrared image, this study takes advantage of the invariant feature of the imaging target topology and introduces the image texture-based segmentation method to obtain the target topology structure. Subsequently, the registration control points are extracted based on the target topology skeleton, which can break through the limitation of feature differences, improve the robustness of the algorithm to target transformation, and realize the automatic registration of multi-source images. Full article

Geomaterial mechanical parameters are critical to implementing construction design and evaluating stability through feedback analysis in geotechnical engineering. The back analysis is widely utilized to identify and calibrate the geomaterial mechanical properties in geotechnical engineering. This study developed a novel back-analysis framework by combining a reduced-order model (ROM), grey wolf optimization (GWO), and numerical technology. The ROM was adopted to evaluate the response of the geotechnical structure based on a numerical model. GWO was used to search and identify the geomaterials properties based on the ROM. The developed back analysis framework was applied to a circular tunnel and a practical tunnel for determining the mechanical property of the surrounding rock mass. The results showed that the ROM could be an excellent surrogated model and replaced it with the numerical model. The obtained geomaterial properties were in excellent agreement with the actual properties. The deformation behavior captured by the developed framework was consistent with the theoretical solution in a circular rock tunnel. The developed framework provides a practical, accurate, and convenient approach for calibrating the geomaterial properties based on field monitoring data in practical geotechnical engineering applications. Full article

Recently, open-source repositories have grown rapidly due to volunteer contributions worldwide. Collaboration software platforms have gained popularity as thousands of external contributors have contributed to open-source repositories. Although data de-duplication decreases the size of backup workloads, this causes poor data locality (fragmentation) and redundant review time and effort. Deep learning and machine learning techniques have recently been applied to identify complex bugs and duplicate issue reports. It is difficult to use, but it increases the risk of developers submitting duplicate pull requests, resulting in additional maintenance costs. We propose a hybrid deep learning technique in this work on the basis of an optimal duplication detection is for pull requests (HDL-ODPRs) in open-source repositories. An algorithm used to extract textual data from pull requests is hybrid leader-based optimization (HLBO), which increases the accuracy of duplicate detection. Following that, we compute the similarities between pull requests by utilizing the multiobjective alpine skiing optimization (MASO) algorithm, which provides textual, file-change, and code-change similarities. For pull request duplicate detection, a hybrid deep learning technique (named GAN-GS) is introduced, in which the global search (GS) algorithm is used to optimize the design metrics of the generative adversarial network (GAN). The proposed HDL-ODPR model is validated against the public standard benchmark datasets, such as DupPR-basic and DupPR-complementary data. According to the simulation results, the proposed HDL-ODPR model can achieve promising results in comparison with existing state-of-the-art models. Full article

The use of the unsupervised monocular depth estimation network approach has seen rapid progress in recent years, as it avoids the use of ground truth data, and also because monocular cameras are readily available in most autonomous devices. Although some effective monocular depth estimation networks have been reported previously, such as Monodepth2 and SC-SfMLearner, most of these approaches are still computationally expensive for lightweight devices. Therefore, in this paper, we introduced a knowledge-distillation-based approach named LUMDE, to deal with the pixel-by-pixel unsupervised monocular depth estimation task. Specifically, we use a teacher network and lightweight student network to distill the depth information, and further, integrate a pose network into the student module to improve the depth performance. Moreover, referring to the idea of the Generative Adversarial Network (GAN), the outputs of the student network and teacher network are taken as fake and real samples, respectively, and Transformer is introduced as the discriminator of GAN to further improve the depth prediction results. The proposed LUMDE method achieves state-of-the-art (SOTA) results in the knowledge distillation of unsupervised depth estimation and also outperforms the results of some dense networks. The proposed LUMDE model only loses 2.6% on $\delta 1$ accuracy on the NYUD-V2 dataset compared with the teacher network but reduces the computational complexity by 95.2%. Full article

Polymer-based composites with a high dielectric property have shown great potential in electrical energy storage applications. It is important to predict the dielectric constant in designing polymer composites, but it is costly and time consuming. In this study, dielectric properties of various polymer composites have been predicted by using an artificial neural network (ANN) model trained with hundreds of experimentally measured data. Eight variables such as the dielectric constant of matrix, filler, and shell, the diameter of filler, the volume fraction of filler, the dimension of filler, the thickness of shell, and the frequency were considered. To improve the prediction accuracy, hyper parameters of the ANN model were optimized through the hyperband method. Using the ANN model, we demonstrated the correlation between the dielectric constant of polymer composites and the variables. The ANN model predicted the dielectric constant with a coefficient of determination (R²) of 0.97. Furthermore, the ANN model shows good performance to predict dielectric constant at various frequencies (spanning from 100 Hz to 100 kHz). Hence, we present that the AI-based prediction model using ANN method can be helpful in designing the polymer composites with desired properties. Full article

The cyber security field has witnessed several intrusion detection systems (IDSs) that are critical to the detection of malicious activities in network traffic. In the last couple of years, much research has been conducted in this field; however, in the present circumstances, network attacks are increasing in both volume and diverseness. The objective of this research work is to introduce new IDSs based on a combination of Genetic Algorithms (GAs) and Optimized Gradient Boost Decision Trees (OGBDTs). To improve classification, enhanced African Buffalo Optimizations (EABOs) are used. Optimization Gradient Boost Decision Trees (OGBDT-IDS) include data exploration, preprocessing, standardization, and feature ratings/selection modules. In high-dimensional data, GAs are appropriate tools for selecting features. In machine learning techniques (MLTs), gradient-boosted decision trees (GBDTs) are used as a base learner, and the predictions are added to the set of trees. In this study, the experimental results demonstrate that the proposed methods improve cyber intrusion detection for unused and new cases. Based on performance evaluations, the proposed IDS (OGBDT) performs better than traditional MLTs. The performances are evaluated by comparing accuracy, precision, recall, and F-score using the UNBS-NB 15, KDD 99, and CICIDS2018 datasets. The proposed IDS has the highest attack detection rates, and can predict attacks in all datasets in the least amount of time. Full article