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Appl. Sci., Volume 11, Issue 20 (October-2 2021) – 421 articles

Cover Story (view full-size image): Essential oils from different plant species were found to contain different compounds exhibiting anti-inflammatory effects with the potential to be a valid alternative to conventional chemotherapy that is limited in long-term use due to its serious side effects. Periodontitis is an infectious and inflammatory disease caused by a dysbiosis in the subgingival microbiome that triggers an exacerbated immune response of the host. The aim of the present review is to summarize the current evidence about the effects of essentials oils from derived from plants of the Lamiaceae family as complementary agents for the treatment of subjects with periodontitis and their possible effect on the cardiovascular risk of these patients. View this paper
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Article
The Effect of Task Complexity on Time Estimation in the Virtual Reality Environment: An EEG Study
Appl. Sci. 2021, 11(20), 9779; https://doi.org/10.3390/app11209779 - 19 Oct 2021
Cited by 1 | Viewed by 841
Abstract
This paper investigated the effect of task complexity on time estimation in the virtual reality environment (VRE) using behavioral, subjective, and physiological measurements. Virtual reality (VR) is not a perfect copy of the real world, and individuals perceive time duration differently in the [...] Read more.
This paper investigated the effect of task complexity on time estimation in the virtual reality environment (VRE) using behavioral, subjective, and physiological measurements. Virtual reality (VR) is not a perfect copy of the real world, and individuals perceive time duration differently in the VRE than they do in reality. Though many researchers have found a connection between task complexity and time estimation under non-VR conditions, the influence of task complexity on time estimation in the VRE is yet unknown. In this study, twenty-nine participants performed a VR jigsaw puzzle task at two levels of task complexity. We observed that as task complexity increased, participants showed larger time estimation errors, reduced relative beta-band power at Fz and Pz, and higher NASA-Task Load Index scores. Our findings indicate the importance of controlling task complexity in the VRE and demonstrate the potential of using electroencephalography (EEG) as real-time indicators of complexity level. Full article
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Article
Investigation of Vehicle Stability with Consideration of Suspension Performance
Appl. Sci. 2021, 11(20), 9778; https://doi.org/10.3390/app11209778 - 19 Oct 2021
Cited by 3 | Viewed by 742
Abstract
The issue of movement stability remains highly relevant considering increasing vehicle speeds. The evaluation of vehicle stability parameters and the modeling of specific movement modes is a complex task, as no universal evaluation criteria have been established. The main task in modeling car [...] Read more.
The issue of movement stability remains highly relevant considering increasing vehicle speeds. The evaluation of vehicle stability parameters and the modeling of specific movement modes is a complex task, as no universal evaluation criteria have been established. The main task in modeling car stability is an integrated assessment of the vehicle’s road interactions and identification of relationships. The main system affecting the vehicle’s road interaction is the suspension of the vehicle. Vehicle suspension is required to provide constant wheel to road surface contact, thus creating the preconditions for stability of vehicle movement. At the same time, it must provide the maximum possible body insulation against the effect of unevennesses on the road surface. Combining the two marginal prerequisites is challenging, and the issue has not been definitively solved to this day. Inaccurate alignment of the suspension and damping characteristics of the vehicle suspension impairs the stability of the vehicle, and passengers feel discomfort due to increased vibrations of the vehicle body. As a result, the driving speed is artificially restricted, the durability of the vehicle body is reduced, and the transported cargo is affected. In the study, analytical computational and experimental research methods were used. Specialized vehicle-road interaction assessment programs were developed for theoretical investigation. The methodology developed for assessing vehicle movement stability may be used for the following purposes: design and improvement of vehicle suspension and other mechanisms that determine vehicle stability; analysis of road spans assigned with characteristic vehicle movement settings; road accident situation analysis; design of road structures and establishment of certain operational restrictions on the road structures. A vehicle suspension test bench that included original structure mechanisms that simulate the effect of the road surface was designed and manufactured to test the results of theoretical calculations describing the work of the vehicle suspension and to study various suspension parameters. Experimental investigations were carried out by examining the vibrations of vehicle suspension elements caused by unevenness on the road surface. Full article
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Article
Hydrogen Peroxide Gas Plasma Sterilizer Combined with Dielectric Barrier Discharge and Corona Discharge Inactivates Prions
Appl. Sci. 2021, 11(20), 9777; https://doi.org/10.3390/app11209777 - 19 Oct 2021
Cited by 1 | Viewed by 811
Abstract
Prions are highly resistant to physical or chemical damage, although previous studies have shown that STERRAD®, a hydrogen gas plasma sterilizer using radiofrequency (RF) discharge, has an inactivation effect. Here, the effect of hydrogen peroxide gas combined with dielectric barrier discharge [...] Read more.
Prions are highly resistant to physical or chemical damage, although previous studies have shown that STERRAD®, a hydrogen gas plasma sterilizer using radiofrequency (RF) discharge, has an inactivation effect. Here, the effect of hydrogen peroxide gas combined with dielectric barrier discharge (DBD) plasma and corona discharge plasma using a RENO-S130 sterilizer on scrapie prions was examined. Scrapie prion-infected mouse brain homogenate was air-dried on a cover glass, sealed in a Tyvek pouch, and subjected to RENO-S130 treatment using either non-lumen mode (28 min) or Eco mode (45 min) with hydrogen peroxide gas derived from 50% hydrogen peroxide. Control (untreated) samples were prepared on a cover glass using the same procedure but without exposure to RENO-S130. PrPres (proteinase K (PK)-resistant prion protein), an index of the conformational variant of prion protein (PrPSc), was decreased by treatment with RENO-S130 under both modes of operation. Specifically, PrPres was identified after the 1st and 2nd cycles of protein misfolding cyclic amplification (PMCA) in control samples but was below the detection limit in RENO-S130-treated samples. A bioassay showed that treatment of prions with RENO-S130 (non-lumen or Eco mode) significantly prolonged mouse survival time. Taken together, these findings show hydrogen peroxide gas combined with DBD/corona discharge plasma can inactivate prions by reducing prion propagation and prion infectivity. This treatment is potentially applicable to the sterilization of prion-contaminated heat-sensitive medical devices. Full article
(This article belongs to the Section Applied Physics)
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Article
Reduced Reference Quality Assessment for Image Retargeting by Earth Mover’s Distance
Appl. Sci. 2021, 11(20), 9776; https://doi.org/10.3390/app11209776 - 19 Oct 2021
Cited by 2 | Viewed by 496
Abstract
A reduced reference quality assessment algorithm for image retargeting by earth mover’s distance is proposed in this paper. In the reference image, all the feature points are extracted using scale invariant feature transform. Let the histograms of image patch around each feature point [...] Read more.
A reduced reference quality assessment algorithm for image retargeting by earth mover’s distance is proposed in this paper. In the reference image, all the feature points are extracted using scale invariant feature transform. Let the histograms of image patch around each feature point be local information, and the histograms of saliency feature as global information. Those feature information is extracted at the sender side and transmitted to the receiver side. After that, the same feature information extraction process is performed for the retargeted image at the receiver side. Finally, all feature information of the reference and retargeted images is used collectively to compute the quality of the retargeted image. An overall quality score is calculated from the local and global similarity measure using earth mover’s distance between reference and retargeted images. The key step in our algorithm is to provide an earth mover’s distance metric in a manner that indicates how the local and global information in the reference image is preserved in corresponding retargeted image. Experimental results show that the proposed algorithm can improve the image quality scores on four common criteria in the retargeted image quality assessment community. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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Article
A Fast and Robust Rotation Search and Point Cloud Registration Method for 2D Stitching and 3D Object Localization
Appl. Sci. 2021, 11(20), 9775; https://doi.org/10.3390/app11209775 - 19 Oct 2021
Cited by 2 | Viewed by 748
Abstract
Rotation search and point cloud registration are two fundamental problems in robotics, geometric vision, and remote sensing, which aim to estimate the rotation and transformation between the 3D vector sets and point clouds, respectively. Due to the presence of outliers (probably in very [...] Read more.
Rotation search and point cloud registration are two fundamental problems in robotics, geometric vision, and remote sensing, which aim to estimate the rotation and transformation between the 3D vector sets and point clouds, respectively. Due to the presence of outliers (probably in very large numbers) among the putative vector or point correspondences in real-world applications, robust estimation is of great importance. In this paper, we present Inlier searching using COmpatible Structures (ICOS), a novel, efficient, and highly robust solver for both the correspondence-based rotation search and point cloud registration problems. Specifically, we (i) propose and construct a series of compatible structures for the two problems, based on which various invariants can be established, and (ii) design time-efficient frameworks to filter out outliers and seek inliers from the invariant-constrained random sampling based on the compatible structures proposed. In this manner, even with extreme outlier ratios, inliers can be effectively sifted out and collected for solving the optimal rotation and transformation, leading to our robust solver ICOS. Through plentiful experiments over standard datasets, we demonstrated that: (i) our solver ICOS is fast, accurate, and robust against over 95% outliers with nearly a 100% recall ratio of inliers for rotation search and both known-scale and unknown-scale registration, outperforming other state-of-the-art methods, and (ii) ICOS is practical for use in real-world application problems including 2D image stitching and 3D object localization. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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Editorial
Advances in Industrial and Environmental Microbiology
Appl. Sci. 2021, 11(20), 9774; https://doi.org/10.3390/app11209774 - 19 Oct 2021
Viewed by 495
Abstract
Understanding microorganisms in terms of their functionality, diversity, and interactions with other organisms is crucial for better understanding of our Biosphere [...] Full article
(This article belongs to the Special Issue Advances in Industrial and Environmental Microbiology)
Article
First Perceptions of Hydroperiod Mapping and Assessment of Shallow Waters in Coastal Landscapes by Drone-Based Monitoring Activities: A Remote-Sensing and GIS Approach
Appl. Sci. 2021, 11(20), 9773; https://doi.org/10.3390/app11209773 - 19 Oct 2021
Viewed by 735
Abstract
Beyond the importance of ponds for aquatic and terrestrial life, pond networks seem to be crucial to providing a vital spatial resource in response to global climate change for all migrating and spreading taxa. Additionally, ponds offer sustainable solutions to issues of concern [...] Read more.
Beyond the importance of ponds for aquatic and terrestrial life, pond networks seem to be crucial to providing a vital spatial resource in response to global climate change for all migrating and spreading taxa. Additionally, ponds offer sustainable solutions to issues of concern in water management, such as nutrient retention, rainfall interception, or carbon sequestration. Although the ecological role of shallow waters seems clear, significant work must be performed to set future guidelines and actions towards their conservation. The main aims of the present study are to (i) georeference all small temporary wetlands within the Tyrrhenian central Italy coastal area, (ii) evaluate their hydroperiod, and (iii) calculate their surface size variability. We found 137 wetlands, 53 of which were temporary and contained listed habitats. Each wetland’s status was assessed in relation to land use and proximity to stressors (e.g., urban centres, railways, roads) while observing the relationship between pond occurrence, lithology, and permeability. Amongst the detected wetlands, we selected and monitored 21 temporary ponds (homogeneously distributed within the study area) for 12 months using images collected by the non-professional drone Parrot Bebop 2. All images were then acquired in ArcGIS to georeference all temporary ponds. The analysis confirmed that the majority of the surveyed ponds are in close proximity to roads and tracks, which might have significant impacts on the preservation of such fragile habitats. Moreover, despite the wide variability of hydroperiod duration, the greater part of the pools fill with water in autumn and dry in summer, in alignment with the Mediterranean climate. This preliminary study allowed for the creation of the first temporary ponds’ database, which is useful for monitoring their status in central Italy and planning further studies to assess eventual detrimental effects caused by human-mediated activities. Full article
(This article belongs to the Special Issue Freshwater Ecological and Environmental Status)
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Article
Design of Gas Cyclone Using Hybrid Particle Swarm Optimization Algorithm
Appl. Sci. 2021, 11(20), 9772; https://doi.org/10.3390/app11209772 - 19 Oct 2021
Cited by 2 | Viewed by 470
Abstract
The method of searching for an optimal solution inspired by nature is referred to as particle swarm optimization. Differential evolution is a simple but effective EA for global optimization since it has demonstrated strong convergence qualities and is relatively straightforward to comprehend. The [...] Read more.
The method of searching for an optimal solution inspired by nature is referred to as particle swarm optimization. Differential evolution is a simple but effective EA for global optimization since it has demonstrated strong convergence qualities and is relatively straightforward to comprehend. The primary concerns of design engineers are that the traditional technique used in the design process of a gas cyclone utilizes complex mathematical formulas and a sensitivity approach to obtain relevant optimal design parameters. The motivation of this research effort is based on the desire to simplify complex mathematical models and the sensitivity approach for gas cyclone design with the use of an objective function, which is of the minimization type. The process makes use of the initial population generated by the DE algorithm, and the stopping criterion of DE is set as the fitness value. When the fitness value is not less than the current global best, the DE population is taken over by PSO. For each iteration, the new velocity and position are updated in every generation until the optimal solution is achieved. When using PSO independently, the adoption of a hybridised particle swarm optimization method for the design of an optimum gas cyclone produced better results, with an overall efficiency of 0.70, and with a low cost at the rate of 230 cost/s. Full article
(This article belongs to the Topic Applied Metaheuristic Computing)
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Article
Vehicle Delay Model Applied to Dynamic and Static Traffic Impact Analysis of Large Parking Lots
Appl. Sci. 2021, 11(20), 9771; https://doi.org/10.3390/app11209771 - 19 Oct 2021
Cited by 1 | Viewed by 553
Abstract
With the surge of motor vehicle ownership and land intensification, plenty of large parking lots emerge as the times demand. Although it solves the problem of insufficient parking spaces, it intensifies the interaction between dynamic and static traffic. This paper presented an impact [...] Read more.
With the surge of motor vehicle ownership and land intensification, plenty of large parking lots emerge as the times demand. Although it solves the problem of insufficient parking spaces, it intensifies the interaction between dynamic and static traffic. This paper presented an impact assessment method for the interaction of dynamic and static traffic flow in parking lots. Firstly, the average vehicle delay was selected as the evaluation index. The delay effect caused by the interaction of dynamic and static traffic flow was determined according to the driving path of vehicles. Then, the average vehicle delay models of arrival vehicles, departure vehicles, and road vehicles in the parking lot were established. Finally, for the parameters difficult to determine directly in the delay model, this paper proposed a method to calibrate the model parameters by using the simulation experimental data on the VISSIM platform. The results showed that the errors of the three models are within the controllable range, and the delay model parameters had high reliability and feasibility. The delay models can provide a quantitative basis for the parking lot management department to formulate regulation strategies and realize more refined information guidance and navigation in the parking lot. Full article
(This article belongs to the Section Transportation and Future Mobility)
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Article
Observing Brief and Irregular Behaviour of Animals—The Validity of Short Observation Periods
Appl. Sci. 2021, 11(20), 9770; https://doi.org/10.3390/app11209770 - 19 Oct 2021
Cited by 1 | Viewed by 514
Abstract
There are efficient sampling methods to accurately estimate behaviour with a moderate or long duration. For short behaviour, observing animals continuously is recommended although there is no recommended minimum observation time. In most studies, sampling method and observation time per day is determined [...] Read more.
There are efficient sampling methods to accurately estimate behaviour with a moderate or long duration. For short behaviour, observing animals continuously is recommended although there is no recommended minimum observation time. In most studies, sampling method and observation time per day is determined by practical considerations. Thus, this study analysed the validity of behavioural observations in different observation periods using continuous sampling (CS) or time sampling (TS) based on biting behaviour. Tail-biting and ear-biting of weaned piglets in six pens were continuously observed for 12 h per day for 4 days to form a reference. Shorter observation periods of CS and TS were simulated by taking subsets of this reference. The amount of behaviour per hour of each observation period was compared to the reference and to other observation period of the same kind and length. Four different measurements were defined to calculate accuracy scores (AS; 0–1; higher values are better). Comparison to the reference shows better AS for observation periods with longer observation time in total (0.5 h of CS: 0.2; 6 h of CS: 0.6). Additionally, TS covers longer time periods without decreasing AS. However, focus on activity time results in an overestimation of irregular behaviour. Comparing AS among observation periods of the same kind and length show overall low agreement. This study indicated problems of different observation periods of CS and TS to accurately estimate behaviour. Therefore, validity of behavioural observations should be analysed in greater detail to determine optimal sampling methods. Full article
(This article belongs to the Special Issue Animal Behavior in Intensive Culture Environment)
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Article
A Deep Convolutional Neural Network-Based Multi-Class Image Classification for Automatic Wafer Map Failure Recognition in Semiconductor Manufacturing
Appl. Sci. 2021, 11(20), 9769; https://doi.org/10.3390/app11209769 - 19 Oct 2021
Cited by 3 | Viewed by 810
Abstract
Wafer maps provide engineers with important information about the root causes of failures during the semiconductor manufacturing process. Through the efficient recognition of the wafer map failure pattern type, the semiconductor manufacturing process and its product performance can be improved, as well as [...] Read more.
Wafer maps provide engineers with important information about the root causes of failures during the semiconductor manufacturing process. Through the efficient recognition of the wafer map failure pattern type, the semiconductor manufacturing process and its product performance can be improved, as well as reducing the product cost. Therefore, this paper proposes an accurate model for the automatic recognition of wafer map failure types using a deep learning-based convolutional neural network (DCNN). For this experiment, we use WM811K, which is an open-source real-time wafer map dataset containing wafer map images of nine failure classes. Our research contents can be briefly summarized as follows. First, we use random sampling to extract 500 images from each class of the original image dataset. Then we propose a deep convolutional neural network model to generate a multi-class classification model. Lastly, we evaluate the performance of the proposed prediction model and compare it with three other popular machine learning-based models—logistic regression, random forest, and gradient boosted decision trees—and several well-known deep learning models—VGGNet, ResNet, and EfficientNet. Consequently, the comprehensive analysis showed that the performance of the proposed DCNN model outperformed those of other popular machine learning and deep learning-based prediction models. Full article
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Article
Mechanism Analysis of the Influence of Seat Attributes on the Seat Dip Effect in Music Halls
Appl. Sci. 2021, 11(20), 9768; https://doi.org/10.3390/app11209768 - 19 Oct 2021
Cited by 1 | Viewed by 454
Abstract
The seat dip effect (SDE) is an acoustic phenomenon of low-frequency band attenuation that occurs in the music halls when the sound of the music passes at a near grazing incidence over the seats. In this paper, the numerical simulations on the basis [...] Read more.
The seat dip effect (SDE) is an acoustic phenomenon of low-frequency band attenuation that occurs in the music halls when the sound of the music passes at a near grazing incidence over the seats. In this paper, the numerical simulations on the basis of the finite element method are conducted to study the influence of seat attributes (seat height, seat spacing and seat absorption) on the SDE and the corresponding mechanism. The mapping of sound spatial distribution related to the SDE is employed to observe the behavior of sound between the seats. The results show that the dip frequency of the SDE can be shifted to frequencies lower than theoretical values when the seat height is smaller than the seat spacing. Additionally, the SDE attenuation can be distinctly suppressed in a sequence from the front seats to the rear seats with an absorption improvement to the seat back or cushion, and the seat back absorption is more effective than the cushion absorption. A mechanism analysis reveals that the SDE is highly associated with standing waves inside the seat gaps and with the “diffusion” effect on the grazing incident waves by energy flow vortexes around the top surfaces of the seats. Full article
(This article belongs to the Special Issue Advances in Architectural Acoustics)
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Article
Numerical Study of the Transmission of Exhaled Droplets between the Instructor and Students in a Typical Classroom
Appl. Sci. 2021, 11(20), 9767; https://doi.org/10.3390/app11209767 - 19 Oct 2021
Cited by 1 | Viewed by 497
Abstract
Conducting physical attendance exams during pandemics is a challenge facing many educational institutes and universities. Our study’s main objective is to numerically simulate the expected transmission of the harmful exhaled droplets of aerosols from an infected instructor to students in an exam room [...] Read more.
Conducting physical attendance exams during pandemics is a challenge facing many educational institutes and universities. Our study’s main objective is to numerically simulate the expected transmission of the harmful exhaled droplets of aerosols from an infected instructor to students in an exam room ventilated by a number of spiral diffusers. Several critical parameters, including the droplet size, the ventilation rate, and status of the entrance door were considered in the analysis. Two dimensionless indices, i.e., the specific normalized average concentration (SNAC) and the exceedance in exposure ratio (EER), were introduced to examine the effect of the said parameters on student exposure to the harmful droplets. The study revealed that the 5 μm droplets were less hazardous as they resulted in an 87% reduction in exposure when compared with the small 1 μm size droplets. We also found that when the ventilation rate ratio (VRR) increased above unity, an upward entrainment process, due to the swirl diffuser, of the aerosol droplets took place, and consequently the risk of student exposure was reduced. The results also demonstrated that increasing VRR from zero to 1 and then to 2 decreased the exceedance in the student exposure from 3.5 to 2.15 and then to less than zero, respectively. The study also showed that keeping the lecture room’s main door open is recommended as this reduced the risk of exposure by 26% in the case of a VRR equal to 2. Full article
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Article
Dynamic Stability of an Electric Monowheel System Using LQG-Based Adaptive Control
Appl. Sci. 2021, 11(20), 9766; https://doi.org/10.3390/app11209766 - 19 Oct 2021
Cited by 4 | Viewed by 566
Abstract
This paper presents the simulation and calculation-based aspect of constructing a dynamically stable, self-balancing electric monowheel from first principles. It further goes on to formulate a reference model-based adaptive control structure in order to maintain balance as well as the desired output. First, [...] Read more.
This paper presents the simulation and calculation-based aspect of constructing a dynamically stable, self-balancing electric monowheel from first principles. It further goes on to formulate a reference model-based adaptive control structure in order to maintain balance as well as the desired output. First, a mathematical model of the nonlinear system analyzes the vehicle dynamics, followed by an appropriate linearization technique. Suitable parameters for real-time vehicle design are calculated based on specific constraints followed by a proper motor selection. Various control methods are tested and implemented on the state-space model of this system. Initially, classical pole placement control is carried out in MATLAB to observe the responses. The LQR control method is also implemented in MATLAB and Simulink, demonstrating the dynamic stability and self-balancing system property. Subsequently, the system considers an extensive range of rider masses and external disturbances by introducing white noise. The parameter estimation of rider position has been implemented using Kalman Filter estimation, followed by developing an LQG controller for the system, in order to mitigate the disturbances caused by factors such as wind. A comparison between LQR and LQG controllers has been conducted. Finally, a reference model-assisted adaptive control structure has been established for the system to account for sudden parameter changes such as rider mass. A reference model stabilizer has been established for the same purpose, and all results have been obtained by running simulations on MATLAB Simulink. Full article
(This article belongs to the Special Issue New Trends in the Control of Robots and Mechatronic Systems)
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Article
Biosignal-Based Driving Skill Classification Using Machine Learning: A Case Study of Maritime Navigation
Appl. Sci. 2021, 11(20), 9765; https://doi.org/10.3390/app11209765 - 19 Oct 2021
Cited by 1 | Viewed by 585
Abstract
This work presents a novel approach to detecting stress differences between experts and novices in Situation Awareness (SA) tasks during maritime navigation using one type of wearable sensor, Empatica E4 Wristband. We propose that for a given workload state, the values of biosignal [...] Read more.
This work presents a novel approach to detecting stress differences between experts and novices in Situation Awareness (SA) tasks during maritime navigation using one type of wearable sensor, Empatica E4 Wristband. We propose that for a given workload state, the values of biosignal data collected from wearable sensor vary in experts and novices. We describe methods to conduct a designed SA task experiment, and collected the biosignal data on subjects sailing on a 240° view simulator. The biosignal data were analysed by using a machine learning algorithm, a Convolutional Neural Network. The proposed algorithm showed that the biosingal data associated with the experts can be categorized as different from that of the novices, which is in line with the results of NASA Task Load Index (NASA-TLX) rating scores. This study can contribute to the development of a self-training system in maritime navigation in further studies. Full article
(This article belongs to the Topic Artificial Intelligence in Sensors)
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Article
The Role of Peripheral Nerve Electrotherapy in Functional Recovery of Muscle Motor Units in Patients after Incomplete Spinal Cord Injury
Appl. Sci. 2021, 11(20), 9764; https://doi.org/10.3390/app11209764 - 19 Oct 2021
Cited by 2 | Viewed by 615
Abstract
Functional electrical nerve stimulation (FES) is a non-invasive technique for neuromodulation and may have the potential for motor rehabilitation following incomplete spinal cord injury (iSCI). Axonal degeneration in motor fibers of lower extremity nerves is an inevitable secondary pathological change in iSCI subjects, [...] Read more.
Functional electrical nerve stimulation (FES) is a non-invasive technique for neuromodulation and may have the potential for motor rehabilitation following incomplete spinal cord injury (iSCI). Axonal degeneration in motor fibers of lower extremity nerves is an inevitable secondary pathological change in iSCI subjects, despite no direct damage to lumbosacral neuromeres. This study evaluated the role of FES with individual parameters based on results of comparative neurophysiological studies. Forty-two participants with C4 to Th12 iSCI received repetitive sessions of electrostimulations applied to peroneal and tibial motor fibers, performed five times a week from 6 to 14 months, and the uniform system of kinesiotherapeutic treatment. The average duration of one electrostimulation session was 17 min, stimulation frequency of a train 20–70 Hz, duration of 2–3 s, intervals 2–3 s, pulses intensity 18–45 mA. The algorithm change was based on objective tests of subsequent surface electromyography (sEMG), and electroneurography (ENG) recordings. The same neurophysiological studies were also performed in patients after C2-Th12 iSCI treated with kinesiotherapy only (K group, N = 25) and compared with patients treated with both kinesiotherapy and electrostimulation (K + E, N = 42). The study revealed improvements in sEMG parameters recorded from tibialis anterior, gastrocnemius, extensor digitorum brevis muscles, and ENG evoked a compound muscle action potential recorded following bilateral stimulation of more peroneal than tibial nerves. Neurophysiological recordings had significantly better parameters in the K + E group of patients after therapy but not in the K group patients. The improvement of the motor transmission peripherally may reflect the specific neuromodulatory effect of FES algorithm evaluated with sEMG and ENG. FES may inhibit degeneration of axons and support functional recovery after iSCI. Full article
(This article belongs to the Section Neurosciences)
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Article
Applying Precision Agriculture to Artificial Waterfowl Hatching, Using the Black Muscovy Duck as an Example
Appl. Sci. 2021, 11(20), 9763; https://doi.org/10.3390/app11209763 - 19 Oct 2021
Viewed by 632
Abstract
(1) Background: agriculture practices adopt homogenization-farming processes to enhance product characteristics, with lower costs, standardization, mass production, and production efficiency. (2) Problem: conventional agriculture practices eliminate products when these products are slightly different from the expected status in each phase of [...] Read more.
(1) Background: agriculture practices adopt homogenization-farming processes to enhance product characteristics, with lower costs, standardization, mass production, and production efficiency. (2) Problem: conventional agriculture practices eliminate products when these products are slightly different from the expected status in each phase of the lifecycle due to the changing natural environment and climate. However, this elimination of products can be avoided when they receive customized care to the expected developing path via a universal prediction model, for the quantitative description of biomass changing with time and the environment, and the corresponding automatic environmental controls. (3) Methods: in this study, we built a prediction model to quantitatively predict the hatching rate of each egg by observing the biomass development path along the waterfowl-like production lifecycle and the corresponding environment settings. (4) Results: two experiments using black Muscovy duck hatching as a case study were executed. The first experiment involved finding out the key characteristics, out of 25 characteristics, and building a prediction model to quantitatively predict the survivability of the black Muscovy duck egg. The second experiment was adopted to validate the effectiveness of our prediction mode; the hatching rate rose from 47% in the first experiment to 62% in the second experiment without any human interference from experienced farmers. (5) Contributions: this research builds on an AI-based precision agriculture system prototype as the reference for waterfowl research. The results show that our proposed model is capable of decreasing the training costs and enhancing the product qualification rate for individual agricultural products. Full article
(This article belongs to the Topic Machine and Deep Learning)
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Article
Blue Whiting Protein Hydrolysates Exhibit Antioxidant and Immunomodulatory Activities in Stimulated Murine RAW264.7 Cells
Appl. Sci. 2021, 11(20), 9762; https://doi.org/10.3390/app11209762 - 19 Oct 2021
Cited by 1 | Viewed by 656
Abstract
This study investigated the antioxidant and immunomodulatory potential of six blue whiting soluble protein hydrolysates (BWSPHs, BW-SPH-A to -F) and their simulated gastrointestinal digests (SGID, BW-SPH-A-GI to -F-GI) in murine RAW264.7 macrophages. Hydrolysate BW-SPH-A, both pre- and post-SGID, increased endogenous antioxidant glutathione (GSH) [...] Read more.
This study investigated the antioxidant and immunomodulatory potential of six blue whiting soluble protein hydrolysates (BWSPHs, BW-SPH-A to -F) and their simulated gastrointestinal digests (SGID, BW-SPH-A-GI to -F-GI) in murine RAW264.7 macrophages. Hydrolysate BW-SPH-A, both pre- and post-SGID, increased endogenous antioxidant glutathione (GSH) in tert-butylhydroperoxide (tBOOH)-treated cells and reduced reactive oxygen species (ROS) in H2O2-challenged RAW264.7 cells compared with treated controls in the absence of BWSPHs (p < 0.05). BW-SPH-A-GI also exhibited higher ferric reducing antioxidant power (FRAP) and oxygen radical absorbance capacity (ORAC) activities than the other BWSPHs tested (p < 0.05). All BWSPHs and SGID BWSPH samples induced immunostimulating effects in lipopolysaccharide (LPS)-activated RAW264.7 macrophages through the upregulation of NO production. BW-SPH-F-GI increased IL-6 and TNF-α levels compared with the LPS controls indicating the liberation of immunomodulatory peptide/amino acids during the SGID process. Therefore, BW-SPH-A and BW-SPH-F may have potential use against oxidative stress and immunosuppression-related diseases, respectively. Full article
(This article belongs to the Special Issue Role and Properties of Proteins and Peptides in Foods)
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Modelling Strategies for the Numerical Simulation of the Behaviour of Corroded RC Columns under Cyclic Loads
Appl. Sci. 2021, 11(20), 9761; https://doi.org/10.3390/app11209761 - 19 Oct 2021
Cited by 4 | Viewed by 585
Abstract
Rebars corrosion phenomena can modify the structural behaviour of reinforced concrete (RC) members and consequently the seismic performance of RC structures. Since many existing RC structures are affected by this phenomenon, the influence of the reinforcement corrosion on the seismic performance is still [...] Read more.
Rebars corrosion phenomena can modify the structural behaviour of reinforced concrete (RC) members and consequently the seismic performance of RC structures. Since many existing RC structures are affected by this phenomenon, the influence of the reinforcement corrosion on the seismic performance is still under examination, especially when the corrosive attack is localized in the dissipative areas of the plastic hinges. In this work, the effect of localized corrosion is numerically investigated, through the adoption of a suitable finite element model, object of validation with the outcomes of an experimental campaign carried out in the Laboratory of the University of Rome “Tor Vergata”, on un-corroded and corroded RC columns subjected to axial load and cyclic horizontal actions. Particular attention has been paid to the definition of the three-dimensional model and to the modelling of the corroded rebars and their corrosion morphology. Indeed, different modelling strategies are proposed with the aim to properly simulate the cyclic behaviour of the corroded columns. The main results show how more refined strategies taking into account the morphological aspects of the corrosion phenomenon produce a better fit with the experimental results for both Damage Control and Life Safety limit states performance. Full article
(This article belongs to the Special Issue Seismic Assessment and Retrofit of Reinforced Concrete Structures)
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Article
Application of ANN in Predicting the Cantilever Wall Deflection in Undrained Clay
Appl. Sci. 2021, 11(20), 9760; https://doi.org/10.3390/app11209760 - 19 Oct 2021
Cited by 1 | Viewed by 554
Abstract
The main objective of this study is to propose an artificial neural network (ANN)-based tool for predicting the cantilever wall deflection in undrained clay. The excavation width, the excavation depth, the wall thickness, the at-rest lateral earth pressure coefficient, the soil shear strength [...] Read more.
The main objective of this study is to propose an artificial neural network (ANN)-based tool for predicting the cantilever wall deflection in undrained clay. The excavation width, the excavation depth, the wall thickness, the at-rest lateral earth pressure coefficient, the soil shear strength ratio at mid-depth of the wall, and the soil stiffness ratio at mid-depth of the wall were selected as the input parameters, whereas the cantilever wall deflection was selected as an output parameter. A set of verified numerical data were utilized to train, test, and validate the ANN models. Two commonly used performance indicators, namely, root mean square error (RMSE) and mean absolute error (MAE), were selected to evaluate the performance of the proposed model. The results indicated that the proposed model can reliably predict the cantilever wall deflection in undrained clay. Moreover, the sensitivity analysis showed that the excavation depth is the most important parameter. Finally, a graphical user interface (GUI) tool was developed based on the proposed ANN model, which is much easier and less expensive to be used in practice. The results of this study can help engineers to better understand and predict the cantilever wall deflection in undrained clay. Full article
(This article belongs to the Section Civil Engineering)
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Article
Multi-Objective Optimization of CO2 Sequestration in Heterogeneous Saline Aquifers under Geological Uncertainty
Appl. Sci. 2021, 11(20), 9759; https://doi.org/10.3390/app11209759 - 19 Oct 2021
Viewed by 622
Abstract
This paper presents a Pareto-based multi-objective optimization for operating CO2 sequestration with a multi-well system under geological uncertainty; the optimal well allocation, i.e., the optimal allocation of CO2 rates at injection wells, is obtained when there is minimum operation pressure as [...] Read more.
This paper presents a Pareto-based multi-objective optimization for operating CO2 sequestration with a multi-well system under geological uncertainty; the optimal well allocation, i.e., the optimal allocation of CO2 rates at injection wells, is obtained when there is minimum operation pressure as well as maximum sequestration efficiency. The distance-based generalized sensitivity analysis evaluates the influence of geological uncertainty on the amount of CO2 sequestration through four injection wells at 3D heterogeneous saline aquifers. The spatial properties significantly influencing the trapping volume, in descending order of influence, are mean sandstone porosity, mean sandstone permeability, shale volume ratio, and the Dykstra–Parsons coefficient of permeability. This confirms the importance of storable capacity and heterogeneity in quantitatively analyzing the trapping mechanisms. Multi-objective optimization involves the use of two aquifer models relevant to heterogeneity; one is highly heterogeneous and the other is less so. The optimal well allocations converge to non-dominated solutions and result in a large injection through one specific well, which generates the wide spread of a highly mobile CO2 plume. As the aquifer becomes heterogeneous with a large shale volume and a high Dykstra–Parsons coefficient, the trapping performances of the combined structural and residual sequestration plateau relatively early. The results discuss the effects of spatial heterogeneity on achieving CO2 geological storage, and they provide an operation strategy including multi-objective optimization. Full article
(This article belongs to the Special Issue Digital Technologies in the Petroleum Industry)
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Article
Design and Test of Load-Lifting Performance for Hydraulic Linkage of the High-Medium Horsepower Tractor
Appl. Sci. 2021, 11(20), 9758; https://doi.org/10.3390/app11209758 - 19 Oct 2021
Viewed by 568
Abstract
To improve the detection efficiency and safety of the tractor, the research proposed a device for detecting the loading–lifting performance of the lower link of the tractor based on the four-bar mechanism. According to the actual use requirements and the testing standards, the [...] Read more.
To improve the detection efficiency and safety of the tractor, the research proposed a device for detecting the loading–lifting performance of the lower link of the tractor based on the four-bar mechanism. According to the actual use requirements and the testing standards, the critical components in the device were designed. The dynamic analysis of the load-lifting device was carried out by dynamic simulation, and the component strength in the machine was checked by the finite element simulation method. The results showed that the designed device could realize the hooking and connection of the lower link without an artificial method. The average cost of the device was 5.13 s to realize the connection with the lower link, and it took 7.30 s to raise the lower hitch point to a set height, about 750 mm. The loading test showed that the device could keep the loading force of the lower link stable during the lifting process. The designed device could shorten the detection time of the tractor hydraulic linkage and improve the cost, safety, and efficiency of detection. The research could provide a reference for the design of hydraulic linkage detection devices for the large-medium horsepower tractors and help realize the intelligent detection of tractors. Full article
(This article belongs to the Section Robotics and Automation)
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Review
Non-Destructive Testing Applications for Steel Bridges
Appl. Sci. 2021, 11(20), 9757; https://doi.org/10.3390/app11209757 - 19 Oct 2021
Cited by 18 | Viewed by 1433
Abstract
The growing population and increasing demand for surface transportation have highlighted the importance of maintaining safe and reliable civil infrastructures for daily use. Among all civil infrastructures, bridges are one of the most important elements in the transportation system. As such, to prevent [...] Read more.
The growing population and increasing demand for surface transportation have highlighted the importance of maintaining safe and reliable civil infrastructures for daily use. Among all civil infrastructures, bridges are one of the most important elements in the transportation system. As such, to prevent any failures caused by aging and environmental impacts, bridges require periodic inspections. This becomes even more critical due to climate change and its effect on bridges, especially in the coastal regions. Most of the inspections conducted incorporate the visual type of evaluation due to its simplicity. However, with the current developments in new technologies, there is a need for more advanced techniques of structural health monitoring (SHM) methods to be incorporated in the maintenance programs for more accurate and efficient surveys. In this paper, non-destructive testing (NDT) methods applicable to steel bridges are reviewed, with a focus on methods applicable to local damage detection. Moreover, the methodology, advantages and disadvantages, and up-to-date research on NDT methods are presented. Furthermore, the application of novel NDT techniques using innovative sensors, drones, and robots for the rapid and efficient assessment of damages on small and large scales is emphasized. This study is deemed necessary as it compiles in one place the available information regarding NDT methods for in-service steel bridges. Access to such information is critical for researchers who intend to work on new or improved NDT techniques. Full article
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Article
Beekeeping in the Desert: Foraging Activities of Honey Bee during Major Honeyflow in a Hot-Arid Ecosystem
Appl. Sci. 2021, 11(20), 9756; https://doi.org/10.3390/app11209756 - 19 Oct 2021
Viewed by 847
Abstract
This study investigated the outgoing and pollen-gathering foraging activities of Apis mellifera jemenitica (AMJ) and Apis mellifera carnica (AMC) under a hot-arid environment in the presence of nectar-rich melliferous Ziziphus nummularia flora. The data revealed the differential effects of [...] Read more.
This study investigated the outgoing and pollen-gathering foraging activities of Apis mellifera jemenitica (AMJ) and Apis mellifera carnica (AMC) under a hot-arid environment in the presence of nectar-rich melliferous Ziziphus nummularia flora. The data revealed the differential effects of weather conditions and Z. nummularia flora on the foraging activities of the studied honey bee subspecies in the Rawdat-Khuraim oasis in central Saudi Arabia. Z. nummularia exhibited two flowering seasons, from June–July (season I) and August–October (season II), with a significantly higher mean flowering density observed during season II (404 flowers/m2) than during season I (235 flowers/m2). AMJ showed significantly higher foraging activities (outgoing and pollen-gathering) than AMC (exotic bees) during all months in each flowering season. The mean outgoing and pollen-gathering foraging rates of AMJ (32.40 ± 0.67 and 4.88 ± 0.40 workers/colony/min, respectively) were significantly higher than those of AMC (15.93 ± 1.20 and 2.39 ± 0.23 workers/colony/min, respectively). The outgoing and pollen-gathering foraging activities of the two subspecies fluctuated throughout the different times of day. Foraging activities were considerably high at sunrise (SR) and low at noon (N) during both flowering seasons. We also observed seasonal variations in the foraging activities of both bee subspecies. The mean foraging activities (outgoing and pollen-gathering) were slightly higher in season I (27.43 ± 1.21 and 4.46 ± 0.45 workers/colony/min, respectively) than in season II (21.71 ± 0.86 and 3.02 ± 0.22 workers/colony/min, respectively). The thermal window analysis revealed a significant difference between the flight activities (bees exiting and returning to the nest throughout the day) of AMJ and AMC; AMJ had a higher temperature threshold than AMC. The outgoing and pollen-gathering foraging activities within each bee subspecies were positively correlated. The present study can help researchers understand the performances of honeybees and the association of their performances with weather and nectar-rich flora conditions. Full article
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Article
Deep Learning-Based In Vitro Detection Method for Cellular Impurities in Human Cell-Processed Therapeutic Products
Appl. Sci. 2021, 11(20), 9755; https://doi.org/10.3390/app11209755 - 19 Oct 2021
Viewed by 712
Abstract
Automated detection of impurities is in demand for evaluating the quality and safety of human cell-processed therapeutic products in regenerative medicine. Deep learning (DL) is a powerful method for classifying and recognizing images in cell biology, diagnostic medicine, and other fields because it [...] Read more.
Automated detection of impurities is in demand for evaluating the quality and safety of human cell-processed therapeutic products in regenerative medicine. Deep learning (DL) is a powerful method for classifying and recognizing images in cell biology, diagnostic medicine, and other fields because it automatically extracts the features from complex cell morphologies. In the present study, we construct prediction models that recognize cancer-cell contamination in continuous long-term (four-day) cell cultures. After dividing the whole dataset into Early- and Late-stage cell images, we found that Late-stage images improved the DL performance. The performance was further improved by optimizing the DL hyperparameters (batch size and learning rate). These findings are first report for the implement of DL-based systems in disease cell-type classification of human cell-processed therapeutic products (hCTPs), that are expected to enable the rapid, automatic classification of induced pluripotent stem cells and other cell treatments for life-threatening or chronic diseases. Full article
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Editorial
Special Issue “Novel Approaches and Applications in Ergonomic Design”
Appl. Sci. 2021, 11(20), 9754; https://doi.org/10.3390/app11209754 - 19 Oct 2021
Viewed by 376
Abstract
Interactions between humans and systems need to be designed appropriately for safety, usability, productivity, health, and/or wellness [...] Full article
(This article belongs to the Special Issue Novel Approaches and Applications in Ergonomic Design)
Article
Ultrafast Electron Dynamics in Magnetic Thin Films
Appl. Sci. 2021, 11(20), 9753; https://doi.org/10.3390/app11209753 - 19 Oct 2021
Cited by 1 | Viewed by 548
Abstract
In past decades, ultrafast spin dynamics in magnetic systems have been associated with heat deposition from high energy laser pulses, limiting the selective access to spin order. Here, we use a long wavelength terahertz (THz) pump–optical probe setup to measure structural features in [...] Read more.
In past decades, ultrafast spin dynamics in magnetic systems have been associated with heat deposition from high energy laser pulses, limiting the selective access to spin order. Here, we use a long wavelength terahertz (THz) pump–optical probe setup to measure structural features in the ultrafast time scale. We find that complete demagnetization is possible with <6 THz pulses. This occurs concurrently with longitudinal acoustic phonons and an electronic response. Full article
(This article belongs to the Special Issue Selected Papers in the Section Materials 2022)
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Article
Exploiting Script Similarities to Compensate for the Large Amount of Data in Training Tesseract LSTM: Towards Kurdish OCR
Appl. Sci. 2021, 11(20), 9752; https://doi.org/10.3390/app11209752 - 19 Oct 2021
Cited by 1 | Viewed by 806
Abstract
Applications based on Long-Short-Term Memory (LSTM) require large amounts of data for their training. Tesseract LSTM is a popular Optical Character Recognition (OCR) engine that has been trained and used in various languages. However, its training becomes obstructed when the target language is [...] Read more.
Applications based on Long-Short-Term Memory (LSTM) require large amounts of data for their training. Tesseract LSTM is a popular Optical Character Recognition (OCR) engine that has been trained and used in various languages. However, its training becomes obstructed when the target language is not resourceful. This research suggests a remedy for the problem of scant data in training Tesseract LSTM for a new language by exploiting a training dataset for a language with a similar script. The target of the experiment is Kurdish. It is a multi-dialect language and is considered less-resourced. We choose Sorani, one of the Kurdish dialects, that is mostly written in Persian-Arabic script. We train Tesseract using an Arabic dataset, and then we use a considerably small amount of texts in Persian-Arabic to train the engine to recognize Sorani texts. Our dataset is based on a series of court case documents in the Kurdistan Region of Iraq. We also fine-tune the engine using 10 Unikurd fonts. We use Lstmeval and Ocreval to evaluate the outputs. The result indicates the achievement of 95.45% accuracy. We also test the engine using texts outside the context of court cases. The accuracy of the system remains close to what was found earlier indicating that the script similarity could be used to overcome the lack of large-scale data. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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Article
Developing the Smart Sorting Screw System Based on Deep Learning Approaches
Appl. Sci. 2021, 11(20), 9751; https://doi.org/10.3390/app11209751 - 19 Oct 2021
Viewed by 567
Abstract
The deep learning technique has turned into a mature technique. In addition, many researchers have applied deep learning methods to classify products into defective categories. However, due to the limitations of the devices, the images from factories cannot be trained and inferenced in [...] Read more.
The deep learning technique has turned into a mature technique. In addition, many researchers have applied deep learning methods to classify products into defective categories. However, due to the limitations of the devices, the images from factories cannot be trained and inferenced in real-time. As a result, the AI technology could not be widely implemented in actual factory inspections. In this study, the proposed smart sorting screw system combines the internet of things technique and an anomaly network for detecting the defective region of the screw product. The proposed system has three prominent characteristics. First, the spiral screw images are stitched into a panoramic image to comprehensively detect the defective region that appears on the screw surface. Second, the anomaly network comprising of convolutional autoencoder (CAE) and adversarial autoencoder (AAE) networks is utilized to automatically recognize the defective areas in the absence of a defective-free image for model training. Third, the IoT technique is employed to upload the screw image to the cloud platform for model training and inference, in order to determine if the defective screw product is a pass or fail on the production line. The experimental results show that the image stitching method can precisely merge the spiral screw image to the panoramic image. Among these two anomaly models, the AAE network obtained the best maximum IOU of 0.41 and a maximum dice coefficient score of 0.59. The proposed system has the ability to automatically detect a defective screw image, which is helpful in reducing the flow of the defective products in order to enhance product quality. Full article
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Article
An Application of Safety Assessment for Radioactive Waste Repository: Non-Equilibrium Transport of Tritium, Selenium, and Cesium in Crushed Granite with Different Path Lengths
Appl. Sci. 2021, 11(20), 9750; https://doi.org/10.3390/app11209750 - 19 Oct 2021
Cited by 1 | Viewed by 456
Abstract
Advection-dispersion experiments (ADE) were effectively designed for inadequate transport models through a calibration/validation process. HTO, selenium (Se), and cesium (Cs) transport in crushed granite were studied using a highly reliable, dynamic column device in order to obtain the retardation factors (R) and the [...] Read more.
Advection-dispersion experiments (ADE) were effectively designed for inadequate transport models through a calibration/validation process. HTO, selenium (Se), and cesium (Cs) transport in crushed granite were studied using a highly reliable, dynamic column device in order to obtain the retardation factors (R) and the dispersion coefficients (D) by fitting experimental breakthrough curves (BTCs) for various path lengths. In order to conduct a safety assessment (SA) of a deep geological repository for high-level radioactive waste, radionuclide transport in rock systems is necessary to clarify and establish a suitable model. A dynamic column with a radiotracer (HTO, Se(IV), and Cs) was applied to 2, 4, and 8 cm path lengths using a STANMOD simulation. The results showed similar results between the BTCs of Se and Cs by fitting a non-equilibrium sorption model due to the retardation effect. In fact, there was a relatively obvious sorption of Se and Cs in the BTCs obtained by fitting a retardation factor (R) value higher than 1. In addition, a two-region (physical) and a two-site (chemical) non-equilibrium model with either the lowest sum of squared residuals (SSQ) or the root mean square error (RMSE) were applied to determine the Se and Cs sorption mechanisms on granite. Full article
(This article belongs to the Special Issue Applied Geochemistry and Clay Science)
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