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Volume 12, January-1

Appl. Sci., Volume 12, Issue 2 (January-2 2022) – 407 articles

Cover Story (view full-size image): We investigated the structural and magnetic properties of 20 nm sized nanoparticles of the half-doped manganite Ho0.5Ca0.5MnO3 prepared by a sol–gel approach. There are signs of CO in the temperature dependence of magnetization. Accordingly, below 100 K, superlattice magnetic Bragg reflections arise, which are consistent with an antiferromagnetic phase strictly related to the bulk Mn ordering of a charge exchange type (CE type) but characterized by an increased fraction of ferromagnetic couplings between manganese species themselves. Our results show that in this narrow band half-doped manganite, size reduction only modifies the balance between the Anderson superexchange and Zener double exchange interactions, without destabilizing an overall very robust antiferromagnetic state. View this paper
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Review
Smart Industrial Robot Control Trends, Challenges and Opportunities within Manufacturing
Appl. Sci. 2022, 12(2), 937; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020937 - 17 Jan 2022
Cited by 4 | Viewed by 1245
Abstract
Industrial robots and associated control methods are continuously developing. With the recent progress in the field of artificial intelligence, new perspectives in industrial robot control strategies have emerged, and prospects towards cognitive robots have arisen. AI-based robotic systems are strongly becoming one of [...] Read more.
Industrial robots and associated control methods are continuously developing. With the recent progress in the field of artificial intelligence, new perspectives in industrial robot control strategies have emerged, and prospects towards cognitive robots have arisen. AI-based robotic systems are strongly becoming one of the main areas of focus, as flexibility and deep understanding of complex manufacturing processes are becoming the key advantage to raise competitiveness. This review first expresses the significance of smart industrial robot control in manufacturing towards future factories by listing the needs, requirements and introducing the envisioned concept of smart industrial robots. Secondly, the current trends that are based on different learning strategies and methods are explored. Current computer-vision, deep reinforcement learning and imitation learning based robot control approaches and possible applications in manufacturing are investigated. Gaps, challenges, limitations and open issues are identified along the way. Full article
(This article belongs to the Special Issue Smart Robots for Industrial Applications)
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Article
Total Phenolic, Anthocyanins HPLC-DAD-MS Determination and Antioxidant Capacity in Black Grape Skins and Blackberries: A Comparative Study
Appl. Sci. 2022, 12(2), 936; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020936 - 17 Jan 2022
Cited by 2 | Viewed by 493
Abstract
Anthocyanins are flavonoids with an antioxidant effect. They are the pigments that give rich colours to berries, red onions, pomegranates, and grapes. In addition to acting as antioxidants and fighting free radicals, anthocyanins may offer anti-inflammatory, anti-viral, and anti-cancer benefits. Among various types [...] Read more.
Anthocyanins are flavonoids with an antioxidant effect. They are the pigments that give rich colours to berries, red onions, pomegranates, and grapes. In addition to acting as antioxidants and fighting free radicals, anthocyanins may offer anti-inflammatory, anti-viral, and anti-cancer benefits. Among various types of fruits, blackberries and grapes are distinguished by their rich content in polyphenols, including anthocyanins. The purpose of this study was the identification and quantification of the anthocyanins in black grape skins and blackberries, but also the determination of the total phenolic content and total antioxidant capacity. The grape skins and blackberry extracts were prepared by an ultrasound-assisted acidified ethanol and methanol extraction method, with the 80% methanol solution being the most effective. Alcoholic extracts of blackberries and grape skins were analysed by the HPLC-DAD-MS method. There were five glycosylated anthocyanin compounds in blackberries, eight glycosylated anthocyanins compounds, and seven fragments of anthocyanin derivatives in grape skins identified. It was concluded that the anthocyanin profile of blackberries and grapes revealed mainly anthocyanin monoglycosides and acetylglycosides. Cyanidin-3-glucoside was the main component (86.49%) in blackberries, while, in the grape skins, the main component was delphinidin-3-O-glucoside (about 40.64%). Principal component analysis (PCA) was carried out on the basis of the 13 identified compounds in order to separate the extracts and describe the anthocyanins characteristics of different groups, the findings being in agreement with the experimental results. Compared to methanol extracts, ethanol extracts showed higher antioxidant activity, being related to the total phenolic content for the blackberries. Overall, the obtained results indicated that the blackberries and grapes skins possessed a high antioxidant content, similar to other berries, highlighting their potential use as fresh functional foods or fruit-derived products. Full article
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Review
Journey to the Market: The Evolution of Biodegradable Drug Delivery Systems
Appl. Sci. 2022, 12(2), 935; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020935 - 17 Jan 2022
Cited by 1 | Viewed by 580
Abstract
Biodegradable polymers have been used as carriers in drug delivery systems for more than four decades. Early work used crude natural materials for particle fabrication, whereas more recent work has utilized synthetic polymers. Applications include the macroscale, the microscale, and the nanoscale. Since [...] Read more.
Biodegradable polymers have been used as carriers in drug delivery systems for more than four decades. Early work used crude natural materials for particle fabrication, whereas more recent work has utilized synthetic polymers. Applications include the macroscale, the microscale, and the nanoscale. Since pioneering work in the 1960’s, an array of products that use biodegradable polymers to encapsulate the desired drug payload have been approved for human use by international regulatory agencies. The commercial success of these products has led to further research in the field aimed at bringing forward new formulation types for improved delivery of various small molecule and biologic drugs. Here, we review recent advances in the development of these materials and we provide insight on their drug delivery application. We also address payload encapsulation and drug release mechanisms from biodegradable formulations and their application in approved therapeutic products. Full article
(This article belongs to the Special Issue Polymeric Nanoparticles in Drug Delivery)
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Article
Numerical Investigation of the Temporal Contrast in ps-OPCPA with Compact Double BBO Arrangement
Appl. Sci. 2022, 12(2), 934; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020934 - 17 Jan 2022
Viewed by 321
Abstract
The picosecond optical parametric chirped pulse amplifier (ps-OPCPA) with double BBO arrangement can support the ultrabroad spectrum even under a relatively long pump pulse duration (∼100 ps). In this work, five-wave-coupled equations taking into consideration different phase matching conditions between the parametric superfluorescence [...] Read more.
The picosecond optical parametric chirped pulse amplifier (ps-OPCPA) with double BBO arrangement can support the ultrabroad spectrum even under a relatively long pump pulse duration (∼100 ps). In this work, five-wave-coupled equations taking into consideration different phase matching conditions between the parametric superfluorescence (PSF) and the signal are proposed to investigate the temporal contrast in ps-OPCPA schemes. Both the temporal contrast and the amplified spectrum are numerically analyzed in double BBO arrangements with four phase matching conditions. Numerical results show that the high temporal contrast and ultrabroad spectrum can be simultaneously realized by choosing the proper phase matching geometry in a double BBO arrangement. The numerical investigation here relaxes the requirement of very short pump pulses in ps-OPCPA, which can provide beneficial guidance for the design and construction of ps-OPCPA. Full article
(This article belongs to the Section Optics and Lasers)
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Article
Lignocellulosic Materials Used as Biosorbents for the Capture of Nickel (II) in Aqueous Solution
Appl. Sci. 2022, 12(2), 933; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020933 - 17 Jan 2022
Viewed by 305
Abstract
Four lignocellulosic materials (walnut shell, chestnut shell, pine wood and burnt pine wood) were analyzed as biosorbents to remove nickel ions in aqueous solution. The optimal pH condition was determined. Due to this, a range of different pHs (3.0 to 7.5) was tested. [...] Read more.
Four lignocellulosic materials (walnut shell, chestnut shell, pine wood and burnt pine wood) were analyzed as biosorbents to remove nickel ions in aqueous solution. The optimal pH condition was determined. Due to this, a range of different pHs (3.0 to 7.5) was tested. The adsorption isotherms and kinetics were established. To plot Langmuir and Freundlich isotherms, batch adsorption tests were made with variable nickel concentrations (5 to 200 mg L−1). The pseudo-first order, pseudo-second order, Elovich and intraparticle diffusion models were used to describe the kinetics, batch adsorption tests were carried out with 25 mg L−1 of nickel solution and agitation time varied from 10 to 1440 min. The specific surface area of the different materials was between 3.97 and 4.85 m2g−1 with the exception for wood with 1.74 m2g−1. The pore size was 26.54 nm for wood and varied between 5.40 and 7.33 nm for the remaining materials. The diffractograms analysis showed that all the lignocellulosic materials presented some crystalline domains with the exception of burnt pine wood which was completely amorphous. The best pH was found to be around 5.0. At this pH the adsorption was higher for chestnut shells, walnut shells, burnt pine wood and wood, respectively. All samples fitted the Langmuir model well, with R2 of 0.994 to 0.998. The sorption kinetics was well described by the pseudo-second order equation with R2 between 0.996 and 1.00. No significative differences on the surface of the materials before and after adsorption could be observed by SEM. Finally, all materials tested were able to remove nickel ions in aqueous solution. Full article
(This article belongs to the Special Issue Sustainable Urban Facilities)
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Article
Switchable Chiral Metasurface for Terahertz Anomalous Reflection Based on Phase Change Material
Appl. Sci. 2022, 12(2), 932; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020932 - 17 Jan 2022
Viewed by 412
Abstract
A switchable chiral metasurface based on a phase change material Ge2Sb2Te5, which can switch between a right-handed circularly polarized mirror and a left-handed circularly polarized mirror, is theoretically discussed. When the conductivity of Ge2Sb2 [...] Read more.
A switchable chiral metasurface based on a phase change material Ge2Sb2Te5, which can switch between a right-handed circularly polarized mirror and a left-handed circularly polarized mirror, is theoretically discussed. When the conductivity of Ge2Sb2Te5 σ is 0 S/m, the metasurface will reflect incident right-handed circularly polarized light and absorb incident left-handed circularly polarized light at 0.76 THz. As σ is set to 3 × 105 S/m, the response of the metasurface to circularly polarized light will be reversed. That is, it reflects the incident left-handed circularly polarized light and absorbs the incident right-handed circularly polarized light at 0.66 THz. The circular dichroism is from 76% to −64%. Then, we also study the performance of the mirror structure of the initial metasurface. By simulating the reflected spectra with different conductivities and the surface current distribution, the reason for the switchable function is clear. Moreover, the switchable chiral metasurface can be applied in spin-selective beam deflectors, which is proven by simulation. This work provides a new strategy for the development of tunable chiral devices. Full article
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Article
Human Activity Classification Using the 3DCNN Architecture
Appl. Sci. 2022, 12(2), 931; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020931 - 17 Jan 2022
Cited by 1 | Viewed by 453
Abstract
Interest in utilizing neural networks in a variety of scientific and academic studies and in industrial applications is increasing. In addition to the growing interest in neural networks, there is also a rising interest in video classification. Object detection from an image is [...] Read more.
Interest in utilizing neural networks in a variety of scientific and academic studies and in industrial applications is increasing. In addition to the growing interest in neural networks, there is also a rising interest in video classification. Object detection from an image is used as a tool for various applications and is the basis for video classification. Identifying objects in videos is more difficult than for single images, as the information in videos has a time continuity constraint. Common neural networks such as ConvLSTM (Convolutional Long Short-Term Memory) and 3DCNN (3D Convolutional Neural Network), as well as many others, have been used to detect objects from video. Here, we propose a 3DCNN for the detection of human activity from video data. The experimental results show that the optimized proposed 3DCNN provides better results than neural network architectures for motion, static and hybrid features. The proposed 3DCNN obtains the highest recognition precision of the methods considered, 87.4%. In contrast, the neural network architectures for motion, static and hybrid features achieve precisions of 65.4%, 63.1% and 71.2%, respectively. We also compare results with previous research. Previous 3DCNN architecture on database UCF Youtube Action worked worse than the architecture we proposed in this article, where the achieved result was 29%. The experimental results on the UCF YouTube Action dataset demonstrate the effectiveness of the proposed 3DCNN for recognition of human activity. For a more complex comparison of the proposed neural network, the modified UCF101 dataset, full UCF50 dataset and full UCF101 dataset were compared. An overall precision of 82.7% using modified UCF101 dataset was obtained. On the other hand, the precision using full UCF50 dataset and full UCF101 dataset was 80.6% and 78.5%, respectively. Full article
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Article
A CSI-Based Multi-Environment Human Activity Recognition Framework
Appl. Sci. 2022, 12(2), 930; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020930 - 17 Jan 2022
Viewed by 479
Abstract
Passive human activity recognition (HAR) systems, in which no sensors are attached to the subject, provide great potentials compared to conventional systems. One of the recently used techniques showing tremendous potential is channel state information (CSI)-based HAR systems. In this work, we present [...] Read more.
Passive human activity recognition (HAR) systems, in which no sensors are attached to the subject, provide great potentials compared to conventional systems. One of the recently used techniques showing tremendous potential is channel state information (CSI)-based HAR systems. In this work, we present a multi-environment human activity recognition system based on observing the changes in the CSI values of the exchanged wireless packets carried by OFDM subcarriers. In essence, we introduce a five-stage CSI-based human activity recognition approach. First, the acquired CSI values associated with each recorded activity instance are processed to remove the existing noise from the recorded data. A novel segmentation algorithm is then presented to identify and extract the portion of the signal that contains the activity. Next, the extracted activity segment is processed using the procedure proposed in the first stage. After that, the relevant features are extracted, and the important features are selected. Finally, the selected features are used to train a support vector machine (SVM) classifier to identify the different performed activities. To validate the performance of the proposed approach, we collected data in two different environments. In each of the environments, several activities were performed by multiple subjects. The performed experiments showed that our proposed approach achieved an average activity recognition accuracy of 91.27%. Full article
(This article belongs to the Special Issue Machine Learning and Signal Processing for IOT Applications)
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Article
Adaptive Cruise Control with Look-Ahead Anticipation for Driving on Freeways
Appl. Sci. 2022, 12(2), 929; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020929 - 17 Jan 2022
Cited by 1 | Viewed by 367
Abstract
This paper presents Adaptive Cruise Control (ACC) with look-ahead anticipation, based on the model of ACC used in recent commercial vehicles, to take early decisions in driving a vehicle on the freeway. The existing ACC found in the high-end cars has limited operating [...] Read more.
This paper presents Adaptive Cruise Control (ACC) with look-ahead anticipation, based on the model of ACC used in recent commercial vehicles, to take early decisions in driving a vehicle on the freeway. The existing ACC found in the high-end cars has limited operating range as it often fails to respond smoothly in advance behind a decelerating vehicle. Although advanced techniques, such as model predictive control (MPC), can significantly improve a vehicle’s driving performance, they are associated with high computational complexity and have limited scopes for practical implementation. The proposed look-ahead anticipatory scheme of ACC predicts the relative states of the preceding vehicle using a conditional persistence prediction technique in an adaptive short horizon. With negligible computation cost, it determines the control input using parametric functions prudently for improving driving performance. The proposed scheme is evaluated on multiple vehicles in typical traffic scenarios to examine individual driving behavior and the stability of a vehicle string. Finally, we investigate the influences of a small part of vehicles with the proposed ACC on overall traffic using the AIMSUN traffic simulator and compare performances of overall traffic. Full article
(This article belongs to the Section Transportation and Future Mobility)
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Article
Analysis of Fault Conditions in the Production of Prestressed Concrete Sleepers
Appl. Sci. 2022, 12(2), 928; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020928 - 17 Jan 2022
Viewed by 313
Abstract
Industries demand that their products are high quality with the least number of defects due to the rapid improvement in manufacturing technology. Quality is a critical criterion for evaluation in manufacturing firms. The production of a final product that can meet customer requirements [...] Read more.
Industries demand that their products are high quality with the least number of defects due to the rapid improvement in manufacturing technology. Quality is a critical criterion for evaluation in manufacturing firms. The production of a final product that can meet customer requirements is essential in a sustainable supply chain system to reduce costs, increase productivity and provide high-quality products. The aim of the study is to identify the root cause of defects emerging in the production process of prestressed railway concrete sleepers. Ishikawa diagram and Pareto analysis were used to identify the root cause. The results showed that the cause of the faulty concrete sleeper is the breaking of the bolts, which are supplied by the external provider. Since the supplier refused to accept the complaint, chemical analysis and measuring of hardness and microhardness of bolts were realized. It showed that the hardness of the bolts does not achieve the values, which should be achieved after the declared heat-treatment. As a corrective action, the input control of bolts hardness was proposed as well as establishing the team cooperating with the supplier to improve the heat treatment process, which will be the objective of further study. Full article
(This article belongs to the Special Issue Mechanical and Biomedical Engineering in Paradigm)
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Article
Non-Destructive Porosity Measurements of 3D Printed Polymer by Terahertz Time-Domain Spectroscopy
Appl. Sci. 2022, 12(2), 927; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020927 - 17 Jan 2022
Viewed by 425
Abstract
The porosity and inhomogeneity of 3D printed polymer samples were examined using terahertz time-domain spectroscopy, and the effects of 3D printer settings were analysed. A set of PETG samples were 3D printed by systematically varying the printer parameters, including layer thickness, nozzle diameter, [...] Read more.
The porosity and inhomogeneity of 3D printed polymer samples were examined using terahertz time-domain spectroscopy, and the effects of 3D printer settings were analysed. A set of PETG samples were 3D printed by systematically varying the printer parameters, including layer thickness, nozzle diameter, filament (line) thickness, extrusion, and printing pattern. Their effective refractive indices and loss coefficients were measured and compared with those of solid PETG. Porosity was calculated from the refractive index. A diffraction feature was observed in the loss spectrum of all 3D printed samples and was used as an indication of inhomogeneity. A “sweet spot” of printer settings was found, where porosity and inhomogeneity were minimised. Full article
(This article belongs to the Special Issue Terahertz Applications for Nondestructive Testing)
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Article
A Novel Narrowband Active Noise Control System with Online Secondary Path Modeling Based on Factor Decomposition and Application in Open Space
Appl. Sci. 2022, 12(2), 926; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020926 - 17 Jan 2022
Viewed by 329
Abstract
Due to the complexity of the coupling between the active noise control (ANC) controller and secondary path estimator, performance analysis of the system becomes particularly difficult. At present, the performance analysis of the system is often based on the fact that the secondary [...] Read more.
Due to the complexity of the coupling between the active noise control (ANC) controller and secondary path estimator, performance analysis of the system becomes particularly difficult. At present, the performance analysis of the system is often based on the fact that the secondary path tends to be stable, and the secondary path fitting error is minimal. However, in the early stage of system operation, or when the secondary path changes suddenly, the secondary path fitting error is significant, which easily causes divergence of the system control. It is still unable to guarantee the step-size bounds of convergence stability. Therefore, factor decomposition was used to analyze the mean weight behavior in this study. This strategy emphasizes the influence of secondary path modeling (SPM) error. The mean square behavior was evaluated using the energy conservation relationship. According to the established theoretical model, the convergence condition of the system was derived and the upper bound of step size suitable for all stages of system operation was obtained. The simulation and experimental results show that the ANC system is quite stable and robust under extreme conditions and has an obvious noise reduction effect in a specific range of open space, which can reach about 20 dB noise reduction. Full article
(This article belongs to the Special Issue Noise Pollution and Environmental Sustainability)
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Article
Feature Importance Analysis for Postural Deformity Detection System Using Explainable Predictive Modeling Technique
Appl. Sci. 2022, 12(2), 925; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020925 - 17 Jan 2022
Viewed by 370
Abstract
This study aimed to analyze feature importance by applying explainable artificial intelligence (XAI) to postural deformity parameters extracted from a computer vision-based posture analysis system (CVPAS). Overall, 140 participants were screened for CVPAS and enrolled. The main data analyzed were shoulder height difference [...] Read more.
This study aimed to analyze feature importance by applying explainable artificial intelligence (XAI) to postural deformity parameters extracted from a computer vision-based posture analysis system (CVPAS). Overall, 140 participants were screened for CVPAS and enrolled. The main data analyzed were shoulder height difference (SHD), wrist height difference (WHD), and pelvic height difference (PHD) extracted using a CVPAS. Standing X-ray imaging and radiographic assessments were performed. Predictive modeling was implemented with XGBoost, random forest regressor, and logistic regression using XAI techniques for global and local feature analyses. Correlation analysis was performed between radiographic assessment and AI evaluation for PHD, SHD, and Cobb angle. Main global features affecting scoliosis were analyzed in the order of importance for PHD (0.18) and ankle height difference (0.06) in predictive modeling. Outstanding local features were PHD, WHD, and KHD that predominantly contributed to the increase in the probability of scoliosis, and the prediction probability of scoliosis was 94%. When the PHD was >3 mm, the probability of scoliosis increased sharply to 85.3%. The paired t-test result for AI and radiographic assessments showed that the SHD, Cobb angle, and scoliosis probability were significant (p < 0.05). Feature importance analysis using XAI to postural deformity parameters extracted from a CVPAS is a useful clinical decision support system for the early detection of posture deformities. PHD was a major parameter for both global and local analyses, and 3 mm was a threshold for significantly increasing the probability of local interpretation of each participant and the prediction of postural deformation, which leads to the prediction of participant-specific scoliosis. Full article
(This article belongs to the Topic Artificial Intelligence in Healthcare)
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Article
Multi-Relational Graph Convolution Network for Service Recommendation in Mashup Development
by and
Appl. Sci. 2022, 12(2), 924; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020924 - 17 Jan 2022
Viewed by 312
Abstract
With the rapid development of service-oriented computing, an overwhelming number of web services have been published online. Developers can create mashups that combine one or multiple services to meet complex business requirements. To speed up the mashup development process, recommending suitable services for [...] Read more.
With the rapid development of service-oriented computing, an overwhelming number of web services have been published online. Developers can create mashups that combine one or multiple services to meet complex business requirements. To speed up the mashup development process, recommending suitable services for developers is a vital problem. In this paper, we address the data sparsity and cold-start problems faced in service recommendation, and propose a novel multi-relational graph convolutional network framework (MRGCN) for service recommendation. Specifically, we first construct a multi-relational mashup-service graph with three types of relations, namely composition relation, functional relation, and tagging relation. These three relations are indispensable and complement each other for capturing multi-view information. Then, the three relations in the graph are seamlessly fused with various strategies. Next, graph convolution is performed on the fused multi-relational graph to capture the high-order relational information between mashups and services. Finally, the relevance between mashup requirements and services is predicted based on the learned features on the graph. We conduct extensive experiments on the ProgrammableWeb dataset and demonstrate that our proposed method can outperform state-of-the-art methods in recommending services when only mashup requirements are available. Full article
(This article belongs to the Special Issue Smart Service Technology for Industrial Applications)
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Article
An Enhanced Discrete Element Modeling Method Considering Spatiotemporal Correlations for Investigating Deformations and Failures of Jointed Rock Slopes
Appl. Sci. 2022, 12(2), 923; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020923 - 17 Jan 2022
Viewed by 284
Abstract
The discrete element method (DEM) is commonly employed to analyze the deformations and failures of jointed rock slopes. However, when the iterative calculation process of the DEM modeling should be terminated is still unclear. To solve the above problem, in this paper, a [...] Read more.
The discrete element method (DEM) is commonly employed to analyze the deformations and failures of jointed rock slopes. However, when the iterative calculation process of the DEM modeling should be terminated is still unclear. To solve the above problem, in this paper, a discrete element modeling method based on the energy correlation coefficient is proposed to determine when the iterative calculation process could be terminated, and then applied the proposed method to analyze the deformations and failures of jointed rock slopes. Compared with the existing discrete element modeling method based on the displacement variation coefficient, the proposed method based on the energy correlation coefficient is much more applicable for jointed rock slopes. The main advantage of the proposed method is that there is no need to determine the position of the potential sliding surface, and the displacements of all blocks are no longer counted as statistics, but the spatiotemporal correlations between all blocks are considered. The effectiveness of the proposed method is verified by comparing with the existing method based on the displacement variation coefficient for an abbreviated jointed rock slope. Moreover, the proposed method is successfully applied to analyze a real-world jointed rock slope without an obvious potential sliding surface in which the existing method cannot work. Full article
(This article belongs to the Special Issue Advanced Numerical Simulations in Geotechnical Engineering)
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Article
Combining Yoga Exercise with Rehabilitation Improves Balance and Depression in Patients with Chronic Stroke: A Controlled Trial
Appl. Sci. 2022, 12(2), 922; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020922 - 17 Jan 2022
Cited by 1 | Viewed by 447
Abstract
Background: We combined yoga with standard stroke rehabilitation and compared it to the rehabilitation alone for depression and balance in patients. Methods: Forty patients aged from 30 to 80 who had suffered a stroke 90 or more days previously were divided evenly with [...] Read more.
Background: We combined yoga with standard stroke rehabilitation and compared it to the rehabilitation alone for depression and balance in patients. Methods: Forty patients aged from 30 to 80 who had suffered a stroke 90 or more days previously were divided evenly with age stratification and patients’ will (hence not randomized). In the intervention group 16 completed 8-week stroke rehabilitation combined with 1 h of yoga twice weekly. Another 19 patients completed the standard rehabilitation as the control group. Results: The yoga group showed significant improvement in depression (Taiwanese Depression Questionnaire, p = 0.002) and balance (Berg Balance Scale, p < 0.001). However, the control group showed improvement only in balance (p = 0.001) but not in depression (p = 0.181). Further analysis showed both sexes benefitted in depression, but men had a greater improvement in balance than women. Depression in left-brain lesion patients improved more significantly than in those with right-brain lesion, whereas balance improved equally despite lesion site. For patients under or above the age of 60, depression and balance both significantly improved after rehabilitation. Older age is significantly related to poor balance but not depression. Conclusions: Combining yoga with rehabilitation has the potential to improve depression and balance. Factors related to sex, brain lesion site and age may influence the differences. Full article
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Article
Numerical Investigation into Lateral Behavior of Monopile Due to Scour Enhanced: Role of State-Dependent Dilatancy
Appl. Sci. 2022, 12(2), 921; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020921 - 17 Jan 2022
Viewed by 322
Abstract
The removal of soil during scouring is crucial to the lateral resistance of piles in bridges of railways or highways. In this process, dilatancy of the interface soil induces variation in normal stress, which in turn influences the interface soil lateral resistance. Due [...] Read more.
The removal of soil during scouring is crucial to the lateral resistance of piles in bridges of railways or highways. In this process, dilatancy of the interface soil induces variation in normal stress, which in turn influences the interface soil lateral resistance. Due to the lack of analysis in previous studies in terms of cohesionless soil state (i.e., relative density and stress level) in remaining soil after scouring, it is difficult to simulate the properties and behavior of interface soil. The objective of this study is to explain the change of the sand state at the compression interface after scouring, quantify the stress-strain characteristics of the pile during this period and eventually present the prediction p-y curves of the lateral service capacity. The state-dependent constitutive model for saturated sand is employed, combined with the 3D finite element simulation, and the state development of the remaining soil is exhibited. The enhancement of dilation and stress relief of the remaining shallow horizon eventually gives rise to the reduction of the lateral resistance. In addition, the remaining overburden soil surrounding the pile restricts the interface soil, enlarging the normal stress and strengthening the deep horizon. Then, the friction angle considered the influence of state-dependent changes is used to quantify the hyperbolic p-y curves. Full article
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Article
An Activity Theory-Based Approach for Context Analysis, Design and Evolution
Appl. Sci. 2022, 12(2), 920; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020920 - 17 Jan 2022
Cited by 1 | Viewed by 245
Abstract
This paper presents a new interdisciplinary approach to support context modeling in context-awareness software developments. The premise of this approach relies on the idea that understanding a complex socio-technical ecology, while adapting the software to its behavior and evolution, is a primary challenge [...] Read more.
This paper presents a new interdisciplinary approach to support context modeling in context-awareness software developments. The premise of this approach relies on the idea that understanding a complex socio-technical ecology, while adapting the software to its behavior and evolution, is a primary challenge to address. Thus, the paper proposes an activity theory-based approach to aid in the conception, design, development, and evolution of emerging context-aware socio-technical ecologies. The concepts and notations used by the proposed approach are illustrated through a proof of concept that demonstrates the essential ideas and their use in real scenarios. Also, the feasibility of this approach is measured empirically through an experiment. Preliminary results show how, for a context-aware software design and development team, the proposal provides a better understanding of context than alternatives and helps to outline context models by establishing relationships and interactions between socio-technical components and by anticipating potential conflicts among them. The key ideas of the proposed approach result in the ability to analyze and model social and technological contexts around perpetually evolving system ecologies as useful representations for understanding operating environments closely tied to human actions, with software as a mediator component. Full article
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Article
Different Scales of Medical Data Classification Based on Machine Learning Techniques: A Comparative Study
Appl. Sci. 2022, 12(2), 919; https://doi.org/10.3390/app12020919 - 17 Jan 2022
Cited by 1 | Viewed by 350
Abstract
In recent years, medical data have vastly increased due to the continuous generation of digital data. The different forms of medical data, such as reports, textual, numerical, monitoring, and laboratory data generate the so-called medical big data. This paper aims to find the [...] Read more.
In recent years, medical data have vastly increased due to the continuous generation of digital data. The different forms of medical data, such as reports, textual, numerical, monitoring, and laboratory data generate the so-called medical big data. This paper aims to find the best algorithm which predicts new medical data with high accuracy, since good prediction accuracy is essential in medical fields. To achieve the study’s goal, the best accuracy algorithm and least processing time algorithm are defined through an experiment and comparison of seven different algorithms, including Naïve bayes, linear model, regression, decision tree, random forest, gradient boosted tree, and J48. The conducted experiments have allowed the prediction of new medical big data that reach the algorithm with the best accuracy and processing time. Here, we find that the best accuracy classification algorithm is the random forest with accuracy values of 97.58%, 83.59%, and 90% for heart disease, M-health, and diabetes datasets, respectively. The Naïve bayes has the lowest processing time with values of 0.078, 7.683, and 22.374 s for heart disease, M-health, and diabetes datasets, respectively. In addition, the best result of the experiment is obtained by the combination of the CFS feature selection algorithm with the Random Forest classification algorithm. The results of applying RF with the combination of CFS on the heart disease dataset are as follows: Accuracy of 90%, precision of 83.3%, sensitivity of 100, and consuming time of 3 s. Moreover, the results of applying this combination on the M-health dataset are as follows: Accuracy of 83.59%, precision of 74.3%, sensitivity of 93.1, and consuming time of 13.481 s. Furthermore, the results on the diabetes dataset are as follows: Accuracy of 97.58%, precision of 86.39%, sensitivity of 97.14, and consuming time of 56.508 s. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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Article
Comparison Uncertainty of Different Types of Membership Functions in T2FLS: Case of International Financial Market
Appl. Sci. 2022, 12(2), 918; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020918 - 17 Jan 2022
Viewed by 277
Abstract
This article deals with the determination and comparison of different types of functions of the type-2 interval of fuzzy logic, using a case study on the international financial market. The model is demonstrated on the time series of the leading stock index DJIA [...] Read more.
This article deals with the determination and comparison of different types of functions of the type-2 interval of fuzzy logic, using a case study on the international financial market. The model is demonstrated on the time series of the leading stock index DJIA of the US market. Type-2 Fuzzy Logic membership features are able to include additional uncertainty resulting from unclear, uncertain or inaccurate financial data that are selected as inputs to the model. Data on the financial situation of companies are prone to inaccuracies or incomplete information, which is why the type-2 fuzzy logic application is most suitable for this type of financial analysis. This paper is primarily focused on comparing and evaluating the performance of different types of type-2 fuzzy membership functions with integrated additional uncertainty. For this purpose, several model situations differing in shape and level or degree of uncertainty of membership functions are constructed. The results of this research show that type-2 fuzzy sets with dual membership functions is a suitable expert system for highly chaotic and unstable international stock markets and achieves higher accuracy with the integration of a certain level of uncertainty compared to type-1 fuzzy logic. Full article
(This article belongs to the Special Issue Women in Artificial intelligence (AI))
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Article
Pulsed Laser Ablation: A Facile and Low-Temperature Fabrication of Highly Oriented n-Type Zinc Oxide Thin Films
Appl. Sci. 2022, 12(2), 917; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020917 - 17 Jan 2022
Viewed by 439
Abstract
Eco-friendly and facile zinc oxide (ZnO) synthesis of zinc-oxide-based nanomaterials with specific properties is a great challenge due to its excellent industrial applications in the field of semiconductors and solar cells. In this paper, we report the production of zinc oxide thin films [...] Read more.
Eco-friendly and facile zinc oxide (ZnO) synthesis of zinc-oxide-based nanomaterials with specific properties is a great challenge due to its excellent industrial applications in the field of semiconductors and solar cells. In this paper, we report the production of zinc oxide thin films at relatively low deposition temperature employing a simple and non-toxic method at low substrate temperature: pulsed laser ablation, as a first step for developing a n-ZnO/p-Si heterojunction. Single-phase n-type zinc oxide thin films are confirmed by an X-ray diffraction (XRD) pattern revealed by the maximum diffraction intensity from the (002) plane. Absorbance measurements indicate an increase in the band gap energy close to the bulk ZnO. A 350 °C substrate temperature led to obtaining a highly porous film with high crystallinity and high bandgap, showing good premises for further applications. Full article
(This article belongs to the Special Issue Recent Advances in Application of Coatings and Films)
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Systematic Review
Upper Airway Changes in Diverse Orthodontic Looms: A Systematic Review and Meta-Analysis
Appl. Sci. 2022, 12(2), 916; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020916 - 17 Jan 2022
Viewed by 382
Abstract
Upper airway assessment is particularly important in the daily work of orthodontists, because of its close connection with the development of craniofacial structures and with other pathologies such as Obstructive Sleep Apnea Syndrome (OSAS). Three-dimensional cone-beam computed tomography images provide a more reliable [...] Read more.
Upper airway assessment is particularly important in the daily work of orthodontists, because of its close connection with the development of craniofacial structures and with other pathologies such as Obstructive Sleep Apnea Syndrome (OSAS). Three-dimensional cone-beam computed tomography images provide a more reliable and comprehensive tool for airway assessment and volumetric measurements. However, the association between upper airway dimensions and skeletal malocclusion is unclear. Therefore, the current systematic review evaluates the effects of different surgical movements on the upper airway. Materials and Methods: Medline (PubMed, OVID Medline, and EBSCO), Cochrane Library (Cochrane Review and Trails), Web of Knowledge (social science, and conference abstracts), Embase (European studies, pharmacological literature, and conference abstracts), CINAHL (nursing and allied health), PsycInfo (psychology and psychiatry), SCOPUS (conference abstracts, and scientific web pages), and ERIC (education) databases were searched. Two authors independently performed the literature search, selection, quality assessment, and data extraction. Inclusion criteria encompassed computed tomography evaluations of the upper airway spaces with retrospective, prospective, and randomised clinical trial study designs. To grade the methodological quality of the included studies a GRADE risk of bias tool was used. Results and conclusion: In total, 29 studies were included. Among these, 17 studies had a low risk of bias, whereas 10 studies had a moderate risk of bias. A meta-analysis was performed with the mean differences using a fixed-effects model. Heterogeneity was assessed with the Q-test and the I2 index. The meta-analysis revealed significant (p ≤ 0.001, 95% confidence interval) increases in upper airway volume after rapid maxillary expansion and surgical advancement for the correction of Class II. Full article
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Article
An Interactive Scholarly Collaborative Network Based on Academic Relationships and Research Collaborations
Appl. Sci. 2022, 12(2), 915; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020915 - 17 Jan 2022
Viewed by 284
Abstract
In this era of digital transformation, when the amount of scholarly literature is rapidly growing, hundreds of papers are published online daily with regard to different fields, especially in relation to academic subjects. Therefore, it difficult to find an expert/author to collaborate with [...] Read more.
In this era of digital transformation, when the amount of scholarly literature is rapidly growing, hundreds of papers are published online daily with regard to different fields, especially in relation to academic subjects. Therefore, it difficult to find an expert/author to collaborate with from a specific research area. This is thought to be one of the most challenging activities in academia, and few people have considered authors’ multi-factors as an enhanced method to find potential collaborators or to identify the expert among them; consequently, this research aims to propose a novel model to improve the process of recommending authors. This is based on the authors’ similarity measurements by extracting their explicit and implicit topics of interest from their academic literature. The proposed model mainly consists of three factors: author-selected keywords, the extraction of a topic’s distribution from their publications, and their publication-based statistics. Furthermore, an enhanced approach for identifying expert authors by extracting evidence of expertise has been proposed based on the topic-modeling principle. Subsequently, an interactive network has been constructed that represents the predicted authors’ collaborative relationship, including the top-k potential collaborators for each individual. Three experiments have been conducted on the collected data; they demonstrated that the most influential factor for accurately recommending a collaborator was the topic’s distribution, which had an accuracy rate of 88.4%. Future work could involve building a heterogeneous co-collaboration network that includes both the authors with their affiliations and computing their similarities. In addition, the recommendations would be improved if potential and real collaborations were combined in a single network. Full article
(This article belongs to the Special Issue Deep Learning from Multi-Sourced Data)
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Article
Latent-Cause Extraction Model in Maritime Collision Accidents Using Text Analytics on Korean Maritime Accident Verdicts
Appl. Sci. 2022, 12(2), 914; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020914 - 17 Jan 2022
Viewed by 261
Abstract
Maritime collision accidents occur frequently and result in huge damages. Complex collision accidents are especially associated with worse damages. Complex maritime collision accidents involve other types of accidents barring the main accident, such as fire, explosions, capsizes, sinking, and even casualties. When a [...] Read more.
Maritime collision accidents occur frequently and result in huge damages. Complex collision accidents are especially associated with worse damages. Complex maritime collision accidents involve other types of accidents barring the main accident, such as fire, explosions, capsizes, sinking, and even casualties. When a maritime accident occurs, the maritime accident verdict covers the surveyed facts from the origin of the accident to the consequences. The survey usually reveals the primary cause of the accident; however, complex causes may remain latent. Therefore, this research aims to apply text analytics to maritime verdicts of collision accident cases to identify the latent causes in complex collision accidents. The proposed methods separated the collected corpus into the training dataset and the test dataset. The word propensity database was extracted from the training dataset and applied to sample verdicts of complex maritime collision accidents in the test dataset. The expected results of this research were words that appeared in only complex maritime accidents with a high propensity for additional categories and the relevant context that explains the latent causes that underlie the complexity of the maritime accident. The conclusion suggested that the latent causes derived should be provided to ships to help prevent future complex collision accidents. Full article
(This article belongs to the Special Issue Natural Language Processing: Approaches and Applications)
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Article
An Incremental Grey-Box Current Regression Model for Anomaly Detection of Resistance Mash Seam Welding in Steel Mills
Appl. Sci. 2022, 12(2), 913; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020913 - 17 Jan 2022
Cited by 1 | Viewed by 250
Abstract
Annealing and galvanization production lines in steel mills run continuously to maximize production throughput. As a part of this process, individual steel coils are joined end-to-end using mash seam welding. Weld breaks result in a production loss of multiple days, so non-destructive, data-driven [...] Read more.
Annealing and galvanization production lines in steel mills run continuously to maximize production throughput. As a part of this process, individual steel coils are joined end-to-end using mash seam welding. Weld breaks result in a production loss of multiple days, so non-destructive, data-driven techniques are used to detect and replace poor quality welds in real-time. Statistical models are commonly used to address this problem as they use data readily available from the welding machine and require no specialized equipment. While successful in finding anomalies, these statistical models do not provide insight into the underlying process and are slow to adapt to changes in the machine’s or material’s behavior. We combine knowledge-based and data-driven techniques to create an incremental grey-box welding current prediction model for detecting anomalous welds, resulting in a powerful and interpretable model. In this work, we detail our approach and show evaluation results on industrial welding data collected over a period of 15 months containing behavioral shifts attributed to machine maintenance. Due to its incremental nature, our model resulted in two-thirds fewer rejected welds compared to statistical models, thus greatly reducing production overhead. Grey-box modeling can be applied to other welding features or domains and results in models that are more desirable for the industry. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Systems)
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Article
Phytochemical Analysis of Polyphenols in Leaf Extract from Vernonia amygdalina Delile Plant Growing in Uganda
Appl. Sci. 2022, 12(2), 912; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020912 - 17 Jan 2022
Viewed by 400
Abstract
Due to the presence of phytochemicals, plants have been known to be used in the treatment and management of various diseases. Vernonia amygdalina, belonging to the Asteraceae family, is a plant known for its many applications in traditional medicine for various purposes. [...] Read more.
Due to the presence of phytochemicals, plants have been known to be used in the treatment and management of various diseases. Vernonia amygdalina, belonging to the Asteraceae family, is a plant known for its many applications in traditional medicine for various purposes. Previous studies on the methanolic leaf extract of this plant have proved the antibacterial, cytotoxic, anticancer and antioxidant effects indicative of promising therapeutic potentials. In this work, chromatographic and spectroscopic techniques along with high-performance liquid chromatography quantitative analysis were adopted to isolate, identify and quantify polyphenolic compounds in V. amygdalina leaf extract. UHPLC-DAD-ESI-MS/MS and UHPLC-DAD methods were adopted for qualitative and quantitative analysis, respectively. In the case of polyphenol separation, some reference substances were isolated by preparative HPLC. Seven polyphenols were identified and quantified in this study: 5-O-caffeoylquinic acid, luteolin hexoside, 3,4-O-dicaffeoylquinic acid, 1,5-O-dicaffeoylquinic acid, 3,5-O-dicaffeoylquinic acid, 4,5-O-dicaffeoylquinic acid and luteolin dihexoside, with 3,5-O-dicaffeoylquinic acid being isolated in the highest quantity of 27.49 mg g−1 extract. Full article
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Article
Quantitative Evaluation of Light Collimating for Commercial UV-LEDs Based on Analytic Collimating Lens
Appl. Sci. 2022, 12(2), 911; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020911 - 17 Jan 2022
Viewed by 315
Abstract
This paper proposes a lens design method for effectively collimating the light emitting from a light-emitting diode (LED). This collimating lens contains two aspherical lens surfaces which can be mathematically characterized using a few designing parameters, and hence is called an analytic collimating [...] Read more.
This paper proposes a lens design method for effectively collimating the light emitting from a light-emitting diode (LED). This collimating lens contains two aspherical lens surfaces which can be mathematically characterized using a few designing parameters, and hence is called an analytic collimating lens. An optical ray-tracing algorithm has been developed for these analytic collimating lenses to analyze their optical performance and to optimize their designs. Six high-power and commercially available ultraviolet (UV) LEDs are chosen as examples for demonstrating the optimal collimating lens design. For each UV-LED, the corresponding optical collimating lens is determined by inputting the ray data file provided by the manufacture over a finite-size emitting area. The divergent angles of the six UV-LEDs have been successfully collimated to a narrow range in between 1.56° to 2.84° from their original radiation angle around 46° to 120°. Furthermore, the proposed analytical collimating lenses are suitable for mass-production using standard mold injection methods, and hence possess great potentials for industry applications of LEDs. Full article
(This article belongs to the Special Issue Optical Design and Engineering II)
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Article
Characterizing the Mechanical Performance of a Bare-Metal Stent with an Auxetic Cell Geometry
Appl. Sci. 2022, 12(2), 910; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020910 - 17 Jan 2022
Viewed by 324
Abstract
This study develops and characterizes the distinctive mechanical features of a stainless-steel metal stent with a tailored structure. A high-precision femtosecond laser was used to micromachine a stent with re-entrant hexagonal (auxetic) cell geometry. We then characterized its mechanical behavior under various mechanical [...] Read more.
This study develops and characterizes the distinctive mechanical features of a stainless-steel metal stent with a tailored structure. A high-precision femtosecond laser was used to micromachine a stent with re-entrant hexagonal (auxetic) cell geometry. We then characterized its mechanical behavior under various mechanical loadings using in vitro experiments and through finite element analysis. The stent properties, such as the higher capability of the stent to bear upon bending, exceptional advantage at elevated levels of twisting angles, and proper buckling, all ensured a preserved opening to maintain the blood flow. The outcomes of this preliminary study present a potential design for a stent with improved physiologically relevant mechanical conditions such as longitudinal contraction, radial strength, and migration of the stent. Full article
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Article
A Fluid Inclusion and Critical/Rare Metal Study of Epithermal Quartz-Stibnite Veins Associated with the Gerakario Porphyry Deposit, Northern Greece
Appl. Sci. 2022, 12(2), 909; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020909 - 17 Jan 2022
Viewed by 331
Abstract
The Gerakario Cu-Au porphyry deposit in the Kilkis ore district, northern Greece, contains epithermal quartz-stibnite veins on the eastern side of the deposit, which crosscut a two-mica gneiss. Metallic mineralization in these veins consists of stibnite + berthierite + native antimony + pyrite [...] Read more.
The Gerakario Cu-Au porphyry deposit in the Kilkis ore district, northern Greece, contains epithermal quartz-stibnite veins on the eastern side of the deposit, which crosscut a two-mica gneiss. Metallic mineralization in these veins consists of stibnite + berthierite + native antimony + pyrite + arsenopyrite, and minor marcasite, pyrrhotite, chalcopyrite, löllingite, and native gold. Bulk geochemical analyses of the ore reveal an enrichment in critical and rare metals, including Ag, Au, Bi, Ce, Co, Ga, La, and Sb. Analysis of stibnite with LA-ICP-MS showed an enrichment in base metals (As, Cu, Pb), as well as weak to moderate contents of critical and rare metals (Ag, Bi, Ce, La, Re, Sm, Th, Ti, Tl). A statistical analysis of the trace elements show a positive correlation for the elemental pairs Ce-La, Ce-Sb, and La-Sb, and a negative correlation for the pair Bi-Sb. Fluid inclusions in the A-type veins of the porphyry-style mineralization show the presence of fluid boiling, resulting in a highly saline aqueous fluid phase (35.7 to 45.6 wt.% NaCl equiv.) and a moderately saline gas phase (14 to 22 wt.% NaCl equiv.) in the system H2O-NaCl-KCl at temperatures varying between 380° and 460 °C and pressures from 100 to 580 bar. Mixing of the moderate saline fluid with meteoric water produced less saline fluids (8 to 10 wt.% NaCl equiv.), which are associated with the epithermal quartz-stibnite vein mineralization. This process took place under hydrostatic pressures ranging from 65 to 116 bar at a depth between 600 and 1000 m, and at temperatures mainly from 280° to 320 °C. Full article
(This article belongs to the Special Issue Mineralogy of Critical Elements Deposits)
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Article
The Effect of Terpenoid Compounds on the Formation of Advanced Glycation Endproducts (AGEs) in Model Systems
Appl. Sci. 2022, 12(2), 908; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020908 - 17 Jan 2022
Viewed by 284
Abstract
Background: Terpenoid compounds, despite their established antioxidant ability, are neglected as potential glycation regulators. Methods: In-vitro model systems of lysine (0.1 M) with glucose (0.1 M and 1 M) were incubated at 80 °C and 100 °C for 3 h in the presence [...] Read more.
Background: Terpenoid compounds, despite their established antioxidant ability, are neglected as potential glycation regulators. Methods: In-vitro model systems of lysine (0.1 M) with glucose (0.1 M and 1 M) were incubated at 80 °C and 100 °C for 3 h in the presence of aniseed oil, thymol and linalool (2–8 μΜ). Color development, absorbance at UV-Vis (280 nm, 360 nm, 420 nm), fluorescence intensity (λexc = 370 nm, λemm = 440 nm) and lysine depletion (HPLC-FL) were measured to monitor the progress of the Maillard reaction. Response Surface Methodology was used to analyze the impact of the five experimental conditions on the glycation indices. Results: All terpenoid compounds promoted color development and did not affect lysine depletion. The choice of terpenoid compound impacted glycation at 280 nm, 360 nm and 420 nm (p < 0.02). The effect was stronger at lower temperatures (p < 0.002) and 0.1 M glucose concentrations (p < 0.001). Terpenoid concentration was important only at 360 nm and 420 nm (p < 0.01). No impact was seen for fluorescence intensity from the choice of terpenoid compounds and their dose (p = 0.08 and p = 0.44 respectively). Conclusion: Terpenoid compounds show both anti- and proglycative activity based on the incubation conditions. Thymol showed the largest antiglycative capacity, followed by aniseed oil and linalool. Maximal antiglycative capacity was seen at 0.1 M glucose, 2 μΜ terpenoid concentration, 80 °C and 1 h incubation. Full article
(This article belongs to the Special Issue Nutraceuticals: Food and Nutritional Applications)
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