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Published By Mdpi Ag

2076-3417
Updated Friday, 17 September 2021

2021 ◽  
Vol 11 (18) ◽  
pp. 8560
Author(s):  
Sabrina Carroll ◽  
Joud Satme ◽  
Shadhan Alkharusi ◽  
Nikolaos Vitzilaios ◽  
Austin Downey ◽  
...  

This paper presents a novel method of procuring and processing data for the assessment of civil structures via vibration monitoring. This includes the development of a custom sensor package designed to minimize the size/weight while being fully self-sufficient (i.e., not relying on external power). The developed package is delivered to the structure utilizing a customized Unmanned Aircraft System (UAS), otherwise known as a drone. The sensor package features an electropermanent magnet for securing it to the civil structure while a second magnet is used to secure the package to the drone during flight. The novel B-Spline Impulse Response Function (BIRF) technique was utilized to extract the Dynamic Signature Response (DSR) from the data collected by the sensor package. Experimental results are presented to validate this method and show the feasibility of deploying the sensor package on structures and collecting data valuable for Structural Health Monitoring (SHM) data processing. The advantages and limitations of the proposed techniques are discussed, and recommendations for further developments are made.


2021 ◽  
Vol 11 (18) ◽  
pp. 8561
Author(s):  
Sneha Samal ◽  
Ignazio Blanco

The movement of isotropic and anisotropic particles of iron and graphite within the polymer matrix was predicted and examined by the COMSOL simulation method. The interfacial adhesion of filler particles within the matrix was investigated under surface features observation. Carbonyl Iron (CI) particles, considered to be regular with a uniform size of (1–5 µm), were mixed with irregular particles of graphite (20–150 µm) with 30 V% in quantity in a silicone rubber matrix. The particle–matrix and particle–particle interactions were analyzed from the inner surface features. The drag of non-spherical particles and particle Reynolds numbers (Rep) were taken into consideration in point force models for both the Stokes (Rep ≪ 1) and Newton regime for particle shape. Newton regime is based on the aspect ratio for particles with regular and irregular shapes. The boundary area of the irregular particles holds like an anchor inside the polymer matrix for strong adhesion; however, regular particles have partial attachment due to the gravitational pull of attraction from the bottom contact points. However, uniform distribution of isotropic particles has been observed in comparison to the anisotropic particles within the polymer matrix.


2021 ◽  
Vol 11 (18) ◽  
pp. 8563
Author(s):  
Anesu Nyabadza ◽  
Mercedes Vázquez ◽  
Shirley Coyle ◽  
Brian Fitzpatrick ◽  
Dermot Brabazon

The use of flexible sensors has tripled over the last decade due to the increased demand in various fields including health monitoring, food packaging, electronic skins and soft robotics. Flexible sensors have the ability to be bent and stretched during use and can still maintain their electrical and mechanical properties. This gives them an advantage over rigid sensors that lose their sensitivity when subject to bending. Advancements in 3D printing have enabled the development of tailored flexible sensors. Various additive manufacturing methods are being used to develop these sensors including inkjet printing, aerosol jet printing, fused deposition modelling, direct ink writing, selective laser melting and others. Hydrogels have gained much attention in the literature due to their self-healing and shape transforming. Self-healing enables the sensor to recover from damages such as cracks and cuts incurred during use, and this enables the sensor to have a longer operating life and stability. Various polymers are used as substrates on which the sensing material is placed. Polymers including polydimethylsiloxane, Poly(N-isopropylacrylamide) and polyvinyl acetate are extensively used in flexible sensors. The most widely used nanomaterials in flexible sensors are carbon and silver due to their excellent electrical properties. This review gives an overview of various types of flexible sensors (including temperature, pressure and chemical sensors), paying particular attention to the application areas and the corresponding characteristics/properties of interest required for such. Current advances/trends in the field including 3D printing, novel nanomaterials and responsive polymers, and self-healable sensors and wearables will also be discussed in more detail.


2021 ◽  
Vol 11 (18) ◽  
pp. 8570
Author(s):  
Rodica Vârban ◽  
Ioana Crișan ◽  
Dan Vârban ◽  
Andreea Ona ◽  
Loredana Olar ◽  
...  

Plant fibers are sustainable sources of materials for many industries, and can be obtained from a variety of plants. Cellulose is the main constituent of plant-based fibers, and its properties give the characteristics of the fibers obtained. Detailed characterization of cellulosic fibers is often performed after lengthy extraction procedures, while fast screening might bring the benefit of quick qualitative assessment of unprocessed stems. The aim of this research was to define some marker spectral regions that could serve for fast, preliminary qualitative characterization of unprocessed stems from some textile plants through a practical and minimally invasive method without lengthy extraction procedures. This could serve as a screening method for sorting raw materials by providing an accurate overall fingerprint of chemical composition. For this purpose, we conducted comparative Fourier Transform Infrared Spectroscopy (FT-IR) prospecting for quality markers in stems of flax (Linum usitatissimum L.), velvet leaf (Abutilon theophrasti Medik.), hemp (Cannabis sativa L.) and jute (Corchorus olitorius L.). Analysis confirmed the presence of major components in the stems of the studied plants. Fingerprint regions for cellulose signals were attributed to bands at 1420–1428 cm−1 assigned to the crystalline region and 896–898 cm−1 assigned to the amorphous region of cellulose. The optimization of characterization methods for raw materials is important and can find immediate practical applications.


2021 ◽  
Vol 11 (18) ◽  
pp. 8569
Author(s):  
Heike Knicker ◽  
Marta Velasco-Molina ◽  
Michael Knicker

The chemistry and nature of biochars are still far from being well understood. In the present work, solid-state 2D HETCOR 1H-13C NMR spectroscopy is introduced for an improved characterization of the aromatic network in biochars. To that end, a pyrochar obtained from the pyrolysis of cellulose at 350 °C for 1 h was used as an example. Variation of the contact time during cross polarization from 50 µs, to 200 µs and 1000 µs gave information about the protonation degree of the different C groups and their interactions. We demonstrated that carbohydrates did not survive the used pyrolysis conditions. Therefore, O-alkyl C was assigned to ethers. Phenols were not identified to a higher extent suggesting that furan and benzofuran-type units determine the O-functionality of the aromatic domains. The latter are directly connected to alkyl chains. Those features are expected to affect chemical but also physical properties of the biochar. Based on our results, we developed a new concept describing the nature of the aromatic network in the studied cellulose-based pyrochars. The latter contrasts common views about the chemical nature of biochar, possibly because pyrolysis temperatures > 350 °C are required for achieving advanced condensation of the aromatic domains.


2021 ◽  
Vol 11 (18) ◽  
pp. 8574
Author(s):  
Michalis Savelonas ◽  
Ioannis Vernikos ◽  
Dimitris Mantzekis ◽  
Evaggelos Spyrou ◽  
Athanasia Tsakiri ◽  
...  

Monitoring driving behaviour is important in controlling driving risk, fuel consumption, and CO2 emissions. Recent advances in machine learning, which include several variants of convolutional neural networks (CNNs), and recurrent neural networks (RNNs), such as long short-term memory (LSTM) and gated recurrent unit (GRU) networks, could be valuable for the development of objective and efficient computational tools in this direction. The main idea in this work is to complement data-driven classification of driving behaviour with rules derived from domain knowledge. In this light, we present a hybrid representation approach, which employs NN-based time-series encoding and rule-guided event detection. Histograms derived from the output of these two components are concatenated, normalized, and used to train a standard support vector machine (SVM). For the NN-based component, CNN-based, LSTM-based, and GRU-based variants are investigated. The CNN-based variant uses image-like representations of sensor measurements, whereas the RNN-based variants (LSTM and GRU) directly process sensor measurements in the form of time-series. Experimental evaluation on three datasets leads to the conclusion that the proposed approach outperforms a state-of-the-art camera-based approaches in distinguishing between normal and aggressive driving behaviour without using data derived from a camera. Moreover, it is demonstrated that both NN-guided time-series encoding and rule-guided event detection contribute to overall classification accuracy.


2021 ◽  
Vol 11 (18) ◽  
pp. 8528
Author(s):  
Hichem Mrabet ◽  
Faouzi Bahloul ◽  
Abdullah S. Karar ◽  
Abdelhamid Cherifi ◽  
Aymen Belghith

A new architecture for Spectral Amplitude Coding Optical Code Division Multiple Access (SAC-OCDMA) system based on two Dimensional Multi Diagonal (2D-MD) codes named 2D-MD SAC-OCDMA and utilizing a laser optical source is proposed for Long-Reach Passive Optical Network (LR-PON). In this work, a computer simulator tool is used, for the first time, as a SAC-OCDMA simulation set-up utilizing the unique combination of a coherent laser array and 2D-MD codes. In addition, the system performance is addressed numerically by taking into account Multiple Access Interference (MAI), optical coherent source noise, first, second and third order fiber dispersion, nonlinear effects and photo-detector noise. Simulation results indicate that for a single user (i.e., without considering MAI), the system can operate at a maximum bit rate of 55 Gb/s over 250 km of Single Mode Fiber (SMF), with a Bit Error Rate (BER) below 10−9 (Q-limit = 15.5 dB), when only first order fiber dispersion is considered. However, including the effects of second and third order fiber dispersion as frequency domain parameters, results in a reduction of the maximum bit rate to 40 Gb/s, while maintaining a Q-factor above the Q-limit under the same transmission distance. Furthermore, we demonstrate that the proposed architecture extends the SMF transmission reach up to 600 km and 480 km, when considering linear and nonlinear effects, respectively. Finally, we show that our proposed 2D-MD SAC-OCDMA system outperforms existing solutions presented in the literature for LR-PON configuration, in terms of both aggregate bit rate and transmission reach.


2021 ◽  
Vol 11 (18) ◽  
pp. 8586
Author(s):  
Andrea Butera ◽  
Simone Gallo ◽  
Carolina Maiorani ◽  
Camilla Preda ◽  
Alessandro Chiesa ◽  
...  

Periodontitis is an irreversible oral disease causing the destruction of tooth-supporting tissues. In addition to scaling and root planing (SRP) procedures, patients should achieve a correct domiciliary oral hygiene in order to maintain a healthy status. The aim of the present study was to evaluate the efficacy of different toothpastes in reducing gingival bleeding in periodontal patients. In addition to a professional treatment of SRP, 80 patients were randomly divided into four groups according to the toothpaste assigned for the daily domiciliary use using an electric toothbrush: Group 1 (Biorepair Gum Protection), Group 2 (Biorepair Plus Parodontgel), Group 3 (Biorepair Peribioma PRO), and Group 4 (Meridol Gum Protection) (control group). After baseline (T0), patients were visited after 15 days (T1), 3 months (T2), and 6 months (T3). At each appointment, the following periodontal indexes were assessed: bleeding on probing (BoP), full-mouth bleeding score (FMBS), and modified sulcus bleeding index (mSBI). All the experimental toothpastes caused an immediate significant modification of the three clinical indexes measured, except for the control product. Biorepair Peribioma PRO, with its paraprobiotic content, was also the only toothpaste causing a prolonged effect, reducing BoP even at T3. Accordingly, both hyaluronic acid and lactoferrin appear as reliable supports for the domiciliary management of periodontal disease. In spite of this, paraprobiotics are likely to show the most important benefit thanks to their immunomodulating mechanism of action.


2021 ◽  
Vol 11 (18) ◽  
pp. 8577
Author(s):  
Hiba Murtadha Al-Saadi ◽  
Kok-Yong Chin ◽  
Fairus Ahmad ◽  
Elvy Suhana Mohd Ramli ◽  
Azlan Mohd Arlamsyah ◽  
...  

Background: Osteoarthritis is a degenerative joint disease lacking disease-modifying therapeutic agents. This study aimed to compare the effects of palm tocotrienol-rich fraction (TRF), glucosamine sulphate, and both agents combined in rats with osteoarthritis induced by monosodium iodoacetate (MIA). Methods: Thirty adult male rats were randomized into normal control, and osteoarthritis groups were treated orally daily with vehicle, palm TRF (100 mg/kg), glucosamine sulphate (250 mg/kg), and both agents combined for 4 weeks. Body weight and grip strength were measured weekly. After being sacrificed, the joints and blood were harvested for histology and serum cartilage oligomeric matrix protein (COMP) levels. Results: The body weight of the rats receiving treatment rebounded significantly after an initial reduction (vs osteoarthritic control, p < 0.05). The rats receiving combined treatments showed significantly better grip strength than the osteoarthritic control and individual treatment groups (p < 0.05). The serum COMP level was lower in all the treated groups (vs osteoarthritic control, p < 0.05). Cartilage histology of the treated rats was not significantly improved (vs osteoarthritic control, p > 0.05). Conclusion: The combination of palm TRF and glucosamine sulphate was more effective than individual agents in improving the grip strength of the rats, but the cartilage damage might need more time to heal.


2021 ◽  
Vol 11 (18) ◽  
pp. 8575
Author(s):  
Sudhir Kumar Mohapatra ◽  
Srinivas Prasad ◽  
Dwiti Krishna Bebarta ◽  
Tapan Kumar Das ◽  
Kathiravan Srinivasan ◽  
...  

Hate speech on social media may spread quickly through online users and subsequently, may even escalate into local vile violence and heinous crimes. This paper proposes a hate speech detection model by means of machine learning and text mining feature extraction techniques. In this study, the authors collected the hate speech of English-Odia code mixed data from a Facebook public page and manually organized them into three classes. In order to build binary and ternary datasets, the data are further converted into binary classes. The modeling of hate speech employs the combination of a machine learning algorithm and features extraction. Support vector machine (SVM), naïve Bayes (NB) and random forest (RF) models were trained using the whole dataset, with the extracted feature based on word unigram, bigram, trigram, combined n-grams, term frequency-inverse document frequency (TF-IDF), combined n-grams weighted by TF-IDF and word2vec for both the datasets. Using the two datasets, we developed two kinds of models with each feature—binary models and ternary models. The models based on SVM with word2vec achieved better performance than the NB and RF models for both the binary and ternary categories. The result reveals that the ternary models achieved less confusion between hate and non-hate speech than the binary models.


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