bluuebunny/crossref_metadata_2025 huggingface dataset:
Unveiling the Effect of Low pH on the SARS-CoV-2 Main Protease by Molecular Dynamics Simulations
Authors: Barazorda-Ccahuana Haruna Luz , Nedyalkova Miroslava , Mas Francesc , Madurga Sergio
Published: November 2021

(1) Background: Main Protease (Mpro) is an attractive therapeutic target that acts in the replication and transcription of the SARS-CoV-2 coronavirus. Mpro is rich in residues exposed to protonation/deprotonation changes which could affect its enzymatic function. This work aimed to explore the effect of the protonation/deprotonation states of Mpro at different pHs using computational techniques. (2) Methods: The different distribution charges were obtained in all the evaluated pHs by the Semi-Grand Canonical Monte Carlo (SGCMC) method. A set of Molecular Dynamics (MD) simulations was performed to consider the different protonation/deprotonation during 250 ns, verifying the structural stability of Mpro at different pHs. (3) Results: The present findings demonstrate that active site residues and residues that allow Mpro dimerisation was not affected by pH changes. However, Mpro substrate-binding residues were altered at low pHs, allowing the increased pocket volume. Additionally, the results of the solvent distribution around Sγ, Hγ, Nδ1 and Hδ1 atoms of the catalytic residues Cys145 and His41 showed a low and high-water affinity at acidic pH, respectively. It which could be crucial in the catalytic mechanism of SARS-CoV-2 Mpro at low pHs. Moreover, we analysed the docking interactions of PF-00835231 from Pfizer in the preclinical phase, which shows excellent affinity with the Mpro at different pHs. (4) Conclusion: Overall, these findings indicate that SARS-CoV-2 Mpro is highly stable at acidic pH conditions, and this inhibitor could have a desirable function at this condition.
Development of Natural Polysaccharide–Based Nanoparticles of Berberine to Enhance Oral Bioavailability: Formulation, Optimization, Ex Vivo, and In Vivo Assessment
Authors: Kohli Kanchan , Mujtaba Ali , Malik Rozina , Amin Saima , Alam Md Sarfaraz , Ali Abuzer , Barkat Md. Abul , Ansari Mohammad Javed
Published: November 2021

The phytogenous alkaloid berberine (BBR) has become a potential drug for the treatment of diabetes, hyperlipidemia, and cancer. However, its therapeutic potential is limited because ofpoor intestinal absorption due to its efflux by the P-gp expressed in the intestinal lumen. Therefore, we aimed to design and fabricate a nanoparticulate system for delivery of BBR employing naturally derived biodegradable and biocompatible polymers, mainly chitosan and alginate, to enhance the oral bioavailability of BBR. A chitosan-alginate nanoparticle system loaded with BBR (BNPs) was formulated by ionic gelation method and was optimized by employing a three-factor, three-level Box-Behnken statistical design. BNPs were characterized for various physicochemical properties, ex vivo, and in vivo evaluations. The optimized BNPs were found to be 202.2 ± 4.9 nm in size, with 0.236 ± 0.02 of polydispersity index, zeta potential −14.8 ± 1.1 mV, and entrapment efficiency of 85.69 ± 2.6%. BNPs showed amorphous nature with no prominent peak in differential scanning calorimetry (DSC) investigation. Similarly, fourier-transform infrared spectroscopy (FTIR) studies did not reveal any interaction between BBR and excipients used. The drug release followed Higuchi kinetics, since these plots demonstrated the highest linearity (R2 = 0.9636), and the mechanism of release was determined to be anomalous or non-Fickian in nature. An ex-vivo gut permeation study showed that BNPs were better internalized into the cells and more highly permeated through the intestine. Furthermore, in vivo pharmacokinetic analysis in female Wistar rats showed a 4.10−fold increase in the oral bioavailability of BBR from BNPs as compared to BBR suspension. With these findings, we have gained new insight into the effective delivery of poorly soluble and permeable drugs via a chitosan-alginate nanoparticle system to improve the therapeutic performance of an oral nanomedicine.
Passive Fire Protection of Taeda pine Wood by Using Starch-Based Surface Coatings
Authors: Tretsiakova-McNally Svetlana , Douarin Adeline Le , Joseph Paul , Arun Malavika
Published: November 2021

The present paper reports the preliminary results relating to the development, subsequent application, and testing of environmentally benign starch-based formulations for passive fire protection of wood substrates. This study evaluated the effectiveness of starch colloid coatings applied onto the wood surface with a view to improving its performance when exposed to the external heat flux (35 kW/m2) during cone calorimetric tests. The formulations were prepared from aqueous colloid solutions of either starch alone, or in combination with inorganic salts, such as: sodium carbonate, Na2CO3, potassium carbonate, K2CO3, and diammonium hydrogen phosphate, (NH4)2HPO4. The fire performance of Taeda pine wood samples, where their top surfaces were treated with these formulations, was compared with the control sample. The thermal and combustion characteristics of the tested samples were determined with the aid of thermo-gravimetric analysis (TGA), bomb and cone calorimetric techniques, and a steady state tube furnace coupled to an FT-IR spectrometer. A significant boost of fire protection was observed when starch formulations with added inorganic salts were applied onto the wood surfaces, compared with the control sample. For example, the presence of K2CO3 in starch colloid solutions resulted in a notable delay of the ignition and exhibited a reduction in the heat release parameters in comparison with the untreated wood substrate.
Recycling of Plastics from Cable Waste from Automotive Industry in Poland as an Approach to the Circular Economy
Authors: Czarnecka-Komorowska Dorota , Kanciak Wiktoria , Barczewski Mateusz , Barczewski Roman , Regulski Roman , Sędziak Dariusz , Jędryczka Cezary
Published: November 2021

This paper presents the contemporary problems of polymer waste recycling, mainly recycling cables from end-of-life vehicles. The authors developed a new material based on mixed polymer waste (ASR) modified with a ductile polymer, mainly recycled low-density polyethylene (rLDPE), to produce moisture-resistant boards with beneficial mechanical properties. The ASR-based compositions without and with homogenization process—including physical, chemical, and shear-assisted compatibilization—were successfully applied and verified by evaluating final recycled product properties. The results showed that recycled polyethylene (rLDPE) was effective as a modifier increasing tensile modulus and flexural strength compare to an ASR mixture. It was found that the adding 5 wt % of polyethylene-grafted maleic anhydride (PE-g-MAH) as a compatibilizer to the ASR mixture significantly increases the homogenization of the components in the ASR matrix. The optimal solution for management cable waste is the manufacture of ASR composites with homogenization using an internal mixer the adding 20 wt % of rLDPE and 5 wt % of PE-g-MAH to the mixed plastics cable waste. The results obtained demonstrate that the hot-pressing with the pre-blending with rLDPE and compatibilizer of the ASR based waste provides a high gain in mechanical and usage properties, enabling the circular economy of plastics from automotive cables.
Immunological Analytical Techniques for Cosmetics Quality Control and Process Monitoring
Authors: Zangheri Martina , Calabretta Maria Maddalena , Calabria Donato , Fiori Jessica , Guardigli Massimo , Michelini Elisa , Melandri Sonia , Maris Assimo , Mirasoli Mara , Evangelisti Luca
Published: November 2021

Cosmetics analysis represents a rapidly expanding field of analytical chemistry as new cosmetic formulations are increasingly in demand on the market and the ingredients required for their production are constantly evolving. Each country applies strict legislation regarding substances in the final product that must be prohibited or regulated. To verify the compliance of cosmetics with current regulations, official analytical methods are available to reveal and quantitatively determine the analytes of interest. However, since ingredients, and the lists of regulated/prohibited substances, rapidly change, dedicated analytical methods must be developed ad hoc to fulfill the new requirements. Research focuses on finding innovative techniques that allow a rapid, inexpensive, and sensitive detection of the target analytes in cosmetics. Among the different methods proposed, immunological techniques are gaining interest, as they make it possible to carry out low-cost analyses on raw materials and finished products in a relatively short time. Indeed, immunoassays are based on the specific and selective antibody/antigen reaction, and they have been extensively applied for clinical diagnostic, alimentary quality control and environmental security purposes, and even for routine analysis. Since the complexity and variability of the matrices, as well as the great variety of compounds present in cosmetics, are analogous with those from food sources, immunological methods could also be applied successfully in this field. Indeed, this would provide a valid approach for the monitoring of industrial production chains even in developing countries, which are currently the greatest producers of cosmetics and the major exporters of raw materials. This review aims to highlight the immunological techniques proposed for cosmetics analysis, focusing on the detection of prohibited/regulated compounds, bacteria and toxins, and allergenic substances, and the identification of counterfeits.
An American Example of Islamic Chaplaincy Education for the European Context
Authors: Vinding Niels Valdemar
Published: November 2021

Against the background of increasing political and academic interest in imam and chaplaincy training and education in Europe, this article argues that the value and purpose of such education remains situated in an alignment between educational provider, student-participants, and employer–stakeholder expectations. These expectations are primarily about Muslim students’ learning and development, requirements and standards of employers, and contributions to community and society, and only secondly, the educations aim at meeting political expectations. The article explores aspects of Hartford Seminary’s success with its programme and alignment of education content and environment with student expectations and the labour market demand. This is supported theoretically by the input–environment–outcome assessment model. The structural and contextually embedded criteria for excellence are discussed and problematised, pointing both to the marginalisation of other drivers of education development that are not market aligned and to strategies of embedding religious authority with chaplains in institutions rather than with imams in mosques. In conclusion, the article highlights the self-sustaining logics that drive educational development but also points to corroborating social, economic, and welfare reasons for quality imam and chaplaincy education.
Improving Hourly Precipitation Estimates for Flash Flood Modeling in Data-Scarce Andean-Amazon Basins: An Integrative Framework Based on Machine Learning and Multiple Remotely Sensed Data
Authors: Chancay Juseth E. , Espitia-Sarmiento Edgar Fabian
Published: November 2021

Accurate estimation of spatiotemporal precipitation dynamics is crucial for flash flood forecasting; however, it is still a challenge in Andean-Amazon sub-basins due to the lack of suitable rain gauge networks. This study proposes a framework to improve hourly precipitation estimates by integrating multiple satellite-based precipitation and soil-moisture products using random forest modeling and bias correction techniques. The proposed framework is also used to force the GR4H model in three Andean-Amazon sub-basins that suffer frequent flash flood events: upper Napo River Basin (NRB), Jatunyacu River Basin (JRB), and Tena River Basin (TRB). Overall, precipitation estimates derived from the framework (BC-RFP) showed a high ability to reproduce the intensity, distribution, and occurrence of hourly events. In fact, the BC-RFP model improved the detection ability between 43% and 88%, reducing the estimation error between 72% and 93%, compared to the original satellite-based precipitation products (i.e., IMERG-E/L, GSMAP, and PERSIANN). Likewise, simulations of flash flood events by coupling the GR4H model with BC-RFP presented satisfactory performances (KGE* between 0.56 and 0.94). The BC-RFP model not only contributes to the implementation of future flood forecast systems but also provides relevant insights to several water-related research fields and hence to integrated water resources management of the Andean-Amazon region.
Shallow-Water Benthic Habitat Mapping Using Drone with Object Based Image Analyses
Authors: Nababan Bisman , Mastu La Ode Khairum , Idris Nurul Hazrina , Panjaitan James P.
Published: November 2021

Spatial information on benthic habitats in Wangiwangi island waters, Wakatobi District, Indonesia was very limited in recent years. However, this area is one of the marine tourism destinations and one of the Indonesia’s triangle coral reef regions with a very complex coral reef ecosystem. The drone technology that has rapidly developed in this decade, can be used to map benthic habitats in this area. This study aimed to map shallow-water benthic habitats using drone technology in the region of Wangiwangi island waters, Wakatobi District, Indonesia. The field data were collected using a 50 × 50 cm squared transect of 434 observation points in March–April 2017. The DJI Phantom 3 Pro drone with a spatial resolution of 5.2 × 5.2 cm was used to acquire aerial photographs. Image classifications were processed using object-based image analysis (OBIA) method with contextual editing classification at level 1 (reef level) with 200 segmentation scale and several segmentation scales at level 2 (benthic habitat). For level 2 classification, we found that the best algorithm to map benthic habitat was the support vector machine (SVM) algorithm with a segmentation scale of 50. Based on field observations, we produced 12 and 9 benthic habitat classes. Using the OBIA method with a segmentation value of 50 and the SVM algorithm, we obtained the overall accuracy of 77.4% and 81.1% for 12 and 9 object classes, respectively. This result improved overall accuracy up to 17% in mapping benthic habitats using Sentinel-2 satellite data within the similar region, similar classes, and similar method of classification analyses.
Hyperspectral Target Detection with an Auxiliary Generative Adversarial Network
Authors: Gao Yanlong , Feng Yan , Yu Xumin
Published: November 2021

In recent years, the deep neural network has shown a strong presence in classification tasks and its effectiveness has been well proved. However, the framework of DNN usually requires a large number of samples. Compared to the training sets in classification tasks, the training sets for the target detection of hyperspectral images may only include a few target spectra which are quite limited and precious. The insufficient labeled samples make the DNN-based hyperspectral target detection task a challenging problem. To address this problem, we propose a hyperspectral target detection approach with an auxiliary generative adversarial network. Specifically, the training set is first expanded by generating simulated target spectra and background spectra using the generative adversarial network. Then, a classifier which is highly associated with the discriminator of the generative adversarial network is trained based on the real and the generated spectra. Finally, in order to further suppress the background, guided filters are utilized to improve the smoothness and robustness of the detection results. Experiments conducted on real hyperspectral images show the proposed approach is able to perform more efficiently and accurately compared to other target detection approaches.
Partial Shape Recognition for Sea Ice Motion Retrieval in the Marginal Ice Zone from Sentinel-1 and Sentinel-2
Authors: Wang Mingfeng , König Marcel , Oppelt Natascha
Published: November 2021

We present an algorithm for computing ice drift in the marginal ice zone (MIZ), based on partial shape recognition. With the high spatial resolution of Sentinel-1 and Sentinel-2 images, and the low sensitivity to atmospheric influences of Sentinel-1, a considerable quantity of ice floes is identified using a mathematical morphology method. Hausdorff distance is used to measure the similarity of segmented ice floes. It is tolerant to perturbations and deficiencies of floe shapes, which enhances the density of retrieved sea ice motion vectors. The PHD algorithm can be applied to sequential images from different sensors, and was tested on two combined image mosaics consisting of Sentinel-1 and Sentinel-2 data acquired over the Fram Strait; the PHD algorithm successfully produced pairs of matched ice floes. The matching result has been verified using shape and surface texture similarity of the ice floes. Moreover, the present method can naturally be extended to the problem of multi-source sea ice image registration.
Resilience of the Central Indian Forest Ecosystem to Rainfall Variability in the Context of a Changing Climate
Authors: Singh Beependra , Jeganathan Chockalingam , Rathore Virendra Singh , Behera Mukunda Dev , Singh Chandra Prakash , Roy Parth Sarathi , Atkinson Peter M.
Published: November 2021

Understanding the spatio-temporal pattern of natural vegetation helps decoding the responses to climate change and interpretation on forest resilience. Satellite remote sensing based data products, by virtue of their synoptic and repetitive coverage, offer to study the correlation and lag effects of rainfall on forest growth in a relatively longer time scale. We selected central India as the study site. It accommodates tropical natural vegetation of varied forest types such as moist and dry deciduous and evergreen and semi-evergreen forests that largely depend on the southwest monsoon. We used the MODIS derived NDVI and CHIRPS based rainfall datasets from 2001 to 2018 in order to analyze NDVI and rainfall trend by using Sen’s slope and standard anomalies. The study observed a decreasing rainfall trend over 41% of the forests, while the rest of the forest area (59%) demonstrated an increase in rainfall. Furthermore, the study estimated drought conditions during 2002, 2004, 2009, 2014 and 2015 for 98.2%, 92.8%, 89.6%, 90.1% and 95.8% of the forest area, respectively; and surplus rainfall during 2003, 2005, 2007, 2011, 2013 and 2016 for 69.5%, 63.9%, 71.97%, 70.35%, 94.79% and 69.86% of the forest area, respectively. Hence, in the extreme dry year (2002), 93% of the forest area showed a negative anomaly, while in the extreme wet year (2013), 89% of forest cover demonstrated a positive anomaly in central India. The long-term vegetation trend analysis revealed that most of the forested area (>80%) has a greening trend in central India. When we considered annual mean NDVI, the greening and browning trends were observed over at 88.65% and 11.35% of the forested area at 250 m resolution and over 93.01% and 6.99% of the area at 5 km resolution. When we considered the peak-growth period mean NDVI, the greening and browning trends were as follows: 81.97% and 18.03% at 250 m and 88.90% and 11.10% at 5 km, respectively. The relative variability in rainfall and vegetation growth at five yearly epochs revealed that the first epoch (2001–2005) was the driest, while the third epoch (2011–2015) was the wettest, corresponding to the lowest vegetation vigour in the first epoch and the highest in the third epoch during the past two decades. The study reaffirms that rainfall is the key climate variable in the tropics regulating the growth of natural vegetation, and the central Indian forests are dominantly resilient to rainfall variation.
Predicting Equivalent Water Thickness in Wheat Using UAV Mounted Multispectral Sensor through Deep Learning Techniques
Authors: Traore Adama , Ata-Ul-Karim Syed Tahir , Duan Aiwang , Soothar Mukesh Kumar , Traore Seydou , Zhao Ben
Published: November 2021

The equivalent water thickness (EWT) is an important biophysical indicator of water status in crops. The effective monitoring of EWT in wheat under different nitrogen and water treatments is important for irrigation management in precision agriculture. This study aimed to investigate the performances of machine learning (ML) algorithms in retrieving wheat EWT. For this purpose, a rain shelter experiment (Exp. 1) with four irrigation quantities (0, 120, 240, 360 mm) and two nitrogen levels (75 and 255 kg N/ha), and field experiments (Exps. 2–3) with the same irrigation and rainfall water levels (360 mm) but different nitrogen levels (varying from 75 to 255 kg N/ha) were conducted in the North China Plain. The canopy reflectance was measured for all plots at 30 m using an unmanned aerial vehicle (UAV)-mounted multispectral camera. Destructive sampling was conducted immediately after the UAV flights to measure total fresh and dry weight. Deep Neural Network (DNN) is a special type of neural network, which has shown performance in regression analysis is compared with other machine learning (ML) models. A feature selection (FS) algorithm named the decision tree (DT) was used as the automatic relevance determination method to obtain the relative relevance of 5 out of 67 vegetation indices (Vis), which were used for estimating EWT. The selected VIs were used to estimate EWT using multiple linear regression (MLR), deep neural network multilayer perceptron (DNN-MLP), artificial neural networks multilayer perceptron (ANN-MLP), boosted tree regression (BRT), and support vector machines (SVMs). The results show that the DNN-MLP with R2 = 0.934, NSE = 0.933, RMSE = 0.028 g/cm2, and MAE of 0.017 g/cm2 outperformed other ML algorithms (ANN-MPL, BRT, and SVM- Polynomial) owing to its high capacity for estimating EWT as compared to other ML methods. Our findings support the conclusion that ML can potentially be applied in combination with VIs for retrieving EWT. Despite the complexity of the ML models, the EWT map should help farmers by improving the real-time irrigation efficiency of wheat by quantifying field water content and addressing variability.
Earthquake Magnitude Estimation from High-Rate GNSS Data: A Case Study of the 2021 Mw 7.3 Maduo Earthquake
Authors: Gao Zhiyu , Li Yanchuan , Shan Xinjian , Zhu Chuanhua
Published: November 2021

Peak ground displacement (PGD) and peak ground velocity (PGV) are critical parameters during earthquake early warning, as they can provide rapid magnitude estimation before rupture end. In this study, we used the high-rate Global Navigation Satellite System (GNSS) data from 55 continuous stations to estimate the magnitude of the 2021 Maduo earthquake in western China. We used the relative positioning method and variometric approach to acquire real-time GNSS displacement and velocity waveforms, respectively. The results showed the amplitude of displacement and velocity waveforms gradually decreased with increasing hypocentral distance. Our results showed that the fluctuation of PGD magnitudes over time is smaller than that of PGV magnitudes. Nonetheless, the earthquake magnitudes estimated from both methods were consistent with their counterparts (Mw 7.3) reported by the United States Geological Survey (USGS). The final magnitude estimated from the PGD and PGV methods were Mw 7.25 and Mw 7.31, respectively. In addition, our results highlighted how the number of high-rate GNSS stations could influence the stability and convergence time of magnitude estimation.
Analysis of Double Elastic Steel Wind Driven Magneto-Electric Vibration Energy Harvesting System
Authors: Wang Yi-Ren , Chu Ming-Ching
Published: November 2021

This research proposes an energy harvesting system that collects the downward airflow from a helicopter or a multi-axis unmanned rotary-wing aircraft and uses this wind force to drive the magnet to rotate, generating repulsive force, which causes the double elastic steel system to slap each other and vibrate periodically in order to generate more electricity than the traditional energy harvesting system. The design concept of the vibration mechanism in this study is to allow the elastic steel carrying the magnet to slap another elastic steel carrying the piezoelectric patch to form a set of double elastic steel vibration energy harvesting (DES VEH) systems. The theoretical DES VEH mechanism of this research is composed of a pair of cantilever beams, with magnets attached to the free end of one beam, and PZT attached to the other beam. This study analyzes the single beam system first. The MOMS method is applied to analyze the frequency response of this nonlinear system theoretically, then combines the piezoelectric patch and the magneto-electric coupling device with this nonlinear elastic beam to analyze the benefits of the system’s converted electrical energy. In the theoretical study of the DES VEH system, the slapping force between the two elastic beams was considered as a concentrated load on each of the beams. Furthermore, both SES and DES VEH systems are studied and correlated. Finally, the experimental data and theoretical results are compared to verify the feasibility and correctness of the theory. It is proven that this DES VEH system can not only obtain the electric energy from the traditional SES VEH system but also obtain the extra electric energy of the steel vibration subjected to the slapping force, which generates optimal power to the greatest extent.
Research on On-Line Detection Method of Transformer Winding Deformation Based on VFTO
Authors: Wang Yanyun , Zhou Guoqiong , Zeng Chunping , Zhang Wenbin , Ren Yanan , Ke Yi , Chu Hequn , Suo Chunguang
Published: November 2021

At present, the detection of transformer winding deformation faults is carried out in an offline state, which requires the transformer to cooperate with the implementation of planned power outages, or it takes place after the sudden failure of the transformer when it is out of operation. It is difficult to obtain the status information of the windings online in time. Since the transformer will suffer very fast transient overvoltage (VFTO) impact during operation, combined with the principle of the frequency response method, an online detection method of transformer winding deformation based on VFTO is proposed. In order to study the frequency response characteristics of transformer winding under the impact of VFTO, the generation process of VFTO is simulated by simulation software, and the equivalent circuit model of transformer winding before and after deformation is established. The VFTO signal is injected into the transformer circuit model as an excitation source, and the changes of resonant frequencies of frequency response curve under different deformation types and different deformation degrees of winding are analyzed. The simulation results show that the frequency response curves of different winding deformation types are different. Different deformation degrees are simulated by increasing the radial capacitance by 4%, 13%, and 23%, series inductance by 2%, 4%, and 6%, and longitudinal capacitance by 3%, 6%, and 9%, and the change of resonance frequencies can comprehensively reflect the deformation information of winding. At the same time, the tests of different deformation types and deformation degrees of the simulated winding are carried out. The results show that with the deepening of the change degree of the simulated fault inductance value, the frequency response curve shifts to the low-frequency direction, confirming the feasibility of the online detection method of transformer winding deformation based on VFTO.
Computational Large Field-of-View RGB-D Integral Imaging System
Authors: Jung Geunho , Won Yong-Yuk , Yoon Sang Min
Published: November 2021

The integral imaging system has received considerable research attention because it can be applied to real-time three-dimensional image displays with a continuous view angle without supplementary devices. Most previous approaches place a physical micro-lens array in front of the image, where each lens looks different depending on the viewing angle. A computational integral imaging system with a virtual micro-lens arrays has been proposed in order to provide flexibility for users to change micro-lens arrays and focal length while reducing distortions due to physical mismatches with the lens arrays. However, computational integral imaging methods only represent part of the whole image because the size of virtual lens arrays is much smaller than the given large-scale images when dealing with large-scale images. As a result, the previous approaches produce sub-aperture images with a small field of view and need additional devices for depth information to apply to integral imaging pickup systems. In this paper, we present a single image-based computational RGB-D integral imaging pickup system for a large field of view in real time. The proposed system comprises three steps: deep learning-based automatic depth map estimation from an RGB input image without the help of an additional device, a hierarchical integral imaging system for a large field of view in real time, and post-processing for optimized visualization of the failed pickup area using an inpainting method. Quantitative and qualitative experimental results verify the proposed approach’s robustness.
An Electromagnetic Time-Reversal Imaging Algorithm for Moisture Detection in Polymer Foam in an Industrial Microwave Drying System
Authors: Omrani Adel , Yadav Rahul , Link Guido , Lähivaara Timo , Vauhkonen Marko , Jelonnek John
Published: November 2021

Microwave tomography (MWT) based control is a novel idea in industrial heating systems for drying polymer foam. In this work, an X-band MWT module is designed and developed using a fixed antenna array configuration and integrated with the HEPHAISTOS industrial heating system. A decomposition of the time-reversal operator (DORT) algorithm with a proper Green’s function of multilayered media is utilized to localize the moisture location. The derived Green’s function can be applied to the media with low or high contrast layers. It is shown that the time-reversal imaging (TRI) with the proposed Green’s function can be applied to the multilayered media with a moderately rough surface. Moreover, a single frequency TRI is proposed to decrease the measurement time. Numerical results for different moisture scenarios are presented to demonstrate the efficacy of the proposed method. The developed method is then tested on the experimental data for different moisture scenarios from our developed MWT experimental prototype. Image reconstruction results show promising capabilities of the TRI algorithm in estimating the moisture location in the polymer foam.
MIMO Antenna System for Modern 5G Handheld Devices with Healthcare and High Rate Delivery
Authors: Kiani Saad Hassan , Altaf Ahsan , Anjum Muhammad Rizwan , Afridi Sharjeel , Arain Zulfiqar Ali , Anwar Sadia , Khan Salahuddin , Alibakhshikenari Mohammad , Lalbakhsh Ali , Khan Muhammad Abbas , Abd-Alhameed Raed A. , Limiti Ernesto
Published: November 2021

In this work, a new prototype of the eight-element MIMO antenna system for 5G communications, internet of things, and networks has been proposed. This system is based on an H-shaped monopole antenna system that offers 200 MHz bandwidth ranges between 3.4–3.6 GHz, and the isolation between any two elements is well below −12 dB without using any decoupling structure. The proposed system is designed on a commercially available 0.8 mm-thick FR4 substrate. One side of the chassis is used to place the radiating elements, while the copper from the other side is being removed to avoid short-circuiting with other components and devices. This also enables space for other systems, sub-systems, and components. A prototype is fabricated and excellent agreement is observed between the experimental and the computed results. It was found that ECC is 0.2 for any two radiating elements which is consistent with the desirable standards, and channel capacity is 38 bps/Hz which is 2.9 times higher than 4 × 4 MIMO configuration. In addition, single hand mode and dual hand mode analysis are conducted to understand the operation of the system under such operations and to identify losses and/or changes in the key performance parameters. Based on the results, the proposed antenna system will find its applications in modern 5G handheld devices and internet of things with healthcare and high rate delivery. Besides that, its design simplicity will make it applicable for mass production to be used in industrial demands.
Non-Singleton Type-3 Fuzzy Approach for Flowmeter Fault Detection: Experimental Study in a Gas Industry
Authors: Wang Jing-he , Tavoosi Jafar , Mohammadzadeh Ardashir , Mobayen Saleh , Asad Jihad H. , Assawinchaichote Wudhichai , Vu Mai The , Skruch Paweł
Published: November 2021

The main contribution of this paper is to develop a new flowmeter fault detection approach based on optimized non-singleton type-3 (NT3) fuzzy logic systems (FLSs). The introduced method is implemented on an experimental gas industry plant. The system is modeled by NT3FLSs, and the faults are detected by comparison of measured end estimated signals. In this scheme, the detecting performance depends on the estimation and modeling performance. The suggested NT3FLS is used because of the existence of a high level of measurement errors and uncertainties in this problem. The designed NT3FLS with uncertain footprint-of-uncertainty (FOU), fuzzy secondary memberships and adaptive non-singleton fuzzification results in a powerful tool for modeling signals immersed in noise and error. The level of non-singleton fuzzification and membership parameters are tuned by maximum correntropy (MC) unscented Kalman filter (KF), and the rule parameters are learned by correntropy KF (CKF) with fuzzy kernel size. The suggested learning algorithms can handle the non-Gaussian noises that are common in industrial applications. The various types of flowmeters are investigated, and the effect of common faults are examined. It is shown that the suggested approach can detect the various faults with good accuracy in comparison with conventional approaches.
Metal–Organic-Framework- and MXene-Based Taste Sensors and Glucose Detection
Authors: Do Ha Huu , Cho Jin Hyuk , Han Sang Mok , Ahn Sang Hyun , Kim Soo Young
Published: November 2021

Taste sensors can identify various tastes, including saltiness, bitterness, sweetness, sourness, and umami, and have been useful in the food and beverage industry. Metal–organic frameworks (MOFs) and MXenes have recently received considerable attention for the fabrication of high-performance biosensors owing to their large surface area, high ion transfer ability, adjustable chemical structure. Notably, MOFs with large surface areas, tunable chemical structures, and high stability have been explored in various applications, whereas MXenes with good conductivity, excellent ion-transport characteristics, and ease of modification have exhibited great potential in biochemical sensing. This review first outlines the importance of taste sensors, their operation mechanism, and measuring methods in sensing utilization. Then, recent studies focusing on MOFs and MXenes for the detection of different tastes are discussed. Finally, future directions for biomimetic tongues based on MOFs and MXenes are discussed.
A Bidirectional Interpolation Method for Post-Processing in Sampling-Based Robot Path Planning
Authors: Kang Tae-Won , Kang Jin-Gu , Jung Jin-Woo
Published: November 2021

This paper proposes a post-processing method called bidirectional interpolation method for sampling-based path planning algorithms, such as rapidly-exploring random tree (RRT). The proposed algorithm applies interpolation to the path generated by the sampling-based path planning algorithm. In this study, the proposed algorithm is applied to the path created by RRT-connect and six environmental maps were used for the verification. It was visually and quantitatively confirmed that, in all maps, not only path lengths but also the piecewise linear shape were decreased compared to the path generated by RRT-connect. To check the proposed algorithm’s performance, visibility graph, RRT-connect algorithm, Triangular-RRT-connect algorithm and post triangular processing of midpoint interpolation (PTPMI) were compared in various environmental maps through simulation. Based on these experimental results, the proposed algorithm shows similar planning time but shorter path length than previous RRT-like algorithms as well as RRT-like algorithms with PTPMI having a similar number of samples.
The Online Education System: COVID-19 Demands, Trends, Implications, Challenges, Lessons, Insights, Opportunities, Outlooks, and Directions in the Work from Home
Authors: Dias Ana , Scavarda Annibal , Silveira Haydee , Scavarda Luiz Felipe , Kondamareddy Kiran Kumar
Published: November 2021

The aim of this exploratory research is to identify how working from home and the consequent social isolation interfered in teachers’ work and students’ learning and to identify the challenges, difficulties, advantages, opportunities, demands, trends, implications, outlooks, lessons, directions, and feelings of students and teachers in the teaching processes during the COVID-19 pandemic period. To reach its aim, the authors of this paper developed searches and scientific databases and they also sent an email questionnaire to Rio de Janeiro city schools. The descriptive analyses were made by descriptive statistics (proportions, rates, minimum, maximum, mean, median, standard deviation, coefficient of variation—CV). The results show that working from home and the consequent social isolation interfered in the students’ and teachers’ feelings and sensations and highlight the words “frustration”, “hope”, and “strangeness”. From the sample, 96.4% of the teachers affirmed that working from home and the social isolation interfered in their work and 97.4% of the teachers affirmed that working from home and the consequent social isolation interfered in the students’ learning. This research is the starting point to boost discussions on the subjects of COVID-19, working from home, social isolation, and education. This paper will support researchers in the development of future studies related to the subjects.
Bamboos: From Bioresource to Sustainable Materials and Chemicals
Authors: Iroegbu Austine , Ray Suprakas
Published: November 2021

Nature is a master engineer. From the bones of the tiniest bird to the sophisticated bioproduction of a spider’s web, the works of nature are an enigma to the scientific mind. In the fields of physics, chemistry, biology, and mathematics, studying, understanding, and harnessing the intricacies of nature’s designs for the benefit of mankind is the bedrock of science and technology. One such exceptionally engineered natural material is the bamboo plant. This ancient vegetation has, over dozens of generations, reinvented itself as a legendary, resilient, ubiquitous, and impressive bioresource that is not just sustainable, but also ecologically and cheaply cultivatable, and invaluable for soil erosion control, while holding the enormous potential to be transmuted into various useful chemicals and materials. With the increasing concerns and obligations in rethinking the future of the environment, sequestration of carbon dioxide, reduction in timber usage, and preservation of already depleted non-renewable resources, it has become vital for environmentalists, governments, scientists, and other stakeholders to identify alternatives to fossil-based chemicals and their derivable materials that are sustainable without compromising efficiency. By coalescing engineering-, chemical-, and materials science-based approaches, including results from over 100 reports, we demonstrate that the bamboo plant presents enormous opportunities for sustainable chemicals and materials. In addition, we highlight the current challenges involving the optimization of bamboo-based technologies and provide recommendations for future studies.
Correlation of Respiratory Aerosols and Metabolic Carbon Dioxide
Authors: Kappelt Niklas , Russell Hugo Savill , Kwiatkowski Szymon , Afshari Alireza , Johnson Matthew Stanley
Published: November 2021

Respiratory aerosols from breathing and talking are an important transmission route for viruses, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Previous studies have found that particles with diameters ranging from 10 nm to 145 μm are produced from different regions in the respiratory system and especially smaller particles can remain airborne for long periods while carrying viral RNA. We present the first study in which respiratory aerosols have been simultaneously measured with carbon dioxide (CO2) to establish the correlation between the two concentrations. CO2 concentrations are easily available through low-cost sensors and could be used to estimate viral exposure through this correlation, whereas source-specific aerosol measurements are complicated and not possible with low-cost sensors. The increase in both respiratory aerosols and CO2 was linear over ten minutes in a 2 m3 chamber for all participants, suggesting a strong correlation. On average, talking released more particles than breathing, with 14,600 ± 16,800 min−1 (one-σ standard deviation) and 6210 ± 5630 min−1 on average, respectively, while CO2 increased with 139 ± 33 ppm min−1 during talking and 143 ± 29 ppm min−1 during breathing. Assuming a typical viral load of 7×106 RNA copies per mL of oral fluid, ten minutes of talking and breathing are estimated to produce 1 and 16 suspended RNA copies, respectively, correlating to a CO2 concentration of around 1800 ppm in a 2 m3 chamber. However, viral loads can vary by several orders of magnitude depending on the stage of the disease and the individual. It was therefore concluded that, by measuring CO2 concentrations, only the number and volume concentrations of released particles can be estimated with reasonable certainty, while the number of suspended RNA copies cannot.
Correction: Fry et al. Global Leadership for Sustainability. Sustainability 2021, 13, 6360
Authors: Fry Louis W. , Egel Eleftheria
Published: November 2021

The authors would like to make the following corrections to the published paper [...]
Modeling Sustainable Risks Mitigation Strategies Using a Morphological Analysis-Based Approach: A Real Case Study
Authors: Elmsalmi Manel , Hachicha Wafik , Aljuaid Awad M.
Published: November 2021

The supply chain risk management (SCRM) is very critical to strategically support the firms to continuous success. There are, at least, three basic steps in this SCRM process: risk identification, risk evaluation, and risk mitigation (treatment). Whatever happens, the main step is risk mitigation (RM) and mainly sustainable RM. In fact, every risk must be eliminated or controlled as much as possible. The purpose of this paper is to elaborate and evaluate various RM scenarios from an initial risk identification and prioritization solution. The proposed scenario modeling technique is based on morphological analysis (MA) as an explorative scenario tool for RM. MA is used to develop a framework to proactively assess critical risk variables. Firstly, MA is employed to exhaustively create possible RM scenarios and, secondly, to assess the likelihood of each scenario. The proposed approach addresses the need for a basic rubric to help identify and choose RM approaches. A real case study is provided from the food industry to illustrate the application of the proposed approach. To handle all possible MA strategies, a dedicated MORPHOL software package is used. In addition, RM strategies are selected based on sustainability indicators. The case study results prove that MA has a considerable value for SCRM. It shows that firms can adopt multiple robust strategies in the form of a scenario describing all stages of SCRM in an integrated representation.
Varieties Matter When Planning for Poverty Alleviation and Ecological Restoration in Karst Fruit Cultivation Areas
Authors: Zou Zhigang , Zeng Fuping , Wang Kelin , Zeng Zhaoxia , Zhang Hao
Published: November 2021

Landscapes are increasingly being managed to meet multiple objectives and balance social, economic, and environmental goals. The Honghe Hani and Yi Autonomous Prefecture in the Yunnan faulted Basin represent examples of areas with a range of concerns, including balancing farming with poverty alleviation, economic development, and ecological protection. Here, we set out four future land-use scenarios to quantify and map the values of key evidence-based, policy-relevant ecosystem services in three typical counties in this area. We found that implementing existing fruit-growing plans may increase the expected total value of ecosystem services due to increased acreage, but it does not actually increase the total value of ecosystem services per unit area. For example, total ecosystem service provision in Jianshui County decreased by 3% by 2023 under the current trend after the expansion of the planting area was subtracted. However, planting fruit trees with better ecological benefits in areas of greater degradation risk, can increase the delivery of ecosystem services by 10–20%. Thus, variety coordination based on detailed study of the geographical contexts and ecological-economic performance of different types of fruit cultivation can produce ecological-economic improvements beyond simple expansion and better inform county-scale policy, planning, and management interventions.
Analysis of the Impact of Input Data on the Planned Costs of Building Maintenance
Authors: Plebankiewicz Edyta , Gracki Jakub
Published: November 2021

The aim of the article is to analyze the method of determining the maintenance costs of buildings based on the method proposed in Polish legal regulations. The analysis of the sensitivity of the method shows that the assumed number of product use cycles during the calculation period has the greatest impact, while the adopted warranty period has the lowest impact. A multi-functional building combining housing, office, service and commercial was analyzed in order to obtain a broader picture of the model’s operation. The results of the analyses allow us to conclude that despite the higher price of materials, the most durable solutions, which are the most expensive to purchase, turn out to be the most advantageous in the entire life cycle of the building. The method proposed in Polish law regulations has certain limitations. In order to level them, it was proposed to extend the method by using NPV (Net Present Value) for calculations and extending the life cycle of the building to 80 years.