bluuebunny/crossref_metadata_2025 huggingface dataset:
Discovery of a 2′-Fluoro,2′-Bromouridine Phosphoramidate Prodrug Exhibiting Anti-Yellow Fever Virus Activity in Culture and in Mice
Authors: LeCher Julia C. , Zandi Keivan , Costa Vivian Vasconcelos , Amblard Franck , Tao Sijia , Patel Dharmeshkumar , Lee Sujin , da Silva Santos Felipe Rocha , Goncalves Matheus Rodrigues , Queroz-Junior Celso Martins , Marim Fernanda Martins , Musall Katie , Goh Shu Ling , McBrayer Tamara , Downs-Bowen Jessica , De Ramyani , Azadi Niloufar , Kohler James , Teixeira Mauro Martins , Schinazi Raymond F.
Published: October 2022

Yellow fever virus (YFV) is a potentially lethal, zoonotic, blood-borne flavivirus transmitted to humans and non-human primates by mosquitoes. Owing to multiple deadly epidemics, the WHO classifies YFV as a “high impact, high threat disease” with resurgent epidemic potential. At present, there are no approved antiviral therapies to combat YFV infection. Herein we report on 2′-halogen-modified nucleoside analogs as potential anti-YFV agents. Of 11 compounds evaluated, three showed great promise with low toxicity, high intracellular metabolism into the active nucleoside triphosphate form, and sub-micromolar anti-YFV activity. Notably, we investigated a 2′-fluoro,2′-bromouridine phosphate prodrug (C9), a known anti-HCV agent with good stability in human blood and favorable metabolism. Predictive modeling revealed that C9 could readily bind the active site of the YFV RdRp, conferring its anti-YFV activity. C9 displayed potent anti-YFV activity in primary human macrophages, 3D hepatocyte spheroids, and in mice. In an A129 murine model, shortly after infection, C9 significantly reduced YFV replication and protected against YFV-induced liver inflammation and pathology with no adverse effects. Collectively, this work identifies a potent new anti-YFV agent with strong therapeutic promise.
Impact of Physicochemical Parameters on the Diversity and Distribution of Microbial Communities Associated with Three South African Peatlands
Authors: Weels Shandré S. L. , Welz Pamela J. , Prins Alaric , Le Roes-Hill Marilize
Published: October 2022

Peatlands are complex wetland-like ecosystems that harbor diverse microbial communities. In this study, the microbial communities (fungal and actinobacterial) associated with an unimpacted peatland (Vankervelsvlei; VV), an impacted peatland (Goukou River system; GK), and a developing peatland (Nuwejaars River system; NR) were determined through ITS and 16S rRNA metataxonomic analyses. Unidentified Acidimicrobiales dominated in GK and NR, unidentified Intrasporangiaceae and Solirubobacterales in NR, and Corynebacterium, Propionibacterium, and Streptomyces species in VV. The fungal phyla, Ascomycota and Basidiomycota, dominated all three sites, and harbored unique fungal taxa belonging to a wide range of fungal guilds. Physicochemical properties of the peat collected from the three sites were analyzed in association with microbial community structures in order to determine which parameters acted as the main drivers for microbial diversity. BEST analysis (linking microbial diversity patterns to environmental variables) showed that nitrogen (N), aluminum (Al), phosphorus (P), and potassium (K) were the most significant physicochemical drivers of actinobacterial community structure, while iron (Fe) and humification were the environmental parameters that affected the fungal communities the most. In conclusion, this study has provided some insight into the fungal and actinobacterial communities associated with three South African peatlands and the main environmental drivers that influence these communities.
Lightweight Chain-Typed Magnetic Fe3O4@rGO Composites with Enhanced Microwave-Absorption Properties
Authors: Qian Congyi , Liang Xiaohui , Wu Mei , Zhang Xingxin
Published: October 2022

A lightweight microwave-absorbing material with a strong electromagnetic-absorption capability of practical significance in the field of electromagnetic compatibility was obtained by adjusting the ratio of Fe3O4 and rGO. A nanoparticle material with a chain-typed structure consisting of a combination of Fe3O4 and rGO was produced by a hydrothermal method under an applied magnetic field. The electromagnetic loss property of the Fe3O4@rGO composites is studied in the frequency range from 2 to 18 GHz. In addition, the reflection loss and the mechanism of microwave absorption are explored. By changing the amounts of rGO, the electromagnetic loss of the Fe3O4@rGO composites can be effectively regulated, which obtain better reflection loss. The minimum reflection loss of the Fe3O4@rGO composites is −49.4 dB at 16.2 GHz only with a thickness of 1.75 mm. Thus, the Fe3O4@rGO composites have an extremely thin thickness and a strong electromagnetic wave absorption capacity, which is a candidate for the development of lightweight magnetic absorbing materials.
Combinatorial microRNA Loading into Extracellular Vesicles for Increased Anti-Inflammatory Efficacy
Authors: Pottash Alex Eli , Levy Daniel , Jeyaram Anjana , Kuo Leo , Kronstadt Stephanie M. , Chao Wei , Jay Steven M.
Published: October 2022

Extracellular vesicles (EVs) have emerged as promising therapeutic entities in part due to their potential to regulate multiple signaling pathways in target cells. This potential is derived from the broad array of constituent and/or cargo molecules associated with EVs. Among these, microRNAs (miRNAs) are commonly implicated as important and have been associated with a wide variety of EV-induced biological phenomena. While controlled loading of single miRNAs is a well-documented approach for enhancing EV bioactivity, loading of multiple miRNAs has not been fully leveraged to maximize the potential of EV-based therapies. Here, an established approach to extrinsic nucleic acid loading of EVs, sonication, was utilized to load multiple miRNAs in HEK293T EVs. Combinations of miRNAs were compared to single miRNAs with respect to anti-inflammatory outcomes in assays of increasing stringency, with the combination of miR-146a, miR-155, and miR-223 found to have the most potential amongst the tested groups.
Powassan Virus Induces Structural Changes in Human Neuronal Cells In Vitro and Murine Neurons In Vivo
Authors: Nelson Jacob , Ochoa Lorenzo , Villareal Paula , Dunn Tiffany , Wu Ping , Vargas Gracie , Freiberg Alexander N.
Published: October 2022

Powassan virus (POWV) is a tick-borne flavivirus (TBFV) that can cause severe encephalitis in humans with a case–fatality rate as high as 11%. Patients who survive severe encephalitic disease can develop long-term neurological sequelae that can be debilitating and life-long. In this study, we have sought to characterize a primary human fetal brain neural stem cell system (hNSC), which can be differentiated into neuron and astrocyte co-cultures, to serve as a translational in vitro system for infection with POWV and a comparative mosquito-borne flavivirus (MBFV), West Nile virus (WNV). We found that both viruses are able to infect both cell types in the co-culture and that WNV elicits a strong inflammatory response characterized by increased cytokines IL-4, IL-6, IL-8, TNF-α and IL-1β and activation of apoptosis pathways. POWV infection resulted in fewer cytokine responses, as well as less detectable apoptosis, while neurons infected with POWV exhibited structural aberrations forming in the dendrites. These anomalies are consistent with previous findings in which tick-borne encephalitis virus (TBEV) infected murine primary neurons formed laminal membrane structures (LMS). Furthermore, these structural aberrations are also recapitulated in brain tissue from infected mice. Our findings indicate that POWV is capable of infecting human primary neurons and astrocytes without causing apparent widespread apoptosis, while forming punctate structures reminiscent with LMS in primary human neurons and in vivo.
Loop-Mediated Isothermal Amplification (LAMP) for the Rapid and Sensitive Detection of Alternaria alternata (Fr.) Keissl in Apple Alternaria Blotch Disease with Aapg-1 Encoding the Endopolygalacturonase
Authors: Liu Baoyou , Li Zhiwei , Du Jianfeng , Zhang Wei , Che Xiaozhi , Zhang Ziran , Chen Ping , Wang Yingzi , Li Yang , Wang Shaoli , Ding Xinhua
Published: October 2022

Apple Alternaria blotch disease, caused by Alternaria alternata (Fr.) Keissl, is one of the most famous leaf diseases. When the disease is prevalent, it causes leaf abscission and influences the formation of flower buds and photosynthesis. Therefore, a simple, rapid, high-specificity and sensitivity method for monitoring infected leaves at early developmental stages is urgently needed, so that the occurrence and expansion of A. alternata can be controlled in time. In our research, a rapid, specific and efficient loop-mediated isothermal amplification (LAMP) method was developed to detect A. alternata within 60 min. Six primers of LAMP detection can only specifically amplify the aapg-1 gene in A. alternata but not in four other important fungi in apples. The aapg-1 gene encodes endopolygalacturonase in A. alternata, and there are significant differences among different species. Thus, it was applied as the target for LAMP primers. Compared to conventional PCR detection, our LAMP method had the same sensitivity as that of detecting as little as 1 fg of pure genomic DNA of A. alternata. When leaves were inoculated with A. alternata conidia, LAMP detected 1 × 102 conidia/mL as the minimum concentration. However, the traditional tissue isolation and identification method only isolated A. alternata from leaves inoculated with 1 × 105 and 1 × 106 conidia/mL, indicating that the LAMP method was more sensitive than the traditional tissue isolation and identification method for A. alternata before symptoms. Further tests also indicated that LAMP detection was more accurate and sensitive than the traditional tissue isolation and identification method for A. alternata in leaves with the Alternaria blotch symptom collected from the field. Our results showed that the LAMP-targeting the aapg-1 gene has the advantages of high sensitivity, specificity and simplicity and can be used for rapid detection and early monitoring of A. alternata in the field. LAMP is instructive for us to effectively prevent and control apple Alternaria blotch disease.
Application of Exploratory Factor Analysis and Item Response Theory to Validate NHANES ADL Scale in Patients Reporting Rheumatoid Arthritis
Authors: Sakharkar Prashant , Ansari Najma
Published: October 2022

Background: Several instruments are used for measuring functional limitations among rheumatoid arthritis (RA) patients. However, these instruments are incongruously assessed for their psychometric properties. The National Health and Nutritional Examination Survey (NHANES) uses a generic questionnaire to assess the activities of daily living (ADL) to measure functional limitations among its participants. The psychometric properties of the NHANES-ADL scale were evaluated using a patient examination and survey data. Methods: NHANES-ADL scale was assessed for its internal consistency and factor structure. Scale reliability was assessed with Cronbach’s alpha reliability coefficient. Principal component analysis with Promax rotation was used to obtain factor structure. Confirmatory factory analysis was used to calculate fit indices. The graded item response theory model was used to estimate item discrimination, difficulty, and test information. Results: Our sample included 1132 individuals with RA. Exploratory factor analyses of 19-item NHANES ADL scale produced one factor solution and accounted for 35% of variance. The Cronbach alpha of this scale was 0.92. The results of graded item response model indicated items performing well discriminating high and low level of functional ability. A higher slope (α) reflected stronger ability of items to discriminate across the continuum. Conclusions: The NHANES ADL scale showed good reliability, single dimensionality, and validity in RA patients. Studies should explore its test-retest reliability and its ability to reliably measure functional change over time in the future.
A Large Mode Area Parabolic-Profile Core Fiber with Modified Segmented in Cladding
Authors: Yang Song , Zhang Wentao , She Yulai , Du Hao , Tu San
Published: October 2022

In this paper, a novel fiber with a parabolic-profile core and eight segmented trenches in cladding is designed. The designed fiber consists of the segmented trench of low refractive index in cladding and parabolic-profile of high refractive index in the core. The proposed fiber has good bending resistance. The eight segmented trenches in the cladding can decrease the refractive index of cladding to increase the difference index between the core and cladding to limit fundamental mode (FM) loss. The proposed fiber can offer an effective single mode (SM) operation with a large mode area (LMA) of 952 µm2 at the small bending radius (R = 10 cm). In addition, the fiber is also suitable under an 18 W/m heat load. The proposed fiber achieves good bending performance, which can be suitable for the compact high-power lasers.
An Optical POCT Device for Colorimetric Detection of Urine Test Strips Based on Raspberry Pi Imaging
Authors: Yang Zixin , Cai Gaozhe , Zhao Jianlong , Feng Shilun
Published: October 2022

Urine examinations are widely applied in hospitals using urine test strip analyzers or other sophisticated professional instruments. However, such methods are inconvenient health monitoring of patients at home. Herein, we construct an optical device for point-of-care testing (POCT) for urine analysis at home or on the spot. A black box and color calibration curve are established to eliminate the influence of ambient light with an independent internal lighting system included in the device. A Raspberry Pi with a CSI camera is programmed to automatically collect the strip images and identify the HSV values of the image with an image processing algorithm. During this process, these corrected colors are converted to concentration values by preloaded standard curves. Under optimal conditions, the proposed POCT device can quantitatively and automatically detect glucose within 1 min, with linear detection ranging from 2 mM to 60 mM and a detection limit of 1.16 mM. In addition, the device demonstrates satisfactory accuracy and quantitative analysis of ketone bodies, glucose, protein, occult blood, pH, and leukocytes in human urine samples with high-resolution concentrations, achieving results similar to those obtained with hospital instruments. The proposed device is portable and user-friendly, providing convenient colorimetric analysis for urine. Furthermore, the proposed device also has considerable potential for the development of in vitro diagnosis methods through combination with other test strips.
Nanoparticle Shape Optimization for Tamm-Plasmon-Polariton-Based Organic Solar Cells in the Visible Spectral Range
Authors: Bikbaev Rashid , Vetrov Stepan , Timofeev Ivan , Shabanov Vasily
Published: October 2022

The effect of the shape of the nanoparticles and the polarization of incident light on the surface current density and the efficiency of an organic solar cell based on the Tamm plasmon polariton is investigated. In the cases of both elongated and flattened nanoparticles, it is shown that the efficiency of such a solar cell is increased when the electric field vector is parallel to the largest axis of the spheroid.
Mitigating the Adverse Effects of Semi-Arid Climate on Capsicum Cultivation by Using the Retractable Roof Production System
Authors: Nguyen Giao N. , Lantzke Neil
Published: October 2022

Capsicum (Capsicum annuum L.) belongs to the Solanaceae family and is an economically important vegetable crop. However, the crop is very sensitive to adverse weather conditions such as high temperatures and excessive sunlight, which cause flower and young fruit to drop and sunscald to mature fruits. Using protected cultivation such as shade covers or net houses is a feasible agronomic approach to protect the crop from high light intensity, which increases plant growth, reduces fruit damage, and increases marketable fruit yield and quality. Low-cost protected cropping options such as fixed-roof net houses have proved cost-effective and suitable for fruiting vegetable production in semi-arid climatic regions. However, this structure type is unable to protect the crops from rainfall, is prone to cyclone damage and is inflexible to accommodate various vegetable crops which have different requirements for healthy and productive growth. This study was conducted in Carnarvon, which has semi-arid climatic conditions and is a key horticultural district of Western Australia, to compare the Retractable Roof Production System (RRPS) and open field (OF) conditions in the production of capsicum. The data showed that the RRPS modified the internal light, temperature and humidity in favour of the capsicum crop. The RRPS-grown capsicum had higher plant height and lower canopy temperature on hot days than those in the OF. The mean marketable fruit yield of capsicum varieties grown in the RRPS was significantly higher than those in the OF with fruit yields of 97 t ha−1 and 39.1 t ha−1, respectively, but the fruit quality remained unchanged. Overall, the data suggest that the RRPS altered the internal microenvironment and enhanced capsicum crop growth, physiology and fruit yield by setting climatic parameters to automatically control the opening and closing of the roof, the insect net and side curtains, and activation of the fogging system. The future perspective of the deployment of RRPS for capsicum production under climatic conditions in Carnarvon was also discussed.
An Integrative Review on the Main Flavonoids Found in Some Species of the Myrtaceae Family: Phytochemical Characterization, Health Benefits and Development of Products
Authors: da Veiga Correia Vinícius Tadeu , da Silva Pâmela Rocha , Ribeiro Carla Mariele Silva , Ramos Ana Luiza Coeli Cruz , Mazzinghy Ana Carolina do Carmo , Silva Viviane Dias Medeiros , Júnior Afonso Henrique Oliveira , Nunes Bruna Vieira , Vieira Ana Luiza Santos , Ribeiro Lucas Victor , de Paula Ana Cardoso Clemente Filha Ferreira , Melo Júlio Onésio Ferreira , Fante Camila Argenta
Published: October 2022

This integrative review aims to identify the main flavonoids present in some species of the Myrtaceae family. Studies published between 2016 and 2022 were selected, specifically those which were fully available and written in Portuguese, English, or Spanish, and which were related to the fruits araçá (Psidium cattleianum), cambuí (Myrciaria floribunda), gabiroba (Campomanesia xanthocarpa), jabuticaba (Plinia cauliflora), and jambolan (Syzygium cumini). Scientific studies were gathered and selected in Google Scholar, Scielo, and Science Direct indexed databases, out of which 14 were about araçá, 7 concerned cambuí, 4 were about gabiroba, 29 were related to jabuticaba, and 33 concerned jambolan, when we observed the pre-established inclusion criteria. Results showed that the anthocyanins, such as cyanidin, petunidin, malvidin, and delphinidin, were the mostly identified class of flavonoids in plants of the Myrtaceae family, mainly relating to the purple/reddish color of the evaluated fruits. Other compounds, such as catechin, epicatechin, quercetin, and rutin were also identified in different constituent fractions, such as leaves, peel, pulp, seeds, and in developed products, such as jams, desserts, wines, teas, and other beverages. It is also worth noting the positive health effects verified in these studies, such as anti-inflammatory qualities for jambolan, antidiabetic qualities for gabiroba, antioxidant qualities for araçá, and cardioprotective actions for jabuticaba, which are related to the presence of these phytochemicals. Therefore, it is possible to point out that flavonoids are important compounds in the chemical constitution of the studied plants of the Myrtaceae family, with promising potential in the development of new products by the food, chemical, and pharmaceutical industries due to their bioactive properties.
Insecticidal Activity of Plant Secondary Metabolites
Authors: Benelli Giovanni , Maggi Filippo
Published: October 2022

Plant-insect interactions are one of the most fascinating fields of research attracting biologists, entomologists, botanists, as well as a wide range of multidisciplinary researchers [...]
Rootstock Priming with Shikimic Acid and Streptomyces griseus for Growth, Productivity, Physio-Biochemical, and Anatomical Characterisation of Tomato Grown under Cold Stress
Authors: Sayed Eman G. , Mahmoud Abdel Wahab M. , Abdel-Wahab Ahmed , El-bahbohy Reham M. , Azoz Samah N.
Published: October 2022

With this research, we aimed to determine the impact of grafting and rootstock seed treated with Streptomyces griseus (MT210913) (S. griseus) or shikimic acid (SA) at a 60 ppm concentration on tomato (Solanum lycopersicum L.) production grown under low-temperature conditions. Two open-field trials were performed during both winter seasons of 2020 and 2021 at the Experimental Farm, Faculty of Agriculture, Cairo University, Giza, Egypt. A tomato cultivar (Peto 86) was used as a scion and two tomato phenotypes were employed as rootstocks (Solanum cheesmaniae L. (line LA 524) and GS hybrid), as well as self-grafted as a control. Effects of sub-optimal temperature on vegetative growth, yield, and fruit quality were tested. The results indicate that, under cold stress, rootstock seed priming, especially with S. griseus, enhanced plant growth, total yield, and fruit quality properties. GS hybrid rootstock was more effective than that of S. cheesmaniae rootstock in terms of mitigating the negative effect of cold stress. GS hybrid, inoculated with S. griseus, increased the total yield per plant by 10.5% and 5.7% in the first and second seasons, respectively. Higher levels of GA3 and mineral content were noticed in leaves that were grafted and treated with S. griseus compared to the control treatment. Additionally, the great enhancing effects of all anatomical features of tomato plants were recorded with GS hybrid rootstock, inoculated by S. griseus. These results prove that grafting on GS hybrid rootstock treated with S. griseus is a potential choice to alleviate the cold stress of commercial tomato varieties.
Diesel Engine Fault Diagnosis Method Based on Optimized VMD and Improved CNN
Authors: Zhan Xianbiao , Bai Huajun , Yan Hao , Wang Rongcai , Guo Chiming , Jia Xisheng
Published: October 2022

The safe operation of diesel engines performs a vital function in industrial production and life. Because diesel engines often work in harsh environmental conditions, they are prone to failure. Therefore, this paper proposes a fault analysis method based on a combination of optimized variational mode decomposition (VMD) and improved convolutional neural networks (CNN) to address the necessary need for preventive maintenance of diesel engines. The authentic vibration sign is first decomposed by using the (VMD) algorithm, then the greatest range of decomposition layers is decided by using scattering entropy and the useful components are preferentially chosen for reconstruction. The continuous wavelet transform (CWT) records preprocessing method is then delivered to radically change the noise-reduced vibration sign into a time-frequency map, which is fed into the CNN for model coaching and extraction of fault features. Finally, fault classification is realized by support vector machine (SVM) with excellent classification performance. Through preset fault experiments on diesel engines, it is established that the technique proposed in this paper can successfully identify fault states, and the classification accuracy is higher than alternative methods.
Dynamic Risk Assessment of High Slope in Open-Pit Coalmines Based on Interval Trapezoidal Fuzzy Soft Set Method: A Case Study
Authors: Wang Zhiliu , Hu Mengxin , Zhang Peng , Li Xinming , Yin Song
Published: October 2022

Effective high slope risk assessment plays an important role in the safety management and control of the open-pit coal mining process. Traditional slope stability risk assessment methods rarely consider the time factor or evaluate the dynamic change of high slope in an open-pit mine at a certain time in a sensitivity assessment. This paper develops an interval trapezoidal fuzzy soft set method to achieve the high slope dynamic risk evaluation. The proposed dynamic interval trapezoidal fuzzy soft set method for risk assessment of high slope in an open-pit coal mine is developed by integrating the time points and weights of slope risk factors. The extended interval trapezoidal fuzzy soft set was used to calculate the weights of risk factors at different times, and the Fuzzy Analytical Hierarchy Process (FAHP) method was applied to determine the weights of risk factors. The weight change of different risk factors with time can be easily achieved with the proposed method. As a case study, this approach is implemented into a risk assessment model for the north high slope in Shengli #1 open-pit mine located in Xilinhot, Inner Mongolia. The model complies with three time points and contains 4 primary risk factors (S) and 17 secondary risk factors. The results indicated that the hydrological climate conditions and slope geometry conditions were the high risk factors affecting this open-pit coal mine slope. The reasonability and effectiveness of the evaluation results were verified with in-situ observations and measurements. This dynamic risk assessment method is helpful for improving safety management and control for the high slopes of open-pit mines in the coal mining process.
Numerical Prediction of the Performance of Chamfered and Sharp Cutting Tools during Orthogonal Cutting of AISI 1045 Steel
Authors: Tagiuri Zakaria Ahmed M. , Dao Thien-My , Samuel Agnes Marie , Songmene Victor
Published: October 2022

This paper presents a numerical investigation of the effects of chamfered and sharp cemented carbide tools using finite element method-based DEFORM-2D software and cutting parameters on different machining characteristics during the orthogonal cutting of AISI 1045 steel. The objective is to study the interactions between chamfer width, chamfer angle, sharp angle and the cutting speed and feed rate on the cutting temperature, effective stress and wear depth. These effects were investigated statistically using the analysis of variance (ANOVA) test. The obtained numerical results showed that for the chamfer tool, high values of temperature, stress and wear depth were obtained for chamfer widths of 0.35 mm and 0.45 mm. In terms of combined influences, for the cutting temperature and stress, a strong interaction between the cutting speed and chamfer width was obtained. For the sharp tool design, and in terms of temperature, strong interactions are mostly observed between cutting speeds and feed rates. The ANOVA showed that for both chamfer and sharp tools, the feed rate, the cutting speed and their interactions are the most significant parameters that influence temperature and stress.
Autoignition of Methane–Hydrogen Mixtures below 1000 K
Authors: Arutyunov Vladimir , Belyaev Andrey , Arutyunov Artem , Troshin Kirill , Nikitin Aleksey
Published: October 2022

In the range of 800–1200 K, both experiments and kinetic modeling demonstrate a significant difference in the dependence of the ignition delay time of methane and hydrogen on pressure and temperature, with the complex influence of these parameters on the autoignition delay time of methane–hydrogen–air mixtures. In connection with the prospects for the widespread use of methane–hydrogen mixtures in energy production and transport, a detailed analysis of their ignition at temperatures below 1000 K, the most important region from the point of view of their practical application, is carried out. It is shown that such a complex behavior is associated with the transition in this temperature range from low-temperature mechanisms of oxidation of both methane and hydrogen, in which peroxide radicals and molecules play a decisive role, to high-temperature mechanisms of their oxidation, in which simpler radicals dominate. A kinetic interpretation of the processes occurring in this case is proposed.
The Applicability of Shear Wave Elastography to Assess Myotendinous Stiffness of Lower Limbs during an Incremental Isometric Strength Test
Authors: Bravo-Sánchez Alfredo , Abián Pablo , Lucenteforte Giacomo , Jiménez Fernando , Abián-Vicén Javier
Published: October 2022

The aim of the study was to describe the applicability of shear wave elastography to assess muscular and tendinous stiffness of the lower limbs during an incremental isometric strength test and to differentiate the stiffness evolution between superficial and deep muscle regions. Dominant rectus femoris and patellar tendons of 30 physically active people (28.3 ± 9.2 years, 173.2 ± 7.7 cm, 76.2 ± 12.6 kg) were measured in different isometric strength conditions (relaxed muscle, and at 10%, 20%, 30%, 40%, 50% and 60% of maximal voluntary contraction (MVC)). The percentage of success was >85% at all muscle contraction intensities for rectus femoris muscles but only in a relaxed condition for patellar tendons. Rectus femoris stiffness significantly increased compared to the relaxed condition from 30% to 60% MVC (p ≤ 0.011) in superficial muscle regions, and from 10% to 60% MVC (p ≤ 0.002) in deep muscle regions. Deep muscle regions showed higher stiffness values than superficial muscle regions at 30% MVC (51.46 ± 38.17 vs. 31.83 ± 17.05 kPa; p = 0.019), 40% MVC (75.21 ± 42.27 vs. 51.25 ± 28.90 kPa; p = 0.018), 50% MCV (85.34 ± 45.05 vs. 61.16 ± 37.03 kPa; p = 0.034) and 60% MVC (109.29 ± 40.04 vs. 76.67 ± 36.07 kPa; p = 0.002). Rectus femoris stiffness increased during the incremental isometric contraction test, and inter-region differences were found at 30% MVC.
Reorganization of Brain Functional Network during Task Switching before and after Mental Fatigue
Authors: Zhong Hongyang , Wang Jie , Li Huayun , Tian Jinghong , Fang Jiaqi , Xu Yanting , Jiao Weidong , Li Gang
Published: October 2022

Mental fatigue is a widely studied topic on account of its serious negative effects. But how the neural mechanism of task switching before and after mental fatigue remains a question. To this end, this study aims to use brain functional network features to explore the answer to this question. Specifically, task-state EEG signals were recorded from 20 participants. The tasks include a 400-s 2-back-task (2-BT), followed by a 6480-s of mental arithmetic task (MAT), and then a 400-s 2-BT. Network features and functional connections were extracted and analyzed based on the selected task switching states, referred to from Pre_2-BT to Pre_MAT before mental fatigue and from Post_MAT to Post_2-BT after mental fatigue. The results showed that mental fatigue has been successfully induced by long-term MAT based on the significant changes in network characteristics and the high classification accuracy of 98% obtained with Support Vector Machines (SVM) between Pre_2-BT and Post_2-BT. when the task switched from Pre_2-BT to Pre_MAT, delta and beta rhythms exhibited significant changes among all network features and the selected functional connections showed an enhanced trend. As for the task switched from Post_MAT to Post_2-BT, the network features and selected functional connectivity of beta rhythm were opposite to the trend of task switching before mental fatigue. Our findings provide new insights to understand the neural mechanism of the brain in the process of task switching and indicate that the network features and functional connections of beta rhythm can be used as neural markers for task switching before and after mental fatigue.
A Dynamic Multi-Mobile Agent Itinerary Planning Approach in Wireless Sensor Networks via Intuitionistic Fuzzy Set
Authors: Alsboui Tariq , Hill Richard , Al-Aqrabi Hussain , Farid Hafiz Muhammad Athar , Riaz Muhammad , Iram Shamaila , Shakeel Hafiz Muhammad , Hussain Muhammad
Published: October 2022

In recent research developments, the application of mobile agents (MAs) has attracted extensive research in wireless sensor networks (WSNs) due to the unique benefits it offers, such as energy conservation, network bandwidth saving, and flexibility of open usage for various WSN applications. The majority of the proposed research ideas on dynamic itinerary planning agent-based algorithms are efficient when dealing with node failure as a result of energy depletion. However, they generate inefficient groups for MAs itineraries, which introduces a delay in broadcasting data return back to the sink node, and they do not consider the expanding size of the MAs during moving towards a sequence of related nodes. In order to rectify these research issues, we propose a new Graph-based Dynamic Multi-Mobile Agent Itinerary Planning approach (GDMIP). GDMIP works with “Directed Acyclic Graph” (DAG) techniques and distributes sensor nodes into various and efficient group-based shortest-identified routes, which cover all nodes in the network using intuitionistic fuzzy sets. MAs are restricted from moving in the predefined path and routes and are responsible for collecting data from the assigned groups. The experimental results of our proposed work show the effectiveness and expediency compared to the published approaches. Therefore, our proposed algorithm is more energy efficient and effective for task delay (time).
Validating Confined Flame Noise Simulation Using External Sensor
Authors: Williamson Andrew J. , Srivastava Shubham , Sallam Khaled A.
Published: October 2022

Advancements in lean premixed combustion have increased the efficiency and reduced the amount of greenhouse gas emissions, but they have led to increased noise emissions due to higher turbulence and mixing fluctuations. This study used an external sensor (microphone) to validate the simulation of the combustion noise of a confined space. An experimental facility with a laboratory-scale furnace was used to carry out the measurement, and the simulation of the confined flame noise was conducted in OpenFOAM. The simulation utilized the Partially Stirred Reactor (PaSR) and a hybrid computational aeroacoustics (CAA) approach using the large eddy simulation (LES)/the Ffwocs Williams–Hawkings (FWH) method. Additionally, unsteady Reynolds-averaged Navier–Stokes (URANS)/the FWH method was tested for a comparison with the LES prediction. A sensor which was placed outside the enclosure for ease of access was then used to validate the results of the numerical model. The sensor data agreed with the LES/FWH results including the amplitude and frequency of the primary combustion peak and the overall sound pressure level (OASPL). This suggested that a sensor which was placed outside the enclosure could serve as a validation tool for the simulation of the confined flames despite the sound reflections from the walls.
Combined SBAS-InSAR and PSO-RF Algorithm for Evaluating the Susceptibility Prediction of Landslide in Complex Mountainous Area: A Case Study of Ludian County, China
Authors: Xiao Bo , Zhao Junsan , Li Dongsheng , Zhao Zhenfeng , Zhou Dingyi , Xi Wenfei , Li Yangyang
Published: October 2022

In complex mountainous areas where earthquakes are frequent, landslide hazards pose a significant threat to human life and property due to their high degree of concealment, complex development mechanism, and abrupt nature. In view of the problems of the existing landslide hazard susceptibility evaluation model, such as poor effectiveness and inaccuracy of landslide hazard data and the need for experts to participate in the calculation of a large number of evaluation factor weight classification statistics. In this paper, a combined SBAS-InSAR (Small Baseline Subsets-Interferometric Synthetic Aperture Radar) and PSO-RF (Particle Swarm Optimization-Random Forest) algorithm was proposed to evaluate the susceptibility of landslide hazards in complex mountainous regions characterized by frequent earthquakes, deep river valleys, and large terrain height differences. First, the SBAS-InSAR technique was used to invert the surface deformation rates of the study area and identified potential landslide hazards. Second, the study area was divided into 412,585 grid cells, and the 16 selected environmental factors were analyzed comprehensively to identify the most effective evaluation factors. Last, 2722 landslide (1361 grid cells) and non-landslide (1361 grid cells) grid cells in the study area were randomly divided into a training dataset (70%) and a test dataset (30%). By analyzing real landslide and non-landslide data, the performances of the PSO-RF algorithm and three other machine learning algorithms, BP (back propagation), SVM (support vector machines), and RF (random forest) algorithms were compared. The results showed that 329 potential landslide hazards were updated using the surface deformation rates and existing landslide cataloguing data. Furthermore, the area under the curve (AUC) value and the accuracy (ACC) of the PSO-RF algorithm were 0.9567 and 0.8874, which were higher than those of the BP (0.8823 and 0.8274), SVM (0.8910 and 0.8311), and RF (0.9293 and 0.8531), respectively. In conclusion, the method put forth in this paper can be effectively updated landslide data sources and implemented a susceptibility prediction assessment of landslide disasters in intricate mountainous areas. The findings can serve as a strong reference for the prevention of landslide hazards and decision-making mitigation by government departments.
Towards More Accurate Determination of the Thermoelectric Properties of Bi2Se3 Epifilms by Suspension via Nanomachining Techniques
Authors: Kim Donguk , Yang Chanuk , Park Yun Daniel
Published: October 2022

We report on the characterization of the thermoelectric properties of Bi2Se3 epifilms. MBE-grown Bi2Se3 films on GaAs (111) A are nanomachined with integrated Pt elements serving as local joule heaters, thermometers, and voltage probes. We suspended a 4 µm × 120 µm Bi2Se3 by nanomachining techniques. Specifically, we selectively etched GaAs buffer/substrate layers by citric acid solution followed by a critical point drying method. We found that the self-heating 3ω method is an appropriate technique for the accurate measurement of the thermal conductivity of suspended Bi2Se3. The measured thermoelectric properties of 200 nm thick Bi2Se3 at room temperature were κ=1.95 W/m K, S=−102.8 μV/K, σ = 75,581 S/m and the figure of merit was ZT=0.12. The study introduces a method to measure thermal conductivity accurately by suspending thin films.
Attention Detection by Heartbeat and Respiratory Features from Radio-Frequency Sensor
Authors: Sharma Pragya , Zhang Zijing , Conroy Thomas B. , Hui Xiaonan , Kan Edwin C.
Published: October 2022

This work presents a study on users’ attention detection with reference to a relaxed inattentive state using an over-the-clothes radio-frequency (RF) sensor. This sensor couples strongly to the internal heart, lung, and diaphragm motion based on the RF near-field coherent sensing principle, without requiring a tension chest belt or skin-contact electrocardiogram. We use cardiac and respiratory features to distinguish attention-engaging vigilance tasks from a relaxed, inattentive baseline state. We demonstrate high-quality vitals from the RF sensor compared to the reference electrocardiogram and respiratory tension belts, as well as similar performance for attention detection, while improving user comfort. Furthermore, we observed a higher vigilance-attention detection accuracy using respiratory features rather than heartbeat features. A high influence of the user’s baseline emotional and arousal levels on the learning model was noted; thus, individual models with personalized prediction were designed for the 20 participants, leading to an average accuracy of 83.2% over unseen test data with a high sensitivity and specificity of 85.0% and 79.8%, respectively
Scale Factor Estimation for Quadrotor Monocular-Vision Positioning Algorithms
Authors: Gómez-Casasola Alejandro , Rodríguez-Cortés Hugo
Published: October 2022

Unmanned aerial vehicle (UAV) autonomous navigation requires access to translational and rotational positions and velocities. Since there is no single sensor to measure all UAV states, it is necessary to fuse information from multiple sensors. This paper proposes a deterministic estimator to reconstruct the scale factor of the position determined by a simultaneous localization and mapping (SLAM) algorithm onboard a quadrotor UAV. The position scale factor is unknown when the SLAM algorithm relies on the information from a monocular camera. Only onboard sensor measurements can feed the estimator; thus, a deterministic observer is designed to rebuild the quadrotor translational velocity. The estimator and the observer are designed following the immersion and invariance method and use inertial and visual measurements. Lyapunov’s arguments prove the asymptotic convergence of observer and estimator errors to zero. The proposed estimator’s and observer’s performance is validated through numerical simulations using a physics-based simulator.
Water Turbidity and Suspended Particulate Matter Concentration at Dredged Material Dumping Sites in the Southern Baltic
Authors: Lednicka Barbara , Kubacka Maria , Freda Włodzimierz , Haule Kamila , Dembska Grażyna , Galer-Tatarowicz Katarzyna , Pazikowska-Sapota Grażyna
Published: October 2022

Dredged material dumping is an activity that causes some of the greatest changes in coastal waters. It results in the need to regularly monitor the properties of seawater related to water quality. In this study, we present the first wide-ranging attempt to correlate seawater turbidity and suspended particulate matter (SPM) concentrations within dumping sites and adjacent waters on the basis of in situ measurements. In the years 2019–2020, we examined four dumping sites, namely Darłowo, Gdynia, Gdańsk, and DCT, located in Polish coastal waters of the Baltic Sea, in the course of four measurement campaigns conducted in the spring, summer, autumn, and winter. The measurements were conducted using a turbidity sensor to determine the turbidity, in formazin turbidity units (FTU), a spectrophotometer to determine the concentrations of nutrients (total phosphorus (P-tot), phosphate phosphorus (P-PO4−3), total nitrogen (N-tot), ammonium nitrogen (N-NH4+), and nitrate nitrogen (N-NO3−)), as well as glass microfiber filters to determine the concentrations of SPM. The analysis of the relationship between the turbidity and SPM within the dumping sites in comparison to reference points showed that the dumping sites are very complex waters and that the issue must be approached locally. The highest turbidity values were registered in the spring, and they correlated linearly with the SPM concentrations (R2 = 0.69). Moreover, we performed a statistical cluster analysis to illustrate the similarities between sampling points in the four dumpsites based on nutrient concentrations. We conclude that the influence of the dumping sites on the local bio-optical and chemical properties significantly exceeds their borders and spreads to the adjacent waters. Nutrient concentrations in many cases exceeded the legal policy limits.
Special Issue “Computer Aided Diagnosis Sensors”
Authors: El-Baz Ayman , Giridharan Guruprasad A. , Shalaby Ahmed , Mahmoud Ali H. , Ghazal Mohammed
Published: October 2022

Sensors used to diagnose, monitor or treat diseases in the medical domain are known as medical sensors [...]
Farmers and Local Residents Collaborate: Application of a Participatory Citizen Science Approach to Characterising Air Quality in a Rural Area in The Netherlands
Authors: Woutersen Amber , de Ruiter Henri , Wesseling Joost , Hendricx Wouter , Blokhuis Christa , van Ratingen Sjoerd , Vegt Kirsten , Voogt Marita
Published: October 2022

In rural areas, livestock farming is a source of environmental concern. We describe a citizen science (CS) project in Venray, the Netherlands, where air quality was measured at livestock farms and surrounding residential premises. We used low-cost methods to measure air quality components and facilitated a dialogue between stakeholders about the results and solutions for cleaner air. PM2.5 and PM10 were measured using Nova Fitness SDS011 sensors, nitrogen dioxide (NO2) and ammonia (NH3) using Palmes tubes and odour annoyance was reported. Particulate Matter (PM) concentrations were higher close to layer farms, but elevated concentrations were limited at other farms and residential locations. NO2 concentrations were elevated near busy roads, and higher NH3 values were measured near livestock farms. Reporting of odour annoyance was limited, yet during the dialogue residents indicated that this was their largest concern. While both farmers and residents agreed with the general conclusions, they still preferred opposing measures. We conclude that characterisation of air quality using low-cost methods is possible, but expert guidance is needed. Moreover, education, commitment of participants and involvement of independent parties are crucial to ensuring a productive dialogue between stakeholders. The insights gained by participants and resulting dialogue were the greatest benefits of this CS approach.
A Novel Expectation-Maximization-Based Blind Receiver for Low-Complexity Uplink STLC-NOMA Systems
Authors: Lee Ki-Hun , Jung Bang Chul
Published: October 2022

In this paper, we revisit a two-user space-time line coded uplink non-orthogonal multiple access (STLC-NOMA) system for Internet-of-things (IoT) networks and propose a novel low-complexity STLC-NOMA system. The basic idea is that both IoT devices (stations: STAs) employ amplitude-shift keying (ASK) modulators and align their modulated symbols to in-phase and quadrature axes, respectively, before the STLC encoding. The phase distortion caused by wireless channels becomes compensated at the receiver side with the STLC, and thus each STA’s signals are still aligned on their axes at the access point (AP) in the proposed uplink STLC-NOMA system. Then, the AP can decode the signals transmitted from STAs via a single-user maximum-likelihood (ML) detector with low-complexity, while the conventional uplink STLC-NOMA system exploits a multi-user joint ML detector with relatively high-complexity. We mathematically analyze the exact BER performance of the proposed uplink STLC-NOMA system. Furthermore, we propose a novel expectation-maximization (EM)-based blind energy estimation (BEE) algorithm to jointly estimate both transmit power and effective channel gain of each STA without the help of pilot signals at the AP. Somewhat interestingly, the proposed BEE algorithm works well even in short-packet transmission scenarios. It is worth noting that the proposed uplink STLC-NOMA architecture outperforms the conventional STLC-NOMA technique in terms of bit-error-rate (BER), especially with high-order modulation schemes, even though it requires lower computation complexity than the conventional technique at the receiver.
A Decision-Making Strategy for Car Following Based on Naturalist Driving Data via Deep Reinforcement Learning
Authors: Li Wenli , Zhang Yousong , Shi Xiaohui , Qiu Fanke
Published: October 2022

To improve the satisfaction and acceptance of automatic driving, we propose a deep reinforcement learning (DRL)-based autonomous car-following (CF) decision-making strategy using naturalist driving data (NDD). This study examines the traits of CF behavior using 1341 pairs of CF events taken from the Next Generation Simulation (NGSIM) data. Furthermore, in order to improve the random exploration of the agent’s action, the dynamic characteristics of the speed-acceleration distribution are established in accordance with NDD. The action’s varying constraints are achieved via a normal distribution 3σ boundary point-to-fit curve. A multiobjective reward function is designed considering safety, efficiency, and comfort, according to the time headway (THW) probability density distribution. The introduction of a penalty reward in mechanical energy allows the agent to internalize negative experiences. Next, a model of agent-environment interaction for CF decision-making control is built using the deep deterministic policy gradient (DDPG) method, which can explore complicated environments. Finally, extensive simulation experiments validate the effectiveness and accuracy of our proposal, and the driving strategy is learned through real-world driving data, which is better than human data.