bluuebunny/crossref_metadata_2025 huggingface dataset:
Polymer Composites with Quantum Dots as Potential Electrode Materials for Supercapacitors Application: A Review
Authors: Das Himadri Tanaya , Barai Paritosh , Dutta Swapnamoy , Das Nigamananda , Das Payaswini , Roy Madhusudan , Alauddin Md. , Barai Hasi Rani
Published: March 2022

Owing to the nanometer size range, Quantum Dots (QDs) have exhibited unique physical and chemical properties which are favourable for different applications. Especially, due to their quantum confinement effect, excellent optoelectronic characteristics is been observed. This considerable progress has not only uplifted the singular usage of QDs, but also encouraged to prepare various hybrid materials to achieve superior efficiency by eliminating certain shortcomings. Such issues can be overcome by compositing QDs with polymers. Via employing polymer composite with QDs (PQDs) for supercapacitor applications, adequate conductivity, stability, excellent energy density, and better specific capacitance is been achieved which we have elaborately discussed in this review. Researchers have already explored various types of polymer nanocomposite with different QDs such as carbonaceous QDs, transition metal oxide/sulphide QDs etc. as electrode material for supercapacitor application. Synthesis, application outcome, benefits, and drawbacks of these are explained to portray a better understanding. From the existing studies it is clearly confirmed that with using PQDs electrical conductivity, electrochemical reactivity, and the charge accumulation on the surface have prominently been improved which effected the fabricated supercapacitor device performance. More comprehensive fundamentals and observations are explained in the current review which indicates their promising scopes in upcoming times.
Investigation of Calcination of Sepia officinalis Cuttlefish Bone for Reinforcement of Polyvinyl Alcohol Added Nano-Size Montmorillonite
Authors: Thum Jia-Yi , Sin Lee Tin , Bee Soo-Tueen , Lim Jun-Ven , Bee Soo-Ling
Published: March 2022

This study aims to investigate the effects on calcination of Sepia officinalis cuttlefish bone (cuttlebone) to enhance reinforcement of polyvinyl alcohol (PVOH) added with nano-size montmorillonite (MMT) blends as potential bio-compatible materials. The polyvinyl alcohol-cuttlebone-montmorillonite nanocomposites were prepared using the solution casting method. Calcined cuttlebone particles were added to the PVOH matrix at different amount of 2 and 5 parts per hundred resin (phr) along with MMT ranging from 1 to 3 phr. Results showed that the tensile strength of cuttlebone-added PVOH-MMT composites at fixed 1 phr MMT was observed to be marginally lower when the cuttlebone increased from 2 phr to 5 phr due to the poor distribution of agglomerated particles. Nevertheless, at higher loading level of MMT, it was found that the addition of cuttlebone at 5 phr exhibited a reinforcing effect in PVOH-MMT blends. This is consistent with the scanning electron microscopy observation, where dispersion of a higher amount of cuttlebone in PVOH-MMT blends was observed to be more homogeneous than a lower amount of cuttlebone. Moreover, based on the X-ray diffraction analysis, the addition of cuttlebone significantly enhanced the intercalation effect of MMT particles in the PVOH matrix. Furthermore, the observation from infrared spectroscopy shows the amount of hydroxyl group for all composites reduced gradually with the increasing amount of cuttlebone. The addition of cuttlebone showed a “red shift” effect, indicating the formation of hydrogen bonds induced by cuttlebone. Lastly, lower enthalpy of melting was detected in relation to higher loading level of cuttlebone embedded in PVOH-MMT blends through differential scanning calorimetry. In conclusion, the blending of cuttlebone in PVOH-MMT is favorable to obtain better properties of composites.
Dynamic Modeling of a Nonlinear Two-Wheeled Robot Using Data-Driven Approach
Authors: Khan Muhammad Aseer , Baig Dur-e-Zehra , Ashraf Bilal , Ali Husan , Rashid Junaid , Kim Jungeun
Published: March 2022

A system identification of a two-wheeled robot (TWR) using a data-driven approach from its fundamental nonlinear kinematics is investigated. The fundamental model of the TWR is implemented in a Simulink environment and tested at various input/output operating conditions. The testing outcome of TWR’s fundamental dynamics generated 12 datasets. These datasets are used for system identification using simple autoregressive exogenous (ARX) and non-linear auto-regressive exogenous (NLARX) models. Initially the ARX structure is heuristically selected and estimated through a single operating condition. We conclude that the single ARX model does not satisfy TWR dynamics for all datasets in term of fitness. However, NLARX fitted the 12 estimated datasets and 2 validation datasets using sigmoid nonlinearity. The obtained results are compared with TWR’s fundamental dynamics and predicted outputs of the NLARX showed more than 98% accuracy at various operating conditions.
Effects of Methanol Application on Carbon Emissions and Pollutant Emissions Using a Passenger Vehicle
Authors: Zhang Zhao , Wen Mingsheng , Cui Yanqing , Ming Zhenyang , Wang Tongjin , Zhang Chuanqi , Ampah Jeffrey Dankwa , Jin Chao , Huang Haozhong , Liu Haifeng
Published: March 2022

Methanol, as a promising carbon-neutral fuel, has become a research hotspot worldwide. In this study, pure gasoline and gasoline blended with five different volume ratios of methanol (10%, 20%, 30%, 50%, and 75%) were selected as test fuels, which were referred to as M0, M10, M20, M30, M50, and M75. The experiments on carbon and pollutant emissions and performance were carried out on a passenger vehicle with gasoline direct injection (GDI) turbocharged engine using the steady-state, new European driving cycle (NEDC), and acceleration approaches. The results show that under steady-state conditions, as the methanol blending ratio increases, the volume of fuel consumption increases. Compared with pure gasoline, the equivalent fuel consumption and the CO2 emissions are reduced by 0.95 L/100 km (10.6%) and 18.95 g/km (9.6%) in maximum extent by fueling M75, respectively. In the NEDC, the CO2 emissions of M30 are reduced by 5.46 g/km (3.7%) compared with pure gasoline. After blending methanol in gasoline, CO emissions increase, and the emissions of NOx, THC, and PM decrease. The acceleration time is shortened with the increase of blending ratio of methanol. The application of methanol reduces the combustion CO2 emissions by 10% and improves the pollutant emissions.
Efficiency of Penicillium canescens in Dissipating PAH in Industrial Aged Contaminated Soil Microcosms and Its Impact on Soil Organic Matter and Ecotoxicity
Authors: Veignie Etienne , Rafin Catherine
Published: March 2022

The filamentous fungus Penicillium canescens, isolated from oil-polluted soil, was evaluated for its ability to dissipate high-molecular-weight polycyclic aromatic hydrocarbons (PAH). The study was conducted in a microcosm containing 180 g of historical PAH-contaminated soil under non-sterile conditions with two incubation temperatures (14 °C and 18 °C) on a 12-h cycle. The experiment was conducted over 8 months, with four experimental conditions created by varying the volumes of the bulking agent and vegetable oil (olive oil) and the time of addition of these compounds. The PAH dissipation performance of the fungal augmentation treatment was compared with that achieved with a biostimulated soil (bulking agent and vegetable oil) and with the untreated soil as control. The greatest PAH dissipation was obtained with P. canescens bioaugmentation (35.71% ± 1.73), with 13 of the 16 US EPA PAH significantly dissipated, at rates above 18%, and particularly high-molecular-weight PAH, composed of more than three fused aromatic rings. Nematode toxicity tests indicated a significant decrease in the toxicity of soil bioaugmented by this fungus. Fulvic and humic contents were significantly increased by this treatment. All these results suggest that bioaugmentation with P. canescens can be used to restore soils with long-term PAH contamination.
Sputtering Yields of Si Bombarded with 10–540-keV C60 Ions
Authors: Narumi Kazumasa , Naramoto Hiroshi , Yamada Keisuke , Chiba Atsuya , Saitoh Yuichi
Published: March 2022

Sputtering yields of Si have been measured for C60 ions in the energy range from 10 to 540 keV, where the nuclear stopping is dominant, by measuring thickness change of a pre-amorphized layer with conventional Rutherford-backscattering spectroscopy. The measured sputtering yield shows the maximum, which is approximately 600 Si/C60, around 100 keV. Comparing with the sputtering yields for a monatomic ion calculated both based on the linear-collision-cascade theory of Sigmund and using the SRIM2008 code, nonlinear effect on the sputtering yield has been observed. The nonlinear effect depends on the energy of C60 ions: it is very large around the energies where the sputtering yield has the maximum and hardly observed at 10 keV.
Preaching Addressing Environmental Crises through the Use of Scripture: An Exploration of a Practical Theological Methodology
Authors: Kim-Cragg HyeRan
Published: March 2022

This article considers the critical roles of preaching in addressing the environmental crises by way of engaging with Paul Ballard’s work as a particular practical theological methodology, namely the use of Scripture. This methodological consideration is followed by highlighting the work of the Earth Bible Team, which compliments Ballard’s work. Both works are used as an example of a homiletical practice as well as a learning exercise, demonstrating how Scripture can be used as a homiletical resource of and hermeneutical source for doing practical theology with an eye to address environmental crises.
Liturgical Framing of Trials in 10th to 11th Century Catalonia
Authors: Rodenbusch Cornel-Peter
Published: March 2022

This paper focuses on the question of how place, time, ritual, and liturgy were interconnected before, during, and after trials in the tenth and eleventh centuries in what is today Catalonia. It does so by highlighting cases that show that Visigothic law heavily affected the way in which trials were organized, while simultaneously leaving enough space for the liturgy and the divine to impact legal customs. This article aims to showcase these dynamics, which combine space and time with liturgy in a well-articulated framework of legal procedure that formed part of how people experienced the law and its application.
Tetrasomy 9p, a Prenatal Challenge: Two Novel Cases
Authors: Zaghi Mahtab , Janssens Katrien , Hectors Wim , Loquet Philip , Blaumeiser Bettina
Published: March 2022

Tetrasomy 9p is a chromosomal disorder characterized by the presence of a supernumerary chromosome. This rare abnormality exhibits a broad phenotypic variability and is not clearly distinguishable from other more frequent aneuploidies in the prenatal setting. We present two prenatal cases with dissimilar phenotypes, one with solely increased fetal nuchal translucency and one with multiple congenital anomalies, and discuss prior studies. These cases illustrate the difficulty of prenatally diagnosing this condition based on phenotypic information alone. We conclude that invasive prenatal diagnosis with (molecular) karyotyping is the best choice for the prenatal detection of tetrasomy 9p.
Placental Maternal Vascular Malperfusion Is Associated with Prepregnancy and Early Pregnancy Maternal Cardiovascular and Thrombotic Profiles
Authors: McBride Carole A. , Bernstein Ira M. , Sybenga Amelia B. , McLean Kelley C. , Orfeo Thomas , Bravo Maria Cristina
Published: March 2022

Characteristics of maternal vascular malperfusion (MVM) are frequently observed in placentas from pregnancies impacted by preeclampsia, intrauterine growth restriction, preterm labor, and intrauterine fetal demise. We sought to evaluate the associations of features of MVM with subclinical measures of cardiovascular health and coagulation potential in healthy young women. Sixty-three healthy young women were recruited and assessed prior to pregnancy on cycle day 9 ± 4, at gestational age 90 ± 6 of early pregnancy, and gestational age 216 ± 5 of late pregnancy. Women were assessed for plasma volume, blood pressure, response to volume loading, cardiac output, and uterine hemodynamics. Platelet-poor plasma was collected to assess thrombin generation on a subset of 33 women at all time points. Following delivery, placentas were collected and analyzed for evidence of MVM. Thrombin generation (TG) was evaluated in the presence of tissue factor (TF) with and without recombinant soluble thrombomodulin (TM). For each, we compared TG lagtime, peak level, and endogenous thrombin potential (ETP). Comparisons were made between dichotomized presence and absence of each individual feature of MVM and cardiovascular and coagulation features. Mean ± standard deviation are presented. Women were 31 ± 4 years of age, body mass index of 24 ± 5 kg/m2, 86% white race, and 80% nulliparous. MVM occurred in 70% of placentas, with infarcts and agglutination (44%), decidual arteriopathy (40%), accelerated villous maturation (32%), placental hypoplasia (29%), and distal villous hypoplasia (17%) documented. Decidual arteriopathy and distal villous hypoplasia were associated with prepregnancy maternal physiology, including decreased plasma volume and subclinical cardiovascular variations. All assessed MVM characteristics had identifiable early pregnancy physiologic characteristics consistent with altered cardiovascular function and decreased uterine response to pregnancy when compared with women who did and did not develop MVM. Accelerated villous maturation was the only MVM feature to differ by thrombin generation parameters in early pregnancy. Thrombin generation potential and blood pressure were elevated in late pregnancy in women who developed decidual arteriopathy. Prepregnancy health status and adaptation to pregnancy play important roles in pregnancy outcomes. Both cardiovascular health and thrombin generation potential may influence early placentation. Longitudinal assessment of subclinical maternal factors may allow for better understanding of the etiologies of MVM lesions, as well as allow for identification of a timeline of the origins of placental pathologies.
Gender Diversity in the Boardroom and Corporate Cash Holdings: The Moderating Effect of Investor Protection
Authors: Wan Ismail Wan Adibah , Kamarudin Khairul Anuar , Gupta Namrata , Harymawan Iman
Published: March 2022

This paper investigates whether gender diversity in the boardroom is associated with corporate cash holdings and whether investor protection moderates the effect of corporate board gender diversity on corporate cash holdings. Using 20,750 firm-year observations from 33 countries, our analyses show that firms with high levels of corporate board gender diversity exhibit low corporate cash holdings. Furthermore, firms in countries with high levels of investor protection have low corporate cash holdings. Moreover, the negative association between board gender diversity and corporate cash holdings is weaker in high-level investor protection countries than in low-level investor protection countries. Our results are robust to various specification tests, such as the endogeneity issue, weighted least-squares regression, the global economic crisis effect, alternative measures for corporate cash holdings, and various country-level institutional features. Taken together, the findings reveal that board gender diversity and investor protection have significant influences on corporate cash holdings. These findings have significant implications for politicians, governments, and regulators in devising policies relating to the United Nations Sustainable Development Goal (SDG Number 5) on achieving gender equality and women empowerment.
Land Surface Phenology Retrieval through Spectral and Angular Harmonization of Landsat-8, Sentinel-2 and Gaofen-1 Data
Authors: Lu Jun , He Tao , Song Dan-Xia , Wang Cai-Qun
Published: March 2022

Land Surface Phenology is an important characteristic of vegetation, which can be informative of its response to climate change. However, satellite-based identification of vegetation transition dates is hindered by inconsistencies in different observation platforms, including band settings, viewing angles, and scale effects. Therefore, time-series data with high consistency are necessary for monitoring vegetation phenology. This study proposes a data harmonization approach that involves band conversion and bidirectional reflectance distribution function (BRDF) correction to create normalized reflectance from Landsat-8, Sentinel-2A, and Gaofen-1 (GF-1) satellite data, characterized by the same spectral and illumination-viewing angles as the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Nadir BRDF Adjusted Reflectance (NBAR). The harmonized data are then subjected to the spatial and temporal adaptive reflectance fusion model (STARFM) to produce time-series data with high spatio–temporal resolution. Finally, the transition date of typical vegetation was estimated using regular 30 m spatial resolution data. The results show that the data harmonization method proposed in this study assists in improving the consistency of different observations under different viewing angles. The fusion result of STARFM was improved after eliminating differences in the input data, and the accuracy of the remote-sensing-based vegetation transition date was improved by the fused time-series curve with the input of harmonized data. The root mean square error (RMSE) estimation of the vegetation transition date decreased by 9.58 days. We concluded that data harmonization eliminates the viewing-angle effect and is essential for time-series vegetation monitoring through improved data fusion.
Biogeochemical Model Optimization by Using Satellite-Derived Phytoplankton Functional Type Data and BGC-Argo Observations in the Northern South China Sea
Authors: Shu Chan , Xiu Peng , Xing Xiaogang , Qiu Guoqiang , Ma Wentao , Brewin Robert J. W. , Ciavatta Stefano
Published: March 2022

Marine biogeochemical models have been widely used to understand ecosystem dynamics and biogeochemical cycles. To resolve more processes, models typically increase in complexity, and require optimization of more parameters. Data assimilation is an essential tool for parameter optimization, which can reduce model uncertainty and improve model predictability. At present, model parameters are often adjusted using sporadic in-situ measurements or satellite-derived total chlorophyll-a concentration at sea surface. However, new ocean datasets and satellite products have become available, providing a unique opportunity to further constrain ecosystem models. Biogeochemical-Argo (BGC-Argo) floats are able to observe the ocean interior continuously and satellite phytoplankton functional type (PFT) data has the potential to optimize biogeochemical models with multiple phytoplankton species. In this study, we assess the value of assimilating BGC-Argo measurements and satellite-derived PFT data in a biogeochemical model in the northern South China Sea (SCS) by using a genetic algorithm. The assimilation of the satellite-derived PFT data was found to improve not only the modeled total chlorophyll-a concentration, but also the individual phytoplankton groups at surface. The improvement of simulated surface diatom provided a better representation of subsurface particulate organic carbon (POC). However, using satellite data alone did not improve vertical distributions of chlorophyll-a and POC. Instead, these distributions were improved by combining the satellite data with BGC-Argo data. As the dominant variability of phytoplankton in the northern SCS is at the seasonal timescale, we find that utilizing monthly-averaged BGC-Argo profiles provides an optimal fit between model outputs and measurements in the region, better than using high-frequency measurements.
Near-Infrared Spectroscopy and Mode Cloning (NIR-MC) for In-Situ Analysis of Crude Protein in Bamboo
Authors: Sheng Qingyu , Santos-Rivera Mariana , Ouyang Xiaoguang , Kouba Andrew J. , Vance Carrie K.
Published: March 2022

This study develops Near-Infrared Spectroscopy (NIRS) and Mode-Cloning (MC) for the rapid assessment of the nutritional quality of bamboo leaves, the primary diet of giant pandas (Ailuropoda melanoleuca) and red pandas (Ailurus fulgens). To test the NIR-MC approach, we evaluated three species of bamboo (Phyllostachys bissetii, Phyllostachys rubromarginata, Phyllostachys aureosulcata). Mode-Cloning incorporated a Slope and Bias Correction (SBC) transform to crude protein prediction models built with NIR spectra taken from Fine–Ground leaves (master mode). The modified models were then applied to spectra from leaves in the satellite minimal processing modes (Course–Ground, Dry–Whole, and Fresh–Whole). The NIR-MC using the SBC yielded a residual prediction deviation (RPD) = 2.73 and 1.84 for Course–Ground and Dry–Whole sample modes, respectively, indicating a good quantitative prediction of crude protein for minimally processed samples that could be easily acquired under field conditions using a portable drier and grinder. The NIR-MC approach also improved the model of crude protein for spectra collected from Fresh–Whole bamboo leaves in the field. Thus, NIR-MC has the potential to provide a real-time prediction of the macronutrient distribution in bamboo in situ, which affects the foraging behavior and dispersion of giant and red pandas in their natural habitats.
Comparing Two Different Development Methods of External Parameter Orthogonalization for Estimating Organic Carbon from Field-Moist Intact Soils by Reflectance Spectroscopy
Authors: Yu Wu , Hong Yongsheng , Chen Songchao , Chen Yiyun , Zhou Lianqing
Published: March 2022

Visible and near-infrared (Vis–NIR) spectroscopy can provide a rapid and inexpensive estimation for soil organic carbon (SOC). However, with respect to field in situ spectroscopy, external environmental factors likely degrade the model accuracy. Among these factors, moisture has the greatest effect on soil spectra. The external parameter orthogonalization (EPO) algorithm in combination with the Chinese soil spectroscopic database (Dataset A, 1566 samples) was investigated to eliminate the interference of the external parameters for SOC estimation. Two different methods of EPO development, namely, laboratory-rewetting archive soil samples and field-collecting actual moist samples, were compared to balance model performance and analytical cost. Memory-based learning (MBL), a local modeling technique, was introduced to compare with partial least square (PLS), a global modeling method. A total of 250 soil samples from Central China were collected. Of these samples, 120 dry ground samples (Dataset B) were rewetted to different moisture levels to develop EPO P1 matrix. Seventy samples (Dataset C) containing field-moist intact and laboratory dry ground soils were used to establish EPO P2 matrix. The remaining 60 samples (Dataset D) also containing field-moist intact and laboratory dry ground soils were employed to validate the spectral models developed based on Dataset A. Results showed that EPO could correct the effect of external factors on soil spectra. For PLS, the validation statistics were as follows: no correction, validation R2 = 0.02; P1 correction, validation R2 = 0.56; and P2 correction, validation R2 = 0.57. For MBL, the validation results were as follows: no correction, validation R2 = 0.06; P1 correction, validation R2 = 0.65; and P2 correction, validation R2 = 0.69. The P2 consistently yielded better results than P1 did but simultaneously increased the sampling time and economic cost. The use of the P1 matrix and the MBL algorithm was recommended because it could reduce the cost of establishing in situ models for SOC.
How Much Attenuation Extinguishes mm-Wave Vertically Pointing Radar Return Signals?
Authors: Williams Christopher R.
Published: March 2022

Vertically pointing radars (VPRs) operating at millimeter wavelengths measure the power return from raindrops enabling precipitation retrievals as a function of height. However, as the rain rate increases, there are combinations of rain rate and rain path length that produce sufficient attenuation to prevent the radar from detecting raindrops all the way through rain shafts. This study explores the question: Which rain rate and path length combinations completely extinguish radar return signals for VPRs operating between 3 and 200 GHz? An important step in these simulations is converting attenuated radar reflectivity factor into radar received signal-to-noise ratio (SNR) in order to determine the range where the SNR drops below the receiver detection threshold. Configuring the simulations to mimic a U.S. Department of Energy Atmospheric Radiation Mission (ARM) W-band (95 GHz) radar deployed in Brazil, the simulation results indicate that a W-band radar could observe raindrops above 3.5 km only when the rain rate was less than approximately 4 mm h−1. The deployed W-band radar measurements confirm the simulation results with maximum observed heights ranging between 3 and 4.5 km when a surface disdrometer measured 4 mm h−1 rain rate (based on 25-to-75 percentiles from over 25,000 W-band radar profiles). In summary, this study contributes to our understanding of how rain and atmospheric gas attenuation impacts the performance of millimeter-wave VPRs and will help with the design and configuration of multi-frequency VPRs deployed in future field campaigns.
Investigation of Red Blood Cells by Atomic Force Microscopy
Authors: Sergunova Viktoria , Leesment Stanislav , Kozlov Aleksandr , Inozemtsev Vladimir , Platitsina Polina , Lyapunova Snezhanna , Onufrievich Alexander , Polyakov Vyacheslav , Sherstyukova Ekaterina
Published: March 2022

Currently, much research is devoted to the study of biological objects using atomic force microscopy (AFM). This method’s resolution is superior to the other non-scanning techniques. Our study aims to further emphasize some of the advantages of using AFM as a clinical screening tool. The study focused on red blood cells exposed to various physical and chemical factors, namely hemin, zinc ions, and long-term storage. AFM was used to investigate the morphological, nanostructural, cytoskeletal, and mechanical properties of red blood cells (RBCs). Based on experimental data, a set of important biomarkers determining the status of blood cells have been identified.
Deductive Reasoning and Working Memory Skills in Individuals with Blindness
Authors: Heled Eyal , Elul Noa , Ptito Maurice , Chebat Daniel-Robert
Published: March 2022

Deductive reasoning and working memory are integral parts of executive functioning and are important skills for blind people in everyday life. Despite the importance of these skills, the influence of visual experience on reasoning and working memory skills, as well as on the relationship between these, is unknown. In this study, fifteen participants with congenital blindness (CB), fifteen with late blindness (LB), fifteen sighted blindfolded controls (SbfC), and fifteen sighted participants performed two tasks of deductive reasoning and two of working memory. We found that while the CB and LB participants did not differ in their deductive reasoning abilities, the CB group performed worse than the sighted controls, and the LB group performed better than the SbfC group. Those with CB outperformed all the other groups in both of the working memory tests. Working memory is associated with deductive reasoning in all three visually impaired groups, but not in the sighted group. These findings suggest that deductive reasoning is not a uniform skill, and that it is associated with visual impairment onset, the level of reasoning difficulty, and the degree of working memory load.
Comparison of Empirical Mode Decomposition and Singular Spectrum Analysis for Quick and Robust Detection of Aerodynamic Instabilities in Centrifugal Compressors
Authors: Stajuda Mateusz , García Cava David , Liśkiewicz Grzegorz
Published: March 2022

Aerodynamic instabilities in centrifugal compressors are dangerous phenomena affecting machine efficiency and in severe cases leading to failure of the compressing system. Quick and robust instability detection during compressor operation is a challenge of utmost importance from an economical and safety point of view. Rapid indication of instabilities can be obtained using a pressure signal from the compressor. Detection of aerodynamic instabilities using pressure signal results in specific challenges, as the signal is often highly contaminated with noise, which can influence the performance of detection methods. The aim of this study is to investigate and compare the performance of two non-linear signal processing methods—Empirical Mode Decomposition (EMD) and Singular Spectrum Analysis (SSA)—for aerodynamic instability detection. Two instabilities of different character, local—inlet recirculation and global—surge, are considered. The comparison focuses on the robustness, sensitivity and pace of detection—crucial parameters for a successful detection method. It is shown that both EMD and SSA perform similarly for the analysed machine, despite different underlying principles of the methods. Both EMD and SSA have great potential for instabilities detection, but tuning of their parameters is important for robust detection.
Tree Trunk Recognition in Orchard Autonomous Operations under Different Light Conditions Using a Thermal Camera and Faster R-CNN
Authors: Jiang Ailian , Noguchi Ryozo , Ahamed Tofael
Published: March 2022

In an orchard automation process, a current challenge is to recognize natural landmarks and tree trunks to localize intelligent robots. To overcome low-light conditions and global navigation satellite system (GNSS) signal interruptions under a dense canopy, a thermal camera may be used to recognize tree trunks using a deep learning system. Therefore, the objective of this study was to use a thermal camera to detect tree trunks at different times of the day under low-light conditions using deep learning to allow robots to navigate. Thermal images were collected from the dense canopies of two types of orchards (conventional and joint training systems) under high-light (12–2 PM), low-light (5–6 PM), and no-light (7–8 PM) conditions in August and September 2021 (summertime) in Japan. The detection accuracy for a tree trunk was confirmed by the thermal camera, which observed an average error of 0.16 m for 5 m, 0.24 m for 15 m, and 0.3 m for 20 m distances under high-, low-, and no-light conditions, respectively, in different orientations of the thermal camera. Thermal imagery datasets were augmented to train, validate, and test using the Faster R-CNN deep learning model to detect tree trunks. A total of 12,876 images were used to train the model, 2318 images were used to validate the training process, and 1288 images were used to test the model. The mAP of the model was 0.8529 for validation and 0.8378 for the testing process. The average object detection time was 83 ms for images and 90 ms for videos with the thermal camera set at 11 FPS. The model was compared with the YOLO v3 with same number of datasets and training conditions. In the comparisons, Faster R-CNN achieved a higher accuracy than YOLO v3 in tree truck detection using the thermal camera. Therefore, the results showed that Faster R-CNN can be used to recognize objects using thermal images to enable robot navigation in orchards under different lighting conditions.
Real-Time Object Detection and Classification by UAV Equipped With SAR
Authors: Gromada Krzysztof , Siemiątkowska Barbara , Stecz Wojciech , Płochocki Krystian , Woźniak Karol
Published: March 2022

The article presents real-time object detection and classification methods by unmanned aerial vehicles (UAVs) equipped with a synthetic aperture radar (SAR). Two algorithms have been extensively tested: classic image analysis and convolutional neural networks (YOLOv5). The research resulted in a new method that combines YOLOv5 with post-processing using classic image analysis. It is shown that the new system improves both the classification accuracy and the location of the identified object. The algorithms were implemented and tested on a mobile platform installed on a military-class UAV as the primary unit for online image analysis. The usage of objective low-computational complexity detection algorithms on SAR scans can reduce the size of the scans sent to the ground control station.
Naked-Eye Detection of Morphine by Au@Ag Nanoparticles-Based Colorimetric Chemosensors
Authors: Rohani Bastami Tahereh , Bayat Mansour , Paolesse Roberto
Published: March 2022

In this study, we report a novel and facile colorimetric assay based on silver citrate-coated Au@Ag nanoparticles (Au@AgNPs) as a chemosensor for the naked-eye detection of morphine (MOR). The developed optical sensing approach relied on the aggregation of Au@Ag NPs upon exposure to morphine, which led to an evident color variation from light-yellow to brown. Au@Ag NPs have been prepared by two different protocols, using high- and low-power ultrasonic irradiation. The sonochemical method was essential for the sensing properties of the resulting nanoparticles. This facile sensing method has several advantages including excellent stability, selectivity, prompt detection, and cost-effectiveness.
Selection of Surge Protection Module Components for Communication Lines Using a Genetic Algorithm
Authors: Zieliński Dariusz , Grzechca Damian
Published: March 2022

Among a variety of problems with communication lines, the faults of surge protection modules in railway applications have a significant impact on the transmission line availability, especially if the devices are located in lightning-prone areas or other high-energy exposure areas, such as voltages or current changes. An advanced optimization of the surge protection module is proposed together with its verification, based on simulated waveforms for components and their limitations (e.g., power, peak power, maximum voltages, maximum currents, etc.). It allows for gathering information about the safety margin for each parameter of the components. This can be used to manage the probability of damage to the protection module. The authors have shown the power distribution during exposure that should be considered while developing new devices for railway transportation industry.
Smart Roads for Autonomous Accident Detection and Warnings
Authors: Mateen Abdul , Hanif Muhammad Zahid , Khatri Narayan , Lee Sihyung , Nam Seung Yeob
Published: March 2022

An increasing number of vehicles on the roads increases the risk of accidents. In bad weather (e.g., heavy rainfall, strong winds, storms, and fog), this risk almost doubles due to bad visibility as well as road conditions. If an accident happens, especially in bad weather, it is important to inform approaching vehicles about it. Otherwise, there might be another accident, i.e., a multiple-vehicle collision (MVC). If the Emergency Operations Center (EOC) is not informed in a timely fashion about the incident, fatalities might increase because they do not receive immediate first aid. Detecting humans or animals would undoubtedly provide us with a better answer for reducing human fatalities in traffic accidents. In this research, an accident alert light and sound (AALS) system is proposed for auto accident detection and alerts with all types of vehicles. No changes are required in non-equipped vehicles (nEVs) and EVs because the system is installed on the roadside. The idea behind this research is to make smart roads (SRs) instead of equipping each vehicle with a separate system. Wireless communication is needed only when an accident is detected. This study is based on different sensors that are used to build SRs to detect accidents. Pre-saved locations are used to reduce the time needed to find the accident’s location without the help of a global positioning system (GPS). Additionally, the proposed framework for the AALS also reduces the risk of MVCs.
Preeminently Robust Neural PPG Denoiser
Authors: Kwon Ju Hyeok , Kim So Eui , Kim Na Hye , Lee Eui Chul , Lee Jee Hang
Published: March 2022

Photoplethysmography (PPG) is a simple and cost-efficient technique that effectively measures cardiovascular response by detecting blood volume changes in a noninvasive manner. A practical challenge in the use of PPGs in real-world applications is noise reduction. PPG signals are likely to be compromised by various types of noise, such as scattering or motion artifacts, and removing such compounding noises using a monotonous method is not easy. To this end, this paper proposes a neural PPG denoiser that can robustly remove multiple types of noise from a PPG signal. By casting the noise reduction problem into a signal restoration approach, we aim to achieve a solid performance in the reduction of different noise types using a single neural denoiser built upon transformer-based deep generative models. Using this proposed method, we conducted the experiments on the noise reduction of a PPG signal synthetically contaminated with five types of noise. Following this, we performed a comparative study using six different noise reduction algorithms, each of which is known to be the best model for each noise. Evaluation results of the peak signal-to-noise ratio (PSNR) show that the neural PPG denoiser is superior in three out of five noise types to the performance of conventional noise reduction algorithms. The salt-and-pepper noise type showed the best performance, with the PSNR of the neural PPG denoiser being 36.6080, and the PSNRs of the other methods were 19.8160 and 32.8234. The Poisson noise type performed the worst, showing a PSNR of 33.0090; the PSNRs of other methods were 35.1822 and 33.4795, respectively. Thereafter, an experiment to recover a signal synthesized with two or more of the five noise types was conducted. When the number of mixed noises was two, three, four, and five, the PSNRs were 29.2759, 27.8759, 26.5608, and 25.9402, respectively. Finally, an experiment to recover motion artifacts was also conducted. The synthesized motion artifact signal was created by synthesizing only a certain ratio of the total signal length. As a result of the motion artifact signal restoration, the PSNRs were 25.2872, 22.8240, 21.2901, and 19.9577 at 30%, 50%, 70%, and 90% motion artifact ratios, respectively. In the three experiments conducted, the neural PPG denoiser showed that various types of noise were effectively removed. This proposal contributes to the universal denoising of continuous PPG signals and can be further expanded to denoise continuous signals in the general domain.
Applications of Wireless Sensor Networks and Internet of Things Frameworks in the Industry Revolution 4.0: A Systematic Literature Review
Authors: Majid Mamoona , Habib Shaista , Javed Abdul Rehman , Rizwan Muhammad , Srivastava Gautam , Gadekallu Thippa Reddy , Lin Jerry Chun-Wei
Published: March 2022

The 21st century has seen rapid changes in technology, industry, and social patterns. Most industries have moved towards automation, and human intervention has decreased, which has led to a revolution in industries, named the fourth industrial revolution (Industry 4.0). Industry 4.0 or the fourth industrial revolution (IR 4.0) relies heavily on the Internet of Things (IoT) and wireless sensor networks (WSN). IoT and WSN are used in various control systems, including environmental monitoring, home automation, and chemical/biological attack detection. IoT devices and applications are used to process extracted data from WSN devices and transmit them to remote locations. This systematic literature review offers a wide range of information on Industry 4.0, finds research gaps, and recommends future directions. Seven research questions are addressed in this article: (i) What are the contributions of WSN in IR 4.0? (ii) What are the contributions of IoT in IR 4.0? (iii) What are the types of WSN coverage areas for IR 4.0? (iv) What are the major types of network intruders in WSN and IoT systems? (v) What are the prominent network security attacks in WSN and IoT? (vi) What are the significant issues in IoT and WSN frameworks? and (vii) What are the limitations and research gaps in the existing work? This study mainly focuses on research solutions and new techniques to automate Industry 4.0. In this research, we analyzed over 130 articles from 2014 until 2021. This paper covers several aspects of Industry 4.0, from the designing phase to security needs, from the deployment stage to the classification of the network, the difficulties, challenges, and future directions.
Introducing a Novel Model-Free Multivariable Adaptive Neural Network Controller for Square MIMO Systems
Authors: Mehrafrooz Arash , He Fangpo , Lalbakhsh Ali
Published: March 2022

In this study, a novel Multivariable Adaptive Neural Network Controller (MANNC) is developed for coupled model-free n-input n-output systems. The learning algorithm of the proposed controller does not rely on the model of a system and uses only the history of the system inputs and outputs. The system is considered as a ‘black box’ with no pre-knowledge of its internal structure. By online monitoring and possessing the system inputs and outputs, the parameters of the controller are adjusted. Using the accumulated gradient of the system error along with the Lyapunov stability analysis, the weights’ adjustment convergence of the controller can be observed, and an optimal training number of the controller can be selected. The Lyapunov stability of the system is checked during the entire weight training process to enable the controller to handle any possible nonlinearities of the system. The effectiveness of the MANNC in controlling nonlinear square multiple-input multiple-output (MIMO) systems is demonstrated via three simulation studies covering the cases of a time-invariant nonlinear MIMO system, a time-variant nonlinear MIMO system, and a hybrid MIMO system, respectively. In each case, the performance of the MANNC is compared with that of a properly selected existing counterpart. Simulation results demonstrate that the proposed MANNC is capable of controlling various types of square MIMO systems with much improved performance over its existing counterpart. The unique properties of the MANNC will make it a suitable candidate for many industrial applications.
Skeleton Graph-Neural-Network-Based Human Action Recognition: A Survey
Authors: Feng Miao , Meunier Jean
Published: March 2022

Human action recognition has been applied in many fields, such as video surveillance and human computer interaction, where it helps to improve performance. Numerous reviews of the literature have been done, but rarely have these reviews concentrated on skeleton-graph-based approaches. Connecting the skeleton joints as in the physical appearance can naturally generate a graph. This paper provides an up-to-date review for readers on skeleton graph-neural-network-based human action recognition. After analyzing previous related studies, a new taxonomy for skeleton-GNN-based methods is proposed according to their designs, and their merits and demerits are analyzed. In addition, the datasets and codes are discussed. Finally, future research directions are suggested.
A Novel Reinforcement Learning Collision Avoidance Algorithm for USVs Based on Maneuvering Characteristics and COLREGs
Authors: Fan Yunsheng , Sun Zhe , Wang Guofeng
Published: March 2022

Autonomous collision avoidance technology provides an intelligent method for unmanned surface vehicles’ (USVs) safe and efficient navigation. In this paper, the USV collision avoidance problem under the constraint of the international regulations for preventing collisions at sea (COLREGs) was studied. Here, a reinforcement learning collision avoidance (RLCA) algorithm is proposed that complies with USV maneuverability. Notably, the reinforcement learning agent does not require any prior knowledge about USV collision avoidance from humans to learn collision avoidance motions well. The double-DQN method was used to reduce the overestimation of the action-value function. A dueling network architecture was adopted to clearly distinguish the difference between a great state and an excellent action. Aiming at the problem of agent exploration, a method based on the characteristics of USV collision avoidance, the category-based exploration method, can improve the exploration ability of the USV. Because a large number of turning behaviors in the early steps may affect the training, a method to discard some of the transitions was designed, which can improve the effectiveness of the algorithm. A finite Markov decision process (MDP) that conforms to the USVs’ maneuverability and COLREGs was used for the agent training. The RLCA algorithm was tested in a marine simulation environment in many different USV encounters, which showed a higher average reward. The RLCA algorithm bridged the divide between USV navigation status information and collision avoidance behavior, resulting in successfully planning a safe and economical path to the terminal.
Gamification in Ecology-Oriented Mobile Applications—Typologies and Purposes
Authors: Torres-Toukoumidis Angel , León Diego Vintimilla , De-Santis Andrea , López-López Paulo Carlos
Published: March 2022

Mobile applications viewed as digital social change tools are focused on sustainable development, particularly in topics that address ecology and the environment. In this research, the aim is to systematize a review of the 10 most downloaded mobile applications in this context, but deepening on those that use game elements in their interface, organizing them according to components, mechanics and playful dynamics. Findings demonstrate the role of levels and achievements as more repeated dynamics, and challenges and feedback as more relevant elements in gameplay, and finally, emotions and narratives as components of the gamified experience. The incorporation of these elements in mobile games shows that digital gamification in the environmental context is organized from an alternative perspective, where argumentation, the succession of events, the progressive incorporation of difficulty and the interaction with the computerized system modify the traditional view that sees gamification as a superficial system of points.
Family Context, Parenting and Child Development: An Epigenetic Approach
Authors: Barreto-Zarza Florencia , Arranz-Freijo Enrique B.
Published: March 2022

This paper presents an essay that uses an epigenetic approach to attain an inclusive and in-depth understanding of the influence of family context and quality of parenting on children’s psychological development. Based on the identification of a key developmental process in which interactions are continuously internalised, the approach draws attention to the bidirectional and systemic nature of intrafamily and parenting interactions and highlights the multiple factors that influence them, which are linked to the developmental history of the species, the individual characteristics of both the child and their parents, and contextual variables. In response to these internalised interactions, the body activates epigenetic mechanisms, such as DNA methylation, which may affect the phenotypic expression of the genome. Theoretical and methodological implications are discussed in light of the current process of identifying the biological profiles underlying negative and positive parenting practices. Some insights are offered regarding the challenges and opportunities that parents and policymakers should address in the 21st century in connection with the promotion of positive parenting, taking into account the epigenetic processes triggered by adverse environments for children and their families.