bluuebunny/crossref_metadata_2025 huggingface dataset:
The Digestibility of Hibiscus sabdariffa L. Polyphenols Using an In Vitro Human Digestion Model and Evaluation of Their Antimicrobial Activity
Authors: Majdoub Yassine Oulad El , Ginestra Giovanna , Mandalari Giuseppina , Dugo Paola , Mondello Luigi , Cacciola Francesco
Published: July 2021

Hibiscus sabdariffa L. (H.s.) is a polyphenolic-rich plant commonly consumed either as a beverage or spice. The aim of the present study was to evaluate the in vitro digestibility of H.s. polyphenols using an in vitro model of digestion which simulates the human stomach and small intestine. The bioaccessible polyphenols released in the digested samples were analyzed by liquid chromatography coupled to photodiode array and mass spectrometry detection. H.s. anthocyanins (cyanidin-3-O-sambubioside and delphinidin-3-O-sambubioside) content drastically dropped during the digestion process from 2.91 ± 0.03 µg g−1 and 8.53 ± 0.08 µg g−1 (w/w) CG (Cyanidin-glucoside) in the raw extract, respectively, to 0.12 ± 0.01 µg g−1 0.12 ± 0.01 µg g−1 (w/w) CG at the end of duodenal digestion. Total polyphenols also have shown a decrease from 1192.65 ± 30.37 µg g−1 (w/w) in the raw extract to 282.24 ± 7.21 µg g−1 (w/w) by the end of gastric digestion, in contrast to their increase by the end of duodenal digestion 372.91 ± 3.97 µg g−1 (w/w). On the other hand, the decrease in certain compounds (e.g., caffeoylquinicandcoumaroylquinic acids) was observed during gastric digestion resulting in an increase of quinic acid in the duodenal aliquots, thus suggesting that this compound was derived from the degradation of the more complex hydroxycinnamic acids. H.s. extract also exhibited a bacteriostatic effect against Staphylococcus aureus ATCC 6538 (MIC of 2.5 mg mL−1) and a bactericidal effect against a food isolate of Listeria monocytogenes (MBC of 2.5 mg mL−1). The undigested polyphenols of H.s. in the upper gastrointestinal tract enters the colon, where they are metabolized by the gut microbiota. The present study results showed that resistance of H.s. polyphenols during gastrointestinal digestion might affect their uptake, resulting in a decrease in their digestibility.
Marine n-3 Polyunsaturated Fatty Acids and Bone Mineral Density in Kidney Transplant Recipients: A Randomized, Placebo-Controlled Trial
Authors: Jørgensen Hanne Skou , Eide Ivar Anders , Jenssen Trond , Åsberg Anders , Bollerslev Jens , Godang Kristin , Hartmann Anders , Schmidt Erik Berg , Svensson My
Published: July 2021

Kidney transplant recipients are at high risk of progressive bone loss and low-energy fractures in the years following transplantation. Marine n-3 polyunsaturated fatty acids (n-3 PUFA) supplementation may have beneficial effects on bone strength. The Omega-3 fatty acids in Renal Transplantation (ORENTRA) trial was an investigator initiated, randomized, placebo-controlled trial investigating the effects of marine n-3 PUFA supplementation after kidney transplantation. Effects of supplementation on bone mineral density (BMD) and calcium metabolism were pre-defined secondary endpoints. Adult kidney transplant recipients (n = 132) were randomized to 2.6 g marine n-3 PUFA supplement or olive oil (control) from 8 to 52 weeks post-transplant. Dual energy X-ray absorptiometry was performed to assess changes in bone mineral density of hip, spine, and forearm, as well as trabecular bone score (TBS) of the lumbar spine. Student’s t test was used to assess between-group differences. There were no differences in ΔBMD between the two groups (intervention vs. control) at lumbar spine (−0.020 ± 0.08 vs. −0.007 ± 0.07 g/cm², p = 0.34), total hip (0.001 ± 0.03 vs. −0.005 ± 0.04, p = 0.38), or other skeletal sites in the intention-to-treat analyses. There was no difference in the change in TBS score (0.001 ± 0.096 vs. 0.009 ± 0.102, p = 0.62). Finally, no effect on biochemical parameters of mineral metabolism was seen. Results were similar when analyzed per protocol. In conclusion, we found no significant effect of 44 weeks of supplementation with 2.6 g of marine n-3 PUFA on BMD in kidney transplant recipients.
Momordica charantia L. Extract Protects Hippocampal Neuronal Cells against PAHs-Induced Neurotoxicity: Possible Active Constituents Include Stigmasterol and Vitamin E
Authors: Pattarachotanant Nattaporn , Prasansuklab Anchalee , Tencomnao Tewin
Published: July 2021

Polycyclic aromatic hydrocarbons (PAHs) have been recognized to cause neurobehavioral dysfunctions and disorder of cognition and behavioral patterns in childhood. Momordica charantia L. (MC) has been widely known for its nutraceutical and health-promoting properties. To date, the effect of MC for the prevention and handling of PAHs-induced neurotoxicity has not been reported. In the current study, the neuroprotective effects of MC and its underlying mechanisms were investigated in mouse hippocampal neuronal cell line (HT22); moreover, in silico analysis was performed with the phytochemicals MC to decipher their potential function as neuroprotectants. MC was demonstrated to possess neuroprotective effect by reducing reactive oxygen species’ (ROS’) production and down-regulating cyclin D1, p53, and p38 mitogen-activated protein kinase (MAPK) protein expressions, resulting in the inhibition of cell apoptosis and the normalization of cell cycle progression. Additionally, 28 phytochemicals of MC and their competence on inhibiting cytochrome P450 (CYP: CYP1A1, CYP1A2, and CYP1B1) functions were resolved. In silico analysis of vitamin E and stigmasterol revealed that their binding to either CYP1A1 or CYP1A2 was more efficient than the binding of each positive control (alizarin or purpurin). Together, MC is potentially an interesting neuroprotectant including vitamin E and stigmasterol as probable active components for the prevention for PAHs-induced neurotoxicity.
Impact of Maternal Daily Oral Low-Dose Vitamin A Supplementation on the Mother-Infant Pair: A Randomised Placebo-Controlled Trial in China
Authors: Ding Ye , Hu Ping , Yang Yue , Xu Fangping , Li Fang , Lu Xiaolong , Xie Zhencheng , Wang Zhixu
Published: July 2021

Background: The nutritional status of vitamin A in lactating mothers and infants is still not optimistic. Due to the dietary habits and dietary restrictions of postpartum customs in China, vitamin A supplementation has been advocated as a potential strategy to improve vitamin A status of lactating mothers with inadequate dietary vitamin A intake. Existing clinical trials are limited to single or double high-dose maternal administrations. However, in China, vitamin A supplements are readily available in the form of daily oral low-dose supplements, and the effect of these is unknown. This study aimed to evaluate the effects of daily oral low-dose vitamin A supplementation on the retinol levels in the serum and breast milk of lactating mothers and the health status of infants in China. Methods: Lactating mothers who met the inclusion criteria and planned to continue exclusive breastfeeding were randomly assigned to receive either daily oral vitamin A and D drops (one soft capsule of 1800 IU vitamin A and 600 IU vitamin D2), or a matching placebo for 2 months. Before and after the intervention, dietary intake was investigated by instant photography, and the retinol concentration in maternal serum and breast milk was determined by ultra-high performance liquid chromatography-tandem mass spectrometry. During the trial, the health status of infants was diagnosed by a paediatrician or reported by lactating mothers. A total of 245 participants completed the study, with 117 in the supplementation group and 128 in the control group. Results: After the 2-month intervention, maternal serum retinol concentrations increased in the supplementation group with no change in the control group. Although breast milk retinol concentrations decreased significantly in both groups, the decrease in the supplementation group was significantly lower than that in the control group. However, maternal vitamin A supplementation was not associated with a lower risk of infant febrile illness, respiratory tract infection, diarrhoea, and eczema. Conclusions: Daily oral low-dose vitamin A supplementation is helpful in improving maternal vitamin A status, despite having no effect on infant health status through breast milk.
Assessment of Adherence to the Healthy Food Pyramid in Pregnant and Lactating Women
Authors: Gila-Díaz Andrea , Witte Castro Ariadna , Herranz Carrillo Gloria , Singh Pratibha , Yakah William , Arribas Silvia M. , Ramiro-Cortijo David
Published: July 2021

There are numerous dietary recommendations during pregnancy. However, there are limited recommendations during the lactation period, a nutritionally vulnerable period for women. The Mediterranean Diet and adherence to the Healthy Food Pyramid (HFP) is considered as the standard for healthy eating. In this study, we investigated the differences in adherence to the HFP in pregnant, lactating, and non-pregnant/non-lactating (NPNL) women concerning sociodemographic factors. A sociodemographic and nutritional and lifestyle questionnaire (AP-Q) were used to assess adherence to the HFP, including lifestyle. The AP-Q score ranges from 0 to 10 meaning the higher the score, the greater the adherence to the HFP. Lactating women had the lowest AP-Q score (6.13 [5.31; 6.82]) compared to the pregnant (6.39 [5.56; 7.05]) and NPNL women (6.27 [5.43; 6.88]), while pregnant women had the highest scores. Maternal age was positively correlated with AP-Q score in pregnant (rho = 0.22; p-Value < 0.001) and lactating women (rho = 0.18; p-Value < 0.001), but not in NPNL women. Educational level and monthly income had a positive influence on the degree of adherence to the HFP. In conclusion, breastfeeding mothers of young age and low socioeconomic and educational level would be the target population to carry out nutritional interventions that improve their adherence to the HFP. The knowledge gained from this study can help to design recommendation guidelines and nutritional educational interventions for a given population.
Effect of a New Formulation of Nutraceuticals as an Add-On to Metformin Monotherapy for Patients with Type 2 Diabetes and Suboptimal Glycemic Control: A Randomized Controlled Trial
Authors: Sartore Giovanni , Ragazzi Eugenio , Antonello Giulia , Cosma Chiara , Lapolla Annunziata
Published: July 2021

The aim of the study was to evaluate the overall biohumoral and metabolic effects of a 12-week add-on therapy consisting of a new nutraceutical formulation (BHC) based on berberine, hesperidin, and chromium picolinate in type 2 diabetes mellitus (T2D) patients with suboptimal glycemic compensation receiving metformin. After 12 weeks, participants in the group receiving metformin plus BHC, compared to the group receiving metformin only, saw a significant improvement in their glucose profile, in terms of both glycated hemoglobin (HbA1c) and fasting blood glucose (FBG). Their FBG dropped from 145 ± 20 mg/dL to 128 ± 23 mg/dL (p < 0.01), a decrease of 11.7% compared with the baseline. This decrease differed significantly from the situation in the control arm (p < 0.05). HbA1c decreased by 7.5% from the baseline, from 53.5 ± 4.3 mmol/mol to 49.5 ± 5.1 mmol/mol (p < 0.01), in the group given BHC, while no difference was seen in the control group. Advanced glycation end products (AGEs) and malondialdehyde (MDA) were found to be significantly reduced (p < 0.01) only in the BHC group, from 9.34 ± 7.61 μg/mL to 6.75 ± 6.13 μg/mL, and from 1.7 ± 0.15 μmol/L to 1.4 ± 0.25 μmol/L, respectively. In patients with T2D taking metformin with suboptimal glycemic compensation, adding BHC for 3 months significantly improved glucose control in terms of FBG and HbA1c, and had a positive effect on the lipid peroxidation profile, as indicated by a decrease in AGEs and MDA.
Reversal of Insulin Resistance in Overweight and Obese Subjects by trans-Resveratrol and Hesperetin Combination—Link to Dysglycemia, Blood Pressure, Dyslipidemia, and Low-Grade Inflammation
Authors: Rabbani Naila , Xue Mingzhan , Weickert Martin O. , Thornalley Paul J.
Published: July 2021

The dietary supplement, trans-resveratrol and hesperetin combination (tRES-HESP), induces expression of glyoxalase 1, countering the accumulation of reactive dicarbonyl glycating agent, methylglyoxal (MG), in overweight and obese subjects. tRES-HESP produced reversal of insulin resistance, improving dysglycemia and low-grade inflammation in a randomized, double-blind, placebo-controlled crossover study. Herein, we report further analysis of study variables. MG metabolism-related variables correlated with BMI, dysglycemia, vascular inflammation, blood pressure, and dyslipidemia. With tRES-HESP treatment, plasma MG correlated negatively with endothelial independent arterial dilatation (r = −0.48, p < 0.05) and negatively with peripheral blood mononuclear cell (PBMC) quinone reductase activity (r = −0.68, p < 0.05)—a marker of the activation status of transcription factor Nrf2. For change from baseline of PBMC gene expression with tRES-HESP treatment, Glo1 expression correlated negatively with change in the oral glucose tolerance test area-under-the-curve plasma glucose (ΔAUGg) (r = −0.56, p < 0.05) and thioredoxin interacting protein (TXNIP) correlated positively with ΔAUGg (r = 0.59, p < 0.05). Tumor necrosis factor-α (TNFα) correlated positively with change in fasting plasma glucose (r = 0.70, p < 0.001) and negatively with change in insulin sensitivity (r = −0.68, p < 0.01). These correlations were not present with placebo. tRES-HESP decreased low-grade inflammation, characterized by decreased expression of CCL2, COX-2, IL-8, and RAGE. Changes in CCL2, IL-8, and RAGE were intercorrelated and all correlated positively with changes in MLXIP, MAFF, MAFG, NCF1, and FTH1, and negatively with changes in HMOX1 and TKT; changes in IL-8 also correlated positively with change in COX-2. Total urinary excretion of tRES and HESP metabolites were strongly correlated. These findings suggest tRES-HESP counters MG accumulation and protein glycation, decreasing activation of the unfolded protein response and expression of TXNIP and TNFα, producing reversal of insulin resistance. tRES-HESP is suitable for further evaluation for treatment of insulin resistance and related disorders.
Kinetic and Microhydrodynamic Modeling of Fenofibrate Nanosuspension Production in a Wet Stirred Media Mill
Authors: Guner Gulenay , Yilmaz Dogacan , Bilgili Ecevit
Published: July 2021

This study examined the impact of stirrer speed and bead material loading on fenofibrate particle breakage during wet stirred media milling (WSMM) via three kinetic models and a microhydrodynamic model. Evolution of median particle size was tracked via laser diffraction during WSMM operating at 3000–4000 rpm with 35–50% (v/v) concentration of polystyrene or zirconia beads. Additional experiments were performed at the center points of the above conditions, as well as outside the range of these conditions, in order to test the predictive capability of the models. First-order, nth-order, and warped-time kinetic models were fitted to the data. Main effects plots helped to visualize the influence of the milling variables on the breakage kinetics and microhydrodynamic parameters. A subset selection algorithm was used along with a multiple linear regression model (MLRM) to delineate how the breakage rate constant k was affected by the microhydrodynamic parameters. As a comparison, a purely empirical correlation for k was also developed in terms of the process/bead parameters. The nth-order model was found to be the best model to describe the temporal evolution; nearly second-order kinetics (n ≅ 2) was observed. When the process was operated at a higher stirrer speed and/or higher loading with zirconia beads as opposed to polystyrene beads, the breakage occurred faster. A statistically significant (p-value ≤ 0.01) MLRM of three microhydrodynamic parameters explained the variation in the breakage rate constant best (R2 ≥ 0.99). Not only do the models and the nth-order kinetic–microhydrodynamic correlation enable deeper process understanding toward developing a WSMM process with reduced cycle time, but they also provide good predictive capability, while outperforming the purely empirical correlation.
Antimelanogenesis Effects of Leaf Extract and Phytochemicals from Ceylon Olive (Elaeocarpus serratus) in Zebrafish Model
Authors: Huang Chi-Ya , Liu I-Hsuan , Huang Xiang-Zhe , Chen Hui-Jen , Chang Shang-Tzen , Chang Mei-Ling , Ho Yu-Tung , Chang Hui-Ting
Published: July 2021

The melanogenesis inhibition effect in zebrafish (Danio rerio) and antityrosinase activity of the ethanolic extract and its phytochemicals from Ceylon olive (Elaeocarpus serratus Linn.) leaves were investigated in this study. Among the leaf extract and four soluble fractions, the ethyl acetate soluble fraction exhibits the best antityrosinase and antimelanogenesis activities. One phenolic acid, gallic acid, and two flavonoids, myricetin and mearnsetin, are isolated from the active subfractions through the bioassay-guided isolation; their structures are elucidated based on the 1D and 2D NMR, FTIR, UV, and MS spectroscopic analyses. These compounds have significant antityrosinase activity whether using l-tyrosine or l-DOPA as the substrate; mearnsetin shows the optimal activity. In the enzyme kinetic investigation, both gallic acid and mearnsetin are the competitive-type inhibitors against mushroom tyrosinase, and myricetin acts as a mixed-type tyrosinase inhibitor. Leaf extract and an ethyl acetate soluble fraction show effective performance in the inhibition of melanin formation in zebrafish embryos. Mearnsetin also possesses a promising antimelanogenesis effect, which is superior to the positive control, arbutin. Results reveal that the Ceylon olive leaf extract and its phytochemicals, especially mearnsetin, have the potential to be used as antimelanogenesis and skin-whitening ingredients.
Behind the Adaptive and Resistance Mechanisms of Cancer Stem Cells to TRAIL
Authors: Quiroz-Reyes Adriana G. , Delgado-Gonzalez Paulina , Islas Jose Francisco , Gallegos Juan Luis Delgado , Martínez Garza Javier Humberto , Garza-Treviño Elsa N.
Published: July 2021

Tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL), also known as Apo-2 ligand (Apo2L), is a member of the TNF cytokine superfamily. TRAIL has been widely studied as a novel strategy for tumor elimination, as cancer cells overexpress TRAIL death receptors, inducing apoptosis and inhibiting blood vessel formation. However, cancer stem cells (CSCs), which are the main culprits responsible for therapy resistance and cancer remission, can easily develop evasion mechanisms for TRAIL apoptosis. By further modifying their properties, they take advantage of this molecule to improve survival and angiogenesis. The molecular mechanisms that CSCs use for TRAIL resistance and angiogenesis development are not well elucidated. Recent research has shown that proteins and transcription factors from the cell cycle, survival, and invasion pathways are involved. This review summarizes the main mechanism of cell adaption by TRAIL to promote response angiogenic or pro-angiogenic intermediates that facilitate TRAIL resistance regulation and cancer progression by CSCs and novel strategies to induce apoptosis.
Epistemology and Ethics in Zhuangzi
Authors: Kreider S. Evan
Published: July 2021

On a prima facia reading, Zhuangzi seems to endorse some form of skepticism or relativism. This seems at odds with Zhuangzi as one of the two main sources of classical Daoism, considering the ideals of virtue and self-development promoted by that philosophy. However, Zhuangzi’s metaphorical and allegorical style lends itself to a number of interpretations of his epistemology, as well as the kind of self-knowledge and ethical development it might allow. A survey of the relevant literature shows that the epistemological debate is not easily solvable, but by narrowing the range of interpretations, a coherent picture of his ethics begins to emerge, one in which some form of knowledge, especially self-knowledge, is still possible, as is an ethics of self-actualization.
Hydrogels Based on Poly([2-(acryloxy)ethyl] Trimethylammonium Chloride) and Nanocellulose Applied to Remove Methyl Orange Dye from Water
Authors: Roa Karina , Tapiero Yesid , Thotiyl Musthafa Ottakam , Sánchez Julio
Published: July 2021

Bio-based hydrogels that adsorb contaminant dyes, such as methyl orange (MO), were synthesized and characterized in this study. The synthesis of poly([2-(acryloyloxy)ethyl] trimethylammonium chloride) and poly(ClAETA) hydrogels containing cellulose nanofibrillated (CNF) was carried out by free-radical polymerization based on a factorial experimental design. The hydrogels were characterized by Fourier transformed infrared spectroscopy, scanning electron microscopy, and thermogravimetry. Adsorption studies of MO were performed, varying time, pH, CNF concentration, initial dye concentration and reuse cycles, determining that when the hydrogels were reinforced with CNF, the dye removal values reached approximately 96%, and that the material was stable when the maximum swelling capacity was attained. The maximum amount of MO retained per gram of hydrogel (q = mg MO g−1) was 1379.0 mg g−1 for the hydrogel containing 1% (w w−1) CNF. Furthermore, it was found that the absorption capacity of MO dye can be improved when the medium pH tends to be neutral (pH = 7.64). The obtained hydrogels can be applicable for the treatment of water containing anionic dyes.
A Review of Plasma Synthesis Methods for Polymer Films and Nanoparticles under Atmospheric Pressure Conditions
Authors: Jang Hyo Jun , Jung Eun Young , Parsons Travis , Tae Heung-Sik , Park Choon-Sang
Published: July 2021

In this paper, we present an overview of recent approaches in the gas/aerosol-through-plasma (GATP) and liquid plasma methods for synthesizing polymer films and nanoparticles (NPs) using an atmospheric-pressure plasma (APP) technique. We hope to aid students and researchers starting out in the polymerization field by compiling the most commonly utilized simple plasma synthesis methods, so that they can readily select a method that best suits their needs. Although APP methods are widely employed for polymer synthesis, and there are many related papers for specific applications, reviews that provide comprehensive coverage of the variations of APP methods for polymer synthesis are rarely reported. We introduce and compile over 50 recent papers on various APP polymerization methods that allow us to discuss the existing challenges and future direction of GATP and solution plasma methods under ambient air conditions for large-area and mass nanoparticle production.
How Spatial Resolution Affects Forest Phenology and Tree-Species Classification Based on Satellite and Up-Scaled Time-Series Images
Authors: Xu Kaijian , Zhang Zhaoying , Yu Wanwan , Zhao Ping , Yue Jibo , Deng Yaping , Geng Jun
Published: July 2021

The distribution of forest tree species provides crucial data for regional forest management and ecological research. Although medium-high spatial resolution remote sensing images are widely used for dynamic monitoring of forest vegetation phenology and species identification, the use of multiresolution images for similar applications remains highly uncertain. Moreover, it is necessary to explore to what extent spectral variation is responsible for the discrepancies in the estimation of forest phenology and classification of various tree species when using up-scaled images. To clarify this situation, we studied the forest area in Harqin Banner in northeast China by using year-round multiple-resolution time-series images (at four spatial resolutions: 4, 10, 16, and 30 m) and eight phenological metrics of four deciduous forest tree species in 2018, to explore potential impacts of relevant results caused by various resolutions. We also investigated the effect of using up-scaled time-series images by comparing the corresponding results that use pixel-aggregation algorithms with the four spatial resolutions. The results indicate that both phenology and classification accuracy of the dominant forest tree species are markedly affected by the spatial resolution of time-series remote sensing data (p < 0.05): the spring phenology of four deciduous forest tree species first rises and then falls as the image resolution varies from 4 to 30 m; similarly, the accuracy of tree species classification increases as the image resolution varies from 4 to 10 m, and then decreases as the image resolution gradually falls to 30 m (p < 0.05). Therefore, there remains a profound discrepancy between the results obtained by up-scaled and actual remote sensing data at the given spatial resolutions (p < 0.05). The results also suggest that combining phenological metrics and time-series NDVI data can be applied to identify the regional dominant tree species across different spatial resolutions, which would help advance the use of multiscale time-series satellite data for forest resource management.
PlanetScope Imageries and LiDAR Point Clouds Processing for Automation Land Cover Mapping and Vegetation Assessment of a Reclaimed Sulfur Mine
Authors: Szostak Marta , Likus-Cieślik Justyna , Pietrzykowski Marcin
Published: July 2021

The present research investigated the possibility of using PlanetScope imageries and LiDAR point clouds for land cover assessment, especially vegetation mapping, in degraded and reclaimed areas. Studies were carried out on the former sulfur mine of Jeziórko located in Southeast Poland. In total, more than ca. 2000 ha of this mine area were reclaimed after borehole exploitation and afforestation. We investigated a total area of 216.72 ha. Integration of PlanetScope imageries and LiDAR point clouds processing offers the ability to derive information about the LULC classes and vegetation growth in the analyzed area and indicate the forest succession progress as an effect of the reclamation treatments. In the Jeziórko area, we identified coniferous forest (90.84 ha, 41.91% of the research area), broad-leaved forest (44.02 ha, 20.31%), and transitional woodland shrub areas with herbaceous communities (77.96 ha, 35.97%). The analyses focused on the detection and monitoring of the forest succession processes and obtaining the tree canopy profiles and characteristics of vegetation, i.e., the height and cover density.
Estimation of Heavy Metals in Agricultural Soils Using Vis-NIR Spectroscopy with Fractional-Order Derivative and Generalized Regression Neural Network
Authors: Xu Xitong , Chen Shengbo , Ren Liguo , Han Cheng , Lv Donglin , Zhang Yufeng , Ai Fukai
Published: July 2021

With the development of industrialization and urbanization, heavy metal contamination in agricultural soils tends to accumulate rapidly and harm human health. Visible and near-infrared (Vis-NIR) spectroscopy provides the feasibility of fast monitoring of the variation of heavy metals. This study explored the potential of fractional-order derivative (FOD), the optimal band combination algorithm and different mathematical models in estimating soil heavy metals with Vis-NIR spectroscopy. A total of 80 soil samples were collected from an agriculture area in Suzi river basin, Liaoning Province, China. The spectra for mercury (Hg), chromium (Cr), and copper (Cu) of the samples were obtained in the laboratory. For spectral preprocessing, FODs were allowed to vary from 0 to 2 with an increment of 0.2 at each step, and the optimal band combination algorithm was applied to the spectra after FOD. Then, four mathematical models, namely, partial least squares regression (PLSR), adaptive neural fuzzy inference system (ANFIS), random forest (RF) and generalized regression neural network (GRNN), were used to estimate the concentration of Hg, Cr and Cu. Results showed that high-order FOD had an excellent effect in highlighting hidden information and separating minor absorbing peaks, and the optimal band combination algorithm could remove the influence of spectral noise caused by high-order FOD. The incorporation of the optimal band combination algorithm and FOD is able to further mine spectral information. Furthermore, GRNN made an obvious improvement to the estimation accuracy of all studied heavy metals compared to ANFIS, PLSR, and RF. In summary, our results provided more feasibility for the rapid estimation of Hg, Cr, Cu and other heavy metal pollution areas in agricultural soils.
Investigating Various Products of IMERG for Precipitation Retrieval over Surfaces with and without Snow and Ice Cover
Authors: Arabzadeh Alireza , Behrangi Ali
Published: July 2021

Precipitation rate from various products of the integrated multisatellite retrievals for GPM (IMERG) and passive microwave (PMW) sensors are assessed with respect to near-surface wet-bulb temperature (Tw), precipitation intensity, and surface type (i.e., with and without snow and ice on the surface) over the contiguous United States (CONUS) and using ground radar product as reference precipitation. IMERG products include precipitation estimates from infrared (IR), combined PMW, and combination of PMW and IR. It was found that precipitation estimates from PMW products generally have higher skills than IR over snow- and ice-free surfaces. Over snow- and ice-covered surfaces: (1) most PMW products show higher correlation coefficients than IR, (2) at cold temperatures (e.g., Tw < −10 °C), PMW products tend to underestimate and IR product shows large overestimations, and (3) PMW sensors show higher overall skill in detecting precipitation occurrence, but not necessarily at very cold Tw. The results suggest that the current approach of IMERG (i.e., replacing PMW with IR precipitation estimates over snow- and ice-surfaces) may need to be revised.
An Adaptive Piecewise Harmonic Analysis Method for Reconstructing Multi-Year Sea Surface Chlorophyll-A Time Series
Authors: Wang Yueqi , Gao Zhiqiang , Ning Jicai
Published: July 2021

High-quality remotely sensed satellite data series are important for many ecological and environmental applications. Unfortunately, irregular spatiotemporal samples, frequent image gaps and inevitable observational biases can greatly hinder their application. As one of the most effective gap filling and noise reduction approaches, the harmonic analysis of time series (HANTS) method has been widely used to reconstruct geographical variables; however, when applied on multi-year time series over large spatial areas, the optimal harmonic formulas are generally varied in different locations or change across different years. The question of how to choose the optimal harmonic formula is still unanswered due to the deficiency of appropriate criteria. In this study, an adaptive piecewise harmonic analysis method (AP-HA) is proposed to reconstruct multi-year seasonal data series. The method introduces a cross-validation scheme to adaptively determine the optimal harmonic model and employs an iterative piecewise scheme to better track the local traits. Whenapplied to the satellite-derived sea surface chlorophyll-a time series over the Bohai and Yellow Seas of China, the AP-HA obtains reliable reconstruction results and outperforms the conventional HANTS methods, achieving improved accuracy. Due to its generic approach to filling missing observations and tracking detailed traits, the AP-HA method has a wide range of applications for other seasonal geographical variables.
New Fiber Bragg Grating Three-Dimensional Accelerometer Based on Composite Flexure Hinges
Authors: Wang Hui , Liang Lei , Zhou Xiongbing , Tu Bin
Published: July 2021

Multi-dimensional acceleration sensors are used in important applications in the aerospace, weapon equipment, and nuclear fields and have strict requirements in terms of performance, volume, and mass. Fiber Bragg grating acceleration sensors use optical wavelength signals as a medium for information transmission to effectively eliminate the influence of electromagnetic interference between multi-dimensional sensors. In this study, we designed a composite flexure hinge three-dimensional acceleration sensor. To this end, we investigated the coupling mechanism between a new integrated elastomer structure and fiber grating to determine the influence of structural parameters on the static and dynamic characteristics, volume, and mass of the sensor. By optimizing the strain distribution, amplitude, and frequency and coupling characteristics between dynamic dimensions, a design theory and a method for integrating the three-dimensional acceleration sensor were developed. The size of the optimized accelerometer is only 25 mm × 25 mm × 30 mm. Performance testing revealed that, along the three spatial dimensions, the sensor had sensitivities of 51.9, 39.5, and 20.3 pm/g, respectively, resonance frequencies of 800, 1125, and 1750 Hz, respectively, and a measurable frequency range of 0–250 Hz.
Mapping Urban Air Quality from Mobile Sensors Using Spatio-Temporal Geostatistics
Authors: Idir Yacine Mohamed , Orfila Olivier , Judalet Vincent , Sagot Benoit , Chatellier Patrice
Published: July 2021

With the advancement of technology and the arrival of miniaturized environmental sensors that offer greater performance, the idea of building mobile network sensing for air quality has quickly emerged to increase our knowledge of air pollution in urban environments. However, with these new techniques, the difficulty of building mathematical models capable of aggregating all these data sources in order to provide precise mapping of air quality arises. In this context, we explore the spatio-temporal geostatistics methods as a solution for such a problem and evaluate three different methods: Simple Kriging (SK) in residuals, Ordinary Kriging (OK), and Kriging with External Drift (KED). On average, geostatistical models showed 26.57% improvement in the Root Mean Squared Error (RMSE) compared to the standard Inverse Distance Weighting (IDW) technique in interpolating scenarios (27.94% for KED, 26.05% for OK, and 25.71% for SK). The results showed less significant scores in extrapolating scenarios (a 12.22% decrease in the RMSE for geostatisical models compared to IDW). We conclude that univariable geostatistics is suitable for interpolating this type of data but is less appropriate for an extrapolation of non-sampled places since it does not create any information.
IMUMETER—A Convolution Neural Network-Based Sensor for Measurement of Aircraft Ground Performance
Authors: Pytka Jarosław Alexander , Budzyński Piotr , Tomiło Paweł , Michałowska Joanna , Gnapowski Ernest , Błażejczak Dariusz , Łukaszewicz Andrzej
Published: July 2021

The paper presents the development of the IMUMETER sensor, designed to study the dynamics of aircraft movement, in particular, to measure the ground performance of the aircraft. A motivation of this study was to develop a sensor capable of airplane motion measurement, especially for airfield performance, takeoff and landing. The IMUMETER sensor was designed on the basis of the method of artificial neural networks. The use of a neural network is justified by the fact that the automation of the measurement of the airplane’s ground distance during landing based on acceleration data is possible thanks to the recognition of the touchdown and stopping points, using artificial intelligence. The hardware is based on a single-board computer that works with the inertial navigation platform and a satellite navigation sensor. In the development of the IMUMETER device, original software solutions were developed and tested. The paper describes the development of the Convolution Neural Network, including the learning process based on the measurement results during flight tests of the PZL 104 Wilga 35A aircraft. The ground distance of the test airplane during landing on a grass runway was calculated using the developed neural network model. Additionally included are exemplary measurements of the landing distance of the test airplane during landing on a grass runway. The results obtained in this study can be useful in the development of artificial intelligence-based sensors, especially those for the measurement and analysis of aircraft flight dynamics.
Wearable Devices for Environmental Monitoring in the Built Environment: A Systematic Review
Authors: Salamone Francesco , Masullo Massimiliano , Sibilio Sergio
Published: July 2021

The so-called Internet of Things (IoT), which is rapidly increasing the number of network-connected and interconnected objects, could have a far-reaching impact in identifying the link between human health, well-being, and environmental concerns. In line with the IoT concept, many commercial wearables have been introduced in recent years, which differ from the usual devices in that they use the term “smart” alongside the terms “watches”, “glasses”, and “jewellery”. Commercially available wearables aim to enhance smartphone functionality by enabling payment for commercial items or monitoring physical activity. However, what is the trend of scientific production about the concept of wearables regarding environmental monitoring issues? What are the main areas of interest covered by scientific production? What are the main findings and limitations of the developed solution in this field? The methodology used to answer the above questions is based on a systematic review. The data were acquired following a reproducible methodology. The main result is that, among the thermal, visual, acoustic, and air quality environmental factors, the last one is the most considered when using wearables even though in combination with some others. Another relevant finding is that of the acquired studies; in only one, the authors shared their wearables as an open-source device, and it will probably be necessary to encourage researchers to consider open-source as a means to promote scalability and proliferation of new wearables customized to cover different domains.
A Grain-Scale Study of Mojave Mars Simulant (MMS-1)
Authors: Kasyap Sathwik S. , Senetakis Kostas
Published: July 2021

Space exploration has attracted significant interest by government agencies and the scientific community in recent years in an attempt to explore possible scenarios of settling of facilities on the Moon and Mars surface. One of the important components in space exploration is related with the understanding of the geophysical and geotechnical characteristics of the surfaces of planets and their natural satellites and because of the limitation of available extra-terrestrial samples, many times researchers develop simulants, which mimic the properties and characteristics of the original materials. In the present study, characterization at the grain-scale was performed on the Mojave Mars Simulant (MMS-1) with emphasis on the frictional behavior of small size samples which follow the particle-to-particle configuration. Additional characterization was performed by means of surface composition and morphology analysis and the crushing behavior of individual grains. The results from the study present for the first time the micromechanical tribological response of Mars simulant, and attempts were also made to compare the behavior of this simulant with previously published results on other types of Earth and extra-terrestrial materials. Despite some similarities between Mars and Moon simulants, the unique characteristics of the MMS-1 samples resulted in significant differences and particularly in severe damage of the grain surfaces, which was also linked to the dilation behavior at the grain-scale.
Fuel Prediction and Reduction in Public Transportation by Sensor Monitoring and Bayesian Networks
Authors: Delussu Federico , Imran Faisal , Mattia Christian , Meo Rosa
Published: July 2021

We exploit the use of a controller area network (CAN-bus) to monitor sensors on the buses of local public transportation in a big European city. The aim is to advise fleet managers and policymakers on how to reduce fuel consumption so that air pollution is controlled and public services are improved. We deploy heuristic algorithms and exhaustive ones to generate Bayesian networks among the monitored variables. The aim is to describe the relevant relationships between the variables, to discover and confirm the possible cause–effect relationships, to predict the fuel consumption dependent on the contextual conditions of traffic, and to enable an intervention analysis to be conducted on the variables so that our goals are achieved. We propose a validation technique using Bayesian networks based on Granger causality: it relies upon observations of the time series formed by successive values of the variables in time. We use the same method based on Granger causality to rank the Bayesian networks obtained as well. A comparison of the Bayesian networks discovered against the ground truth is proposed in a synthetic data set, specifically generated for this study: the results confirm the validity of the Bayesian networks that agree on most of the existing relationships.
Stereo Visual Odometry Pose Correction through Unsupervised Deep Learning
Authors: Zhang Sumin , Lu Shouyi , He Rui , Bao Zhipeng
Published: July 2021

Visual simultaneous localization and mapping (VSLAM) plays a vital role in the field of positioning and navigation. At the heart of VSLAM is visual odometry (VO), which uses continuous images to estimate the camera’s ego-motion. However, due to many assumptions of the classical VO system, robots can hardly operate in challenging environments. To solve this challenge, we combine the multiview geometry constraints of the classical stereo VO system with the robustness of deep learning to present an unsupervised pose correction network for the classical stereo VO system. The pose correction network regresses a pose correction that results in positioning error due to violation of modeling assumptions to make the classical stereo VO positioning more accurate. The pose correction network does not rely on the dataset with ground truth poses for training. The pose correction network also simultaneously generates a depth map and an explainability mask. Extensive experiments on the KITTI dataset show the pose correction network can significantly improve the positioning accuracy of the classical stereo VO system. Notably, the corrected classical stereo VO system’s average absolute trajectory error, average translational relative pose error, and average translational root-mean-square drift on a length of 100–800 m in the KITTI dataset is 13.77 cm, 0.038 m, and 1.08%, respectively. Therefore, the improved stereo VO system has almost reached the state of the art.
Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks
Authors: Mehedi Sk. Tanzir , Anwar Adnan , Rahman Ziaur , Ahmed Kawsar
Published: July 2021

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.
Real-Time Structural Monitoring of the Multi-Point Hoisting of a Long-Span Converter Station Steel Structure
Authors: Zhu Yunfeng , Gao Yi , Zeng Qinghe , Liao Jin , Liu Zhen , Zhou Cuiying
Published: July 2021

In the process of using a long-span converter station steel structure, engineering disasters can easily occur. Structural monitoring is an important method to reduce hoisting risk. In previous engineering cases, the structural monitoring of long-span converter station steel structure hoisting is rare. Thus, no relevant hoisting experience can be referenced. Traditional monitoring methods have a small scope of application, making it difficult to coordinate monitoring and construction control. In the monitoring process, many problems arise, such as complicated installation processes, large-scale data processing, and large-scale installation errors. With a real-time structural monitoring system, the mechanical changes in the long-span converter station steel structure during the hoisting process can be monitored in real-time in order to achieve real-time warning of engineering disasters, timely identification of engineering issues, and allow for rapid decision-making, thus avoiding the occurrence of engineering disasters. Based on this concept, automatic monitoring and manual measurement of the mechanical changes in the longest long-span converter station steel structure in the world is carried out, and the monitoring results were compared with the corresponding numerical simulation results in order to develop a real-time structural monitoring system for the whole long-span converter station steel structure’s multi-point lifting process. This approach collects the monitoring data and outputs the deflection, stress, strain, wind force, and temperature of the long-span converter station steel structure in real-time, enabling real-time monitoring to ensure the safety of the lifting process. This research offers a new method and basis for the structural monitoring of the multi-point hoisting of a long-span converter station steel structure.
A Framework for Long-Term Vibration-Based Monitoring of Bridges
Authors: Erduran Emrah , Ulla Frida Kristin , Næss Lone
Published: July 2021

A new framework for long-term monitoring of bridges is proposed in order to negate (i) the impact of measurement uncertainties on damage detection in vibration-based structural health monitoring and (ii) the low sensitivity of damage indicators to low levels of damage. The framework is developed using three vibration-based damage indicators that have an intuitive physical correlation with damage: modal curvature, modal strain energy and modal flexibility. The article first quantifies the efficacy of these damage indicators when based on two observations, one from the undamaged state and one from the monitored state, in detecting and locating damage for different damage levels that are simulated on an 84-m long railway bridge. A long-term monitoring framework based on a new parameter defined as the frequency of the damage indicator exceeding the threshold value within a population of observations is developed. Impact of several factors including the damage location, damage indicator used in the framework, and the noise level on the success of the developed framework was investigated through numerical analysis. The new parameter, when used together with modal strain energy, was shown to provide a very clear picture of damage initiation and development over time starting from very low damage levels. Furthermore, the location of the simulated damage can be identified successfully at all damage levels and even for very high noise levels using the proposed framework.