bluuebunny/crossref_metadata_2025 huggingface dataset:
The Expression Profiles of the Salvia miltiorrhiza 3-Hydroxy-3-methylglutaryl-coenzyme A Reductase 4 Gene and Its Influence on the Biosynthesis of Tanshinones
Authors: Majewska Małgorzata , Szymczyk Piotr , Gomulski Jan , Jeleń Agnieszka , Grąbkowska Renata , Balcerczak Ewa , Kuźma Łukasz
Published: July 2022

Salvia miltiorrhiza is a medicinal plant that synthesises biologically-active tanshinones with numerous therapeutic properties. An important rate-limiting enzyme in the biosynthesis of their precursors is 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGR). This study presents the organ-specific expression profile of the S. miltiorrhiza HMGR4 gene and its sensitivity to potential regulators, viz. gibberellic acid (GA3), indole-3-acetic acid (IAA) and salicylic acid (SA). In addition, it demonstrates the importance of the HMGR4 gene, the hormone used, the plant organ, and the culture environment for the biosynthesis of tanshinones. HMGR4 overexpression was found to significantly boost the accumulation of dihydrotanshinone I (DHTI), cryptotanshinone (CT), tanshinone I (TI) and tanshinone IIA (TIIA) in roots by 0.44 to 5.39 mg/g dry weight (DW), as well as TIIA in stems and leaves. S. miltiorrhiza roots cultivated in soil demonstrated higher concentrations of the examined metabolites than those grown in vitro. GA3 caused a considerable increase in the quantity of CT (by 794.2 µg/g DW) and TIIA (by 88.1 µg/g DW) in roots. In turn, IAA significantly inhibited the biosynthesis of the studied tanshinones in root material.
Solubility Optimization of Loxoprofen as a Nonsteroidal Anti-Inflammatory Drug: Statistical Modeling and Optimization
Authors: Alqarni Mohammed , Namazi Nader Ibrahim , Alshehri Sameer , Naguib Ibrahim A. , Alsubaiyel Amal M. , Venkatesan Kumar , Elmokadem Eman Mohamed , Pishnamazi Mahboubeh , Abourehab Mohammed A. S.
Published: July 2022

Industrial-based application of supercritical CO2 (SCCO2) has emerged as a promising technology in numerous scientific fields due to offering brilliant advantages, such as simplicity of application, eco-friendliness, and high performance. Loxoprofen sodium (chemical formula C15H18O3) is known as an efficient nonsteroidal anti-inflammatory drug (NSAID), which has been long propounded as an effective alleviator for various painful disorders like musculoskeletal conditions. Although experimental research plays an important role in obtaining drug solubility in SCCO2, the emergence of operational disadvantages such as high cost and long-time process duration has motivated the researchers to develop mathematical models based on artificial intelligence (AI) to predict this important parameter. Three distinct models have been used on the data in this work, all of which were based on decision trees: K-nearest neighbors (KNN), NU support vector machine (NU-SVR), and Gaussian process regression (GPR). The data set has two input characteristics, P (pressure) and T (temperature), and a single output, Y = solubility. After implementing and fine-tuning to the hyperparameters of these ensemble models, their performance has been evaluated using a variety of measures. The R-squared scores of all three models are greater than 0.9, however, the RMSE error rates are 1.879 × 10−4, 7.814 × 10−5, and 1.664 × 10−4 for the KNN, NU-SVR, and GPR models, respectively. MAE metrics of 1.116 × 10−4, 6.197 × 10−5, and 8.777 × 10−5errors were also discovered for the KNN, NU-SVR, and GPR models, respectively. A study was also carried out to determine the best quantity of solubility, which can be referred to as the (x1 = 40.0, x2 = 338.0, Y = 1.27 × 10−3) vector.
Unrevealing the Potential of Sansevieria trifasciata Prain Fraction for the Treatment of Androgenetic Alopecia by Inhibiting Androgen Receptors Based on LC-MS/MS Analysis, and In-Silico Studies
Authors: Kasmawati Henny , Mustarichie Resmi , Halimah Eli , Ruslin Ruslin , Arfan Arfan , Sida Nurramadhani A.
Published: July 2022

Androgenetic Alopecia (AGA) occurs due to over-response to androgens causing severe hair loss on the scalp, and requires the development of new and efficient drugs to treat this condition. This study explores and identifies secondary metabolites from Sansevieriatrifasciata Prain using the LC-MS/MS and in-silico method. The inhibitory activity of bioactive compounds from S. trifasciata Prain against androgen receptors (PDB ID: 4K7A) was evaluated molecularly using docking and dynamics studies by comparing their binding energies, interactions, and stability with minoxidil. The results of the LC-MS/MS analysis identified Methyl pyrophaeophorbide A (1), Oliveramine (2), (2S)-3′, 4′-Methylenedioxy-5, 7-dimethoxyflavane (3), 1-Acetyl-β-carboline (4), Digiprolactone (5), Trichosanic acid (6) and Methyl gallate (7) from the leaves subfraction of this plant. Three alkaloid compounds (compounds 1, 3, and 4), and one flavonoid (compound 2), had lower docking scores of −7.0, −5.8, −5.2, and −6.3 kcal/mol, respectively. The prediction of binding energy using the MM-PBSA approach ensured that the potency of the four compounds was better than minoxidil, with energies of −66.13, −59.36, −40.39, and −40.25 kJ/mol for compounds 1, 3, 2, and 4, respectively. The dynamics simulation shows the stability of compound 1 based on the trajectory analysis for the 100 ns simulation. This research succeeded in identifying the compound and assessing the anti-alopecia activity of Sansevieria trifasciata Prain. Seven compounds were identified as new compounds never reported in Sansevieria trifasciata Prain. Four compounds were predicted to have better anti-alopecia activity than minoxidil in inhibiting androgen receptors through an in silico approach.
Nutritional Status of Saudi Children with Celiac Disease Following the Ministry of Health’s Gluten-Free Diet Program
Authors: Allowaymi Shiekhah S. , Binobead Manal Abdulaziz , Alshammari Ghedeir M. , Alrasheed Ali , Mohammed Mohammed A. , Yahya Mohammed Abdo
Published: July 2022

This study aimed to evaluate the nutritional status of Saudi children with celiac disease (CD) who followed the Ministry of Health’s gluten-free diet (GFD) program. This study involved 66 children with CD (29 boys and 37 girls) from 5 hospitals belonging to the Ministry of Health. Socioeconomic characteristics were obtained using a structured questionnaire. Anthropometric indices were measured using a body composition analyzer. Dietary intake was assessed using three 24 h dietary records. The biochemical parameters were determined in the hospitals’ laboratories. According to the findings, the majority of respondents had ages ranging from 10 to 13 years, a father and mother with a university education, a high family income, and 5 to 7 family members. Carbohydrates and protein intake for both genders were significantly higher than the DRI’s recommended dietary intake. However, the majority of nutrients consumed were at levels significantly lower than the DRI. Both genders had normal anthropometric indices, with girls having at significantly higher indices than boys. The biochemical parameters of both genders were comparable and within the normal range, except for vitamin D, which was below the normal range. The most important factors influencing nutritional status were age for both genders, and family income and number of family members for boys. In conclusion, data obtained for nutrient intake, anthropometric indicators, body composition, and biochemical analysis indicated that CD children following the Ministry of Health GFD program have a generally good nutritional status.
Defining the Underlying-Event Activity in the Presence of Heavy-Flavour Processes in Proton-Proton Collisions at LHC Energies
Authors: Gyulai László , Sándor Szende , Vértesi Róbert
Published: July 2022

We present a systematic analysis of heavy-flavour production in the underlying event in connection to a leading hard process in pp collisions at s=13 TeV, using the PYTHIA 8 Monte Carlo event generator. We compare results from events selected by triggering on the leading hadron, as well as those triggered with reconstructed jets. We show that the kinematics of heavy-flavour fragmentation complicates the characterisation of the underlying event, and the usual method which uses the leading charged final-state hadron as a trigger may wash away the connection between the leading process and the heavy-flavour particle created in association with that. Events triggered with light or heavy-flavour jets, however, retain this connection and bring more direct information on the underlying heavy-flavour production process, but may also import unwanted sensitivity to gluon radiation. The methods outlined in the current work provide means to verify model calculations for light and heavy-flavour production in the jet and the underlying event in great details.
In Vitro Analysis of Extracts of Plant Used in Mexican Traditional Medicine, Which Are Useful to Combat Clostridioides difficile Infection
Authors: Martínez-Alva Jacqueline E. , Espinoza-Simón Emilio , Bayona-Pérez Yuli , Ruiz-Pérez Nancy C. , Ochoa Sara A. , Xicohtencatl-Cortes Juan , Torres Javier , Romo-Castillo Mariana
Published: July 2022

Recently, a worrying acceleration of the emergence of antibiotic-resistant bacteria has been reported. The increase in antibiotic-associated diseases, such as Clostridioides difficile infection (CDI), has promoted research on new treatments that could be more effective and less aggressive for CDI patients. This study evaluates eight plants with antimicrobial activity commonly used in Mexican traditional medicine to evaluate their potential against C. difficile. We provide essential information about these plants’ activities and action mechanisms against C. difficile and their effect on different bacterial infection activities: motility, adherence, sporulation, and germination. The selected plants are rosemary, estafiate, rue, epazote, mint, toloache, ajenjo, and thyme. We used clinical isolates to test their activity against strains responsible for current outbreaks to provide more information about the clinical impact of these extracts. We found that thyme, ajenjo, and mint were the most effective against the isolates. We identified that the extracts affected protein synthesis. In addition, the extracts affect the strains’ motility, and some, such as thyme extract, affect adherence, whereas rue extract affects sporulation. These results led to the identification of new compounds beneficial to CDI treatment.
Novel Neuroprotective Potential of Bunchosia armeniaca (Cav.) DC against Lipopolysaccharide Induced Alzheimer’s Disease in Mice
Authors: Abbas Haidy A. , Salama Ahmed M. , El-Toumy Sayed A. , A. Salama Abeer A. , Tadros Soad H. , El Gedaily Rania A.
Published: July 2022

Bunchosia armeniaca (Cav.) DC (Malpighiaceae) is one of the well-known traditionally used remedies worldwide. This study aims to explore the leaves’ metabolome via Quadrupole-Time-of-Flight-Liquid-Chromatography-Mass Spectrometry and to investigate the neuroprotective effect of leaves using lipopolysaccharide (LPS) induced Alzheimer’s disease model. Mice were administered LPS (0.25 mg/kg/day; intraperitoneal) as well as methanolic extract (BME), dichloromethane (BDMF), and butanol (BBF) fractions (each 200 mg/kg/day; oral) for one week. BME and BBF improved behavioral activity on the Y maze test, decreased brain content of inflammatory markers such as nuclear factor kappa B and interleukin 1 beta, and prevented the elevation of cytochrome P450 2E1, and glial fibrillary acidic protein compared to the LPS-administered group. Histopathological examination of several brain parts confirmed the neuroprotective effect of the tested extracts. In addition, BBF exhibited higher activity in all tested in vitro antioxidant and acetylcholinesterase inhibition assays. Metabolic profiling offered tentative identification of 88 metabolites, including mainly flavonoids, phenolic acids, and coumarins. Several detected metabolites, such as quercetin, apigenin, baicalin, vitexin, and resveratrol, had previously known neuroprotective effects. The current study highlighted the possible novel potential of B. armeniaca in preventing memory impairment, possibly through its antioxidant effect and inhibition of acetylcholinesterase, inflammatory and oxidative stress mediators.
Essential Oils and Neuropathic Pain
Authors: Ridouh Imane , Hackshaw Kevin V.
Published: July 2022

Neuropathic pain is one of the most prominent chronic pain syndromes, affecting almost 10% of the United States population. While there are a variety of established pharmacologic and non-pharmacologic treatment options, including tricyclic antidepressants (TCAs), serotonin-noradrenaline reuptake inhibitors, anticonvulsants, trigger point injections, and spinal cord stimulators, many patients continue to have chronic pain or suboptimal symptom control. This has led to an increased interest in alternative solutions for neuropathic pain such as nutritional supplements and essential oils. In this review, we explore the literature on the most commonly cited essential oils, including lavender, bergamot, rosemary, nutmeg, Billy goat weed, and eucalyptus. However, the literature is limited and largely comprised of preclinical animal models and a few experimental studies, some of which were poorly designed and did not clearly isolate the effects of the essential oil treatment. Additionally, no standardized method of dosing or route of administration has been established. Further randomized control studies isolating the active components of various essential oils are needed to provide conclusive evidence on the use of essential oils for neuropathic pain. In this review, we explore the basis behind some of the essential oils of interest to patients with neuropathic pain seen in rheumatology clinics.
Simulation of the Biofiltration of Sulfur Compounds: Effect of the Partition Coefficients
Authors: Silva Javier , Ortiz-Soto Rodrigo , León Marcelo , Morales Marjorie , Aroca Germán
Published: July 2022

The effect of the partition coefficient on the simulation of the operation of a biotrickling filter treating a mixture of sulfur compounds was analyzed to evaluate the pertinence of using Henry’s law in determining its removal capacity. The analysis consisted of the simulation of a biotrickling filter that bio-oxides hydrogen sulfide (H2S), dimethyl sulfide (DMS), methyl mercaptan (MM) and dimethyl disulfide (DMDS) using different types of models for determining the partition coefficient: Henry’s law for pure water, Henry’s law adjusted from experimental data, a mixed model (Extended UNIQUAC) and a semi-empirical model of two-parameters. The simulations were compared with experimental data. It was observed that Henry’s law for pure water could produce significant deviations from empirical data due to the liquid phase not being pure water. The two-parameter model better fits with similar results compared to the extended UNIQUAC model, with a lower calculation cost and necessary parameter amount. It shows that semi-empirical models can considerably improve simulation accuracy where complex phase interactions are present.
Prediction of Casing Collapse Strength Based on Bayesian Neural Network
Authors: Li Dongfeng , Fan Heng , Wang Rui , Yang Shangyu , Zhao Yating , Yan Xiangzhen
Published: July 2022

With the application of complex fracturing and other complex technologies, external extrusion has become the main cause of casing damage, which makes non-API high-extrusion-resistant casing continuously used in unconventional oil and gas resources exploitation. Due to the strong sensitivity of string ovality, uneven wall thickness, residual stress, and other factors to high anti-collapse casing, the API formula has a big error in predicting the anti-collapse strength of high anti-collapse casing. Therefore, Bayesian regularization artificial neural network (BRANN) is used to predict the external collapse strength of high anti-collapse casing. By collecting full-scale physical data, including initial defect data, geometric size, mechanical parameters, etc., after data preprocessing, the casing collapse strength data set is established for model training and blind measurement. Under the classical three-layer neural network, the Bayesian regularization algorithm is used for training. Through empirical formula and trial and error method, it is determined that when the number of hidden neurons is 12, the model is the best prediction model for high collapse resistance casing. The prediction results of the blind test data imported by the model show that the coincidence rate of BRANN casing collapse strength prediction can reach 96.67%. Through error analysis with API formula prediction results and KT formula prediction results improved by least square fitting, the BRANN-based casing collapse strength prediction has higher accuracy and stability. Compared with the traditional prediction method, this model can be used to predict casing strength under more complicated working conditions, and it has a certain guiding significance.
Personality Cults from a Communicative Standpoint
Authors: Postoutenko Kirill
Published: July 2022

Drawing upon a wide variety of personality cults in religion, culture and politics from Ancient Egypt to our times, the author attempts to present a summary view of this phenomenon from a communicative standpoint. Personality cult is seen as an attribution of universal and eternal socio-cultural significance to certain beings, messages and interaction scenarios in defiance of changes in and diversity of the surrounding reality. The communicative implementation of such a cult involves the suspension of some of the most basic mechanisms of social coordination. Thus, deification of political, cultural or religious leaders eliminates the subordination of individuals to their social roles, whereas canonization severs the ties of certain selected texts with the contexts of their production and reception. Last, but not least, random signaling between the subject of cultic adoration and his or her subordinates runs counter the standard cooperation rules in interaction (‘turn-taking’). Illustrating these points, the article points out at specific communicative pathologies accompanying personality cults and jeopardizing the stability of their socio-cultural environments.
The Impact of Inefficient Urban Growth on Spatial Inequality of Urban Green Resources (Case Study: Urmia City)
Authors: Ramezani Mehrian Majid , Manouchehri Miandoab Ayoub , Abedini Asghar , Aram Farshid
Published: July 2022

Urban green spaces are essential for improving the livability of cities. Urban parks as green and public open spaces and signs of nature in cities have special economic and social value. The existence of neighborhood parks and their proper distribution is a key element for improving the quality of life in the cities. Spatial equity and accessibility to parks are factors influencing the performance of parks. This study was conducted to determine the current situation of Urmia city in this regard by using the travel cost approach and network analysis, and also examining the impact of rapid urban growth on spatial inequalities. According to the results, 18% of the residential land in Urmia city is located outside of the park service area, and 68% of residential lands outside the park’s service area are areas that have developed as a result of urban growth in the last two decades. Based on the analysis, in 23 neighborhoods of Urmia city, the entire neighborhood is located in the service area of parks and has pedestrian access to this service. Other neighborhoods—to address the issue of spatial justice related to urban parks—fall into three categories: high, medium, and low priority.
Rare Earth Market, Electric Vehicles and Future Mobility Index: A Time-Frequency Analysis with Portfolio Implications
Authors: Haq Inzamam Ul , Ferreira Paulo , Maneengam Apichit , Wisetsri Worakamol
Published: July 2022

This study investigates the co-movements between the Solactive Electric Vehicle and Future Mobility Index (EVFMI) and multiple rare earth elements (REEs). We applied a TVP-VAR model and bivariate wavelet coherence approach to capture co-movements both in the time and frequency domain considering short-, medium- and long-term investment horizons. Using daily returns from 1 June 2012 to 4 June 2021, the results of the TVP-VAR model show that individual REEs and the EVFMI have strong return connectedness and are heterogenous over time. The bivariate wavelet coherence approach reveals that Dysprosium, Neodymium, Praseodymium and Terbium returns have positive co-movement (in-phase) with the EVFMI in the medium-term and long-term. In contrast, Cerium, Europium, Lanthanum and Yttrium returns have negative co-movements (out-phase) with the EVFMI in the medium-term and long-term. We find strong positive co-movements between the MVIS Global Rare Earth/Strategic Metals Index (MVREMX) and EVFMI at multiple wavelet scales. Following the lead/lag relationship, Cerium, Europium and Lanthanum, Yttrium returns are leading the EVFMI, and Neodymium, Dysprosium, Praseodymium, Terbium and MVREMX returns are lagging to the EVFMI. This study, therefore, suggests heterogenous hedging and diversification properties of REEs over time and investment horizons. Specifically, Cerium, Europium, Lanthanum and Yttrium act as strong hedges in long-term investment horizons and Neodymium, Dysprosium, Praseodymium and Terbium are weak hedges or diversifiers in short-term investment horizons. These results may be of particular interest to investors and relevant to policymakers considering multiple investment horizons.
A Year-Long Total Lightning Forecast over Italy with a Dynamic Lightning Scheme and WRF
Authors: Federico Stefano , Torcasio Rosa Claudia , Lagasio Martina , Lynn Barry H. , Puca Silvia , Dietrich Stefano
Published: July 2022

Lightning is an important threat to life and properties and its forecast is important for several applications. In this paper, we show the performance of the “dynamic lightning scheme” for next-day total strokes forecast. The predictions were compared against strokes recorded by a ground observational network for a forecast period spanning one year. Specifically, a total of 162 case studies were selected between 1 March 2020 and 28 February 2021, characterized by at least 3000 observed strokes over Italy. The events span a broad range of lightning intensity from about 3000 to 600,000 strokes in one day: 69 cases occurred in summer, 46 in fall, 18 in winter, and 29 in spring. The meteorological driver was the Weather Research and Forecasting (WRF) model (version 4.1) and we focused on the next-day forecast. Strokes were simulated by adding three extra variables to WRF, namely, the potential energies for positive and negative cloud to ground flashes and intracloud strokes. Each potential energy is advected by WRF, it is built by the electrification processes occurring into the cloud, and it is dissipated by lightning. Observed strokes were remapped onto the WRF model grid with a 3 km horizontal resolution for comparison with the strokes forecast. Results are discussed for the whole year and for different seasons. Moreover, statistics are presented for the land and the sea. In general, the results of this study show that lightning forecast with the dynamic lightning scheme and WRF model was successful for Italy; nevertheless, a careful inspection of forecast performance is necessary for tuning the scheme. This tuning is dependent on the season. A numerical experiment changing the microphysics scheme used in WRF shows the sensitivity of the results according to the choice of the microphysics scheme.
Rice Crop Monitoring Using Sentinel-1 SAR Data: A Case Study in Saku, Japan
Authors: Kobayashi Shoko , Ide Hiyuto
Published: July 2022

Global warming affects rice crop production, causing deterioration of rice grain quality. This study used C-band microwave images taken by the Sentinel-1 satellites to monitor rice crop growth with the aim to understand microwave backscatter behavior, focusing on decreases in panicle water contents with ripening, which affect C-band backscatter. Time-series changes illustrated a similar tendency across all four analysis years, showing that VV/VH ratio at an incidence angle of 45–46° stopped decreasing to be stable over the reproductive and ripening periods due to reductions in the panicle water content that allowed for greater microwave penetration into the canopy, thereby increasing panicle-related backscatter. Furthermore, multivariate regression analysis combined with field observations showed that VV and VH with the shallow incidence angles were significantly negatively correlated with panicle water content, which well demonstrated backscatter increases with plant senescence. Furthermore, it was observed that backscatter behaviors were highly consistent with changes in crop phenology and surface condition. Accordingly, Sentinel-1 images with shallow incidence angles and high revisit observation capabilities offer a strong potential for estimating panicle water content. Therefore, it seems reasonable to conclude that C-band SAR data is capable of retrieving grain filling conditions to estimate proper harvesting time.
Context Information Refinement for Few-Shot Object Detection in Remote Sensing Images
Authors: Wang Yan , Xu Chaofei , Liu Cuiwei , Li Zhaokui
Published: July 2022

Recently, few-shot object detection based on fine-tuning has attracted much attention in the field of computer vision. However, due to the scarcity of samples in novel categories, obtaining positive anchors for novel categories is difficult, which implicitly introduces the foreground–background imbalance problem. It is difficult to identify foreground objects from complex backgrounds due to various object sizes and cluttered backgrounds. In this article, we propose a novel context information refinement few-shot detector (CIR-FSD) for remote sensing images. In particular, we design a context information refinement (CIR) module to extract discriminant context features. This module uses dilated convolutions and dense connections to capture rich context information from different receptive fields and then uses a binary map as the supervision label to refine the context information. In addition, we improve the region proposal network (RPN). Concretely, the RPN is fine-tuned on novel categories, and the constraint of non-maximum suppression (NMS) is relaxed, which can obtain more positive anchors for novel categories. Experiments on two remote sensing public datasets show the effectiveness of our detector.
Land Subsidence Monitoring Method in Regions of Variable Radar Reflection Characteristics by Integrating PS-InSAR and SBAS-InSAR Techniques
Authors: Zhang Peng , Guo Zihao , Guo Shuangfeng , Xia Jin
Published: July 2022

In the InSAR solution, the uneven distribution of permanent scatterer candidates (PSCs) or slowly decoherent filtering phase (SDFP) pixel density in a region of variable radar reflection feature can cause local low accuracy in single interferometry. PSCs with higher-order coherence in Permanent Scatter InSAR (PS-InSAR) are generally distributed in those point targets of urban built-up areas, and SDFP pixels in Small Baseline Subset InSAR (SBAS-InSAR) are generally distributed in those distributed targets of countryside vegetation areas. According to the respective reliability of PS-InSAR and SBAS-InSAR for different radar reflection features, a new land subsidence monitoring method is proposed, which combines PS-SBAS InSAR by data fusion of different interferometry in different radar reflection regions. Density-based spatial clustering of applications with noise (DBSCAN) clustering analysis is carried out on the density of PSCs with higher-order coherence in PS-InSAR processing to zone the region of variable radar reflection features for acquiring the boundary of data fusion. The vector monitoring data of PS-InSAR is retained in the dense region of PSCs with higher-order coherence, and the vector monitoring data of SBAS-InSAR is used in the sparse region of PSCs with higher-order coherence. The vertical displacements from PS-InSAR and SBAS-InSAR are integrated to obtain the optimal land subsidence. The verification case of 38 SAR images acquired by the Sentinel-1A in Suzhou city indicates that the proposed method can automatically choose a matched interferometry technique according to the variability of radar reflection features in the region and improve the accuracy of using a single interferometry method. The integrated method of the combined field is more representative of overall subsidence characteristics than the PS-InSAR-only or SBAS-InSAR-only results, and it is better suited for the assessment of the impact of land subsidence over the study area. The research results of this paper can provide a useful comprehensive reference for city planning and help decrease land subsidence in Suzhou.
Optimizing Local Alignment along the Seamline for Parallax-Tolerant Orthoimage Mosaicking
Authors: Yin Hongche , Li Yunmeng , Shi Junfeng , Jiang Jiaqin , Li Li , Yao Jian
Published: July 2022

Orthoimage mosaicking with obvious parallax caused by geometric misalignment is a challenging problem in the field of remote sensing. Because the obvious objects are not included in the digital terrain model (DTM), large parallax exists in these objects. A common strategy is to search an optimal seamline between orthoimages, avoiding the majority of obvious objects. However, stitching artifacts may remain because (1) the seamline may still cross several obvious objects and (2) the orthoimages may not be precisely aligned in geometry when the accuracy of the DTM is low. While applying general image warping methods to orthoimages can improve the local geometric consistency of adjacent images, these methods usually significantly modify the geometric properties of orthophoto maps. To the best of our knowledge, no approach has been proposed in the field of remote sensing to solve the problem of local geometric misalignments after orthoimage mosaicking with obvious parallax. In this paper, we creatively propose a method to optimize local alignment along the seamline after seamline detection. It consists of the following main processes. First, we locate regions with geometric misalignments along the seamline based on the similarity measure. Second, for any one region, we find one-dimensional (1D) feature matches along the seamline using a semi-global matching approach. The deformation vectors are calculated for these matches. Third, these deformation vectors are robustly and smoothly propagated into the buffer region centered on the seamline by minimizing the associated energy function. Finally, we directly warp the orthoimages to eliminate the local parallax under the guidance of dense deformation vectors. The experimental results on several groups of orthoimages show that our proposed approach is capable of eliminating the local parallax existing in the seamline while preserving most geometric properties of digital orthophoto maps, and that it outperforms state-of-the-art approaches in terms of both visual quality and quantitative metrics.
A Sensor Bias Correction Method for Reducing the Uncertainty in the Spatiotemporal Fusion of Remote Sensing Images
Authors: Zhang Hongwei , Huang Fang , Hong Xiuchao , Wang Ping
Published: July 2022

With the development of multisource satellite platforms and the deepening of remote sensing applications, the growing demand for high-spatial resolution and high-temporal resolution remote sensing images has aroused extensive interest in spatiotemporal fusion research. However, reducing the uncertainty of fusion results caused by sensor inconsistencies and input data preprocessing is one of the challenges in spatiotemporal fusion algorithms. Here, we propose a novel sensor bias correction method to correct the input data of the spatiotemporal fusion model through a machine learning technique learning the bias between different sensors. Taking the normalized difference vegetation index (NDVI) images with low-spatial resolution (MODIS) and high-spatial resolution (Landsat) as the basic data, we generated the neighborhood gray matrices from the MODIS image and established the image bias pairs of MODIS and Landsat. The light gradient boosting machine (LGBM) regression model was used for the nonlinear fitting of the bias pairs to correct MODIS NDVI images. For three different landscape areas with various spatial heterogeneities, the fusion of the bias-corrected MODIS NDVI and Landsat NDVI was conducted by using the spatiotemporal adaptive reflection fusion model (STARFM) and the flexible spatiotemporal data fusion method (FSDAF), respectively. The results show that the sensor bias correction method can enhance the spatially detailed information in the input data, significantly improve the accuracy and robustness of the spatiotemporal fusion technology, and extend the applicability of the spatiotemporal fusion models.
Deep-Learning-Based Multiple Model Tracking Method for Targets with Complex Maneuvering Motion
Authors: Tian Weiming , Fang Linlin , Li Weidong , Ni Na , Wang Rui , Hu Cheng , Liu Hanzhe , Luo Weigang
Published: July 2022

The effective detection of unmanned aerial vehicle (UAV) targets is of great significance to guarantee national military security and social stability. In recent years, with the development of communication and control technology, the movement of UAVs has become increasingly flexible and complex, presenting diverse trajectory forms and different motion models in different phases. The Gaussian mixture probability hypothesis density filter incorporating the linear Gaussian jump Markov system approach (LGJMS-GMPHD) provides an efficient method for tracking multiple maneuvering targets, as applied to the switching of motions between a set of models in a Markovian chain. However, in practice, the motion model parameters of targets are generally unknown and the model switching is uncertain. When the preset filtering model parameters are mismatched, the tracking performance is dramatically degraded. In this paper, within the framework of the LGJMS-GMPHD filter, a deep-learning-based multiple model tracking method is proposed. First, an adaptive turn rate estimation network is designed to solve the filtering model mismatch caused by unknown turn rate parameters in coordinate turn models. Second, a filter state modification network is designed to solve the large tracking errors in the maneuvering phase caused by uncertain motion model switching. Finally, based on simulations of multiple maneuvering targets in cluttered environments and experimental field data verification, it can be concluded that the proposed method has strong adaptability to multiple maneuvering forms and can effectively improve the tracking performance of targets with complex maneuvering motion.
Human Pulse Detection by a Soft Tactile Actuator
Authors: Huang Zixin , Li Xinpeng , Wang Jiarun , Zhang Yi , Mei Jingfu
Published: July 2022

Soft sensing technologies offer promising prospects in the fields of soft robots, wearable devices, and biomedical instruments. However, the structural design, fabrication process, and sensing algorithm design of the soft devices confront great difficulties. In this paper, a soft tactile actuator (STA) with both the actuation function and sensing function is presented. The tactile physiotherapy finger of the STA was fabricated by a fluid silica gel material. Before pulse detection, the tactile physiotherapy finger was actuated to the detection position by injecting compressed air into its chamber. The pulse detecting algorithm, which realized the pulse detection function of the STA, is presented. Finally, in actual pulse detection experiments, the pulse values of the volunteers detected by using the STA and by employing a professional pulse meter were close, which illustrates the effectiveness of the pulse detecting algorithm of the STA.
A Drifter-Based Self-Powered Piezoelectric Sensor for Ocean Wave Measurements
Authors: Kargar Seyyed Masoud , Hao Guangbo
Published: July 2022

Recently, piezoelectric materials have received remarkable attention in marine applications for energy harvesting from the ocean, which is a harsh environment with powerful and impactful waves and currents. However, to the best of the authors’ knowledge, although there are various designs of piezoelectric energy harvesters for marine applications, piezoelectric materials have not been employed for sensory and measurement applications in marine environment. In the present research, a drifter-based piezoelectric sensor is proposed to measure ocean waves’ height and period. To analyze the motion principle and the working performance of the proposed drifter-based piezoelectric sensor, a dynamic model was developed. The developed dynamic model investigated the system’s response to an input of ocean waves and provides design insights into the geometrical and material parameters. Next, finite element analysis (FEA) simulations using the commercial software COMSOL-Multiphysics were carried out with the help of a coupled physics analysis of Solid Mechanics and Electrostatics Modules to achieve the output voltages. An experimental prototype was fabricated and tested to validate the results of the dynamic model and the FEA simulation. A slider-crank mechanism was used to mimic ocean waves throughout the experiment, and the results showed a close match between the proposed dynamic modeling, FEA simulations, and experimental testing. In the end, a short discussion is devoted to interpreting the output results, comparing the results of the simulations with those of the experimental testing, sensor’s resolution, and the self-powering functionality of the proposed drifter-based piezoelectric sensor.
Eco-Environmental Effect Evaluation of Tamarix chinesis Forest on Coastal Saline-Alkali Land Based on RSEI Model
Authors: Wang Jin , Li Guangxue , Chen Feiyong
Published: July 2022

Taking representative Tamarix chinensis forest in the national-level special protection zone for ocean ecology of Changyi city in Shandong province of China as the objective, this research studied how to use remote sensing technology to evaluate natural eco-environment and analyze spatiotemporal variation. In the process of constructing the index system of ecological environment effect evaluation based on RSEI (Remote Sensing Ecological Index) model, AOD (Aerosol Optical Depth), Salinity, Greenness, Wetness, Heat and Dryness, which can represent the ecological environment of the reserve, were selected as the corresponding indexes. In order to accurately obtain the value of the RSEI of the study area and to retain the information of the original indexes to the greatest extent, the SPCA (spatial principal components analysis) method was applied in this research. Finally, the RSEI was applied to evaluate the ecological and environmental effects and to analyze the spatial characteristics and spatiotemporal evolution of the study area. The results not only provide scientific evidence and technical guidance for the protection, transformation and management of the Tamarix chinensis forest in the protection zone but also push the development of the universal model of the ecological environment quality with a remote sensing evaluation index system at a regional scale.
Pilot Study for Correlation of Heart Rate Variability and Dopamine Transporter Brain Imaging in Patients with Parkinsonian Syndrome
Authors: Warhadpande Devdutta S. , Huo Jiayan , Libling William A. , Stuehm Carol , Najafi Bijan , Sherman Scott , Lei Hong , Roveda Janet Meiling , Kuo Phillip H.
Published: July 2022

Background: Parkinsonian syndrome (PS) is a broad category of neurodegenerative movement disorders that includes Parkinson disease, multiple system atrophy (MSA), progressive supranuclear palsy, and corticobasal degeneration. Parkinson disease (PD) is the second most common neurodegenerative disorder with loss of dopaminergic neurons of the substantia nigra and, thus, dysfunction of the nigrostriatal pathway. In addition to the motor symptoms of bradykinesia, rigidity, tremors, and postural instability, nonmotor symptoms such as autonomic dysregulation (AutD) can also occur. Heart rate variability (HRV) has been used as a measure of AutD and has shown to be prognostic in diseases such as diabetes mellitus and cirrhosis, as well as PD. I-123 ioflupane, a gamma ray-emitting radiopharmaceutical used in single-photon emission computed tomography (SPECT), is used to measure the loss of dopaminergic neurons in PD. Through the combination of SPECT and HRV, we tested the hypothesis that asymmetrically worse left-sided neuronal loss would cause greater AutD. Methods: 51 patients were enrolled on the day of their standard of care I-123 ioflupane scan for the work-up of possible Parkinsonian syndrome. Demographic information, medical and medication history, and ECG data were collected. HRV metrics were extracted from the ECG data. I-123 ioflupane scans were interpreted by a board-certified nuclear radiologist and quantified by automated software to generate striatal binding ratios (SBRs). Statistical analyses were performed to find correlations between the HRV and SPECT parameters. Results: 32 patients were excluded from the final analysis because of normal scans, prior strokes, cardiac disorders and procedures, or cancer. Abnormal I-123 ioflupane scans were clustered using T-SNE, and one-way ANOVA was performed to compare HRV and SBR parameters. The analysis was repeated after the exclusion of patients taking angiotensin-converting enzyme inhibitors, given the known mechanism on autonomic function. Subsequent analysis showed a significant difference between the high-frequency domains of heart rate variability, asymmetry of the caudate SBR, and putamen-to-caudate SBR. Conclusion: Our results support the hypothesis that more imbalanced (specifically worse left-sided) neuronal loss results in greater AutD.
Innovative Image Processing Method to Improve Autofocusing Accuracy
Authors: Liu Chien-Sheng , Tu Ho-Da
Published: July 2022

For automated optical inspection, autofocusing microscopes play an important role in capturing clear images of the measured object. At present, the image processing part of optics-based autofocusing microscopes often has various factors, which makes it impossible to describe the image information of the semicircular (or elliptical) spot with a simple circle-finding method. Accordingly, this study has developed a novel algorithm that can quickly calculate the ideal center of the elliptical spot and effectively compensate the linearity of the focusing characteristic curve. A prototype model was used to characterize and verify the proposed algorithm. The experimental results show that by using the proposed algorithm, the autofocusing accuracy can be effectively improved to less than 1.5 μm.
Rendering Immersive Haptic Force Feedback via Neuromuscular Electrical Stimulation
Authors: Galofaro Elisa , D’Antonio Erika , Lotti Nicola , Masia Lorenzo
Published: July 2022

Haptic feedback is the sensory modality to enhance the so-called “immersion”, meant as the extent to which senses are engaged by the mediated environment during virtual reality applications. However, it can be challenging to meet this requirement using conventional robotic design approaches that rely on rigid mechanical systems with limited workspace and bandwidth. An alternative solution can be seen in the adoption of lightweight wearable systems equipped with Neuromuscular Electrical Stimulation (NMES): in fact, NMES offers a wide range of different forces and qualities of haptic feedback. In this study, we present an experimental setup able to enrich the virtual reality experience by employing NMES to create in the antagonists’ muscles the haptic sensation of being loaded. We developed a subject-specific biomechanical model that estimated elbow torque during object lifting to deliver suitable electrical muscle stimulations. We experimentally tested our system by exploring the differences between the implemented NMES-based haptic feedback (NMES condition), a physical lifted object (Physical condition), and a condition without haptic feedback (Visual condition) in terms of kinematic response, metabolic effort, and participants’ perception of fatigue. Our results showed that both in terms of metabolic consumption and user fatigue perception, the condition with electrical stimulation and the condition with the real weight differed significantly from the condition without any load: the implemented feedback was able to faithfully reproduce interactions with objects, suggesting its possible application in different areas such as gaming, work risk assessment simulation, and education.
Structural Health Monitoring of Fatigue Cracks for Steel Bridges with Wireless Large-Area Strain Sensors
Authors: Taher Sdiq Anwar , Li Jian , Jeong Jong-Hyun , Laflamme Simon , Jo Hongki , Bennett Caroline , Collins William N. , Downey Austin R. J.
Published: July 2022

This paper presents a field implementation of the structural health monitoring (SHM) of fatigue cracks for steel bridge structures. Steel bridges experience fatigue cracks under repetitive traffic loading, which pose great threats to their structural integrity and can lead to catastrophic failures. Currently, accurate and reliable fatigue crack monitoring for the safety assessment of bridges is still a difficult task. On the other hand, wireless smart sensors have achieved great success in global SHM by enabling long-term modal identifications of civil structures. However, long-term field monitoring of localized damage such as fatigue cracks has been limited due to the lack of effective sensors and the associated algorithms specifically designed for fatigue crack monitoring. To fill this gap, this paper proposes a wireless large-area strain sensor (WLASS) to measure large-area strain fatigue cracks and develops an effective algorithm to process the measured large-area strain data into actionable information. The proposed WLASS consists of a soft elastomeric capacitor (SEC) used to measure large-area structural surface strain, a capacitive sensor board to convert the signal from SEC to a measurable change in voltage, and a commercial wireless smart sensor platform for triggered-based wireless data acquisition, remote data retrieval, and cloud storage. Meanwhile, the developed algorithm for fatigue crack monitoring processes the data obtained from the WLASS under traffic loading through three automated steps, including (1) traffic event detection, (2) time-frequency analysis using a generalized Morse wavelet (GM-CWT) and peak identification, and (3) a modified crack growth index (CGI) that tracks potential fatigue crack growth. The developed WLASS and the algorithm present a complete system for long-term fatigue crack monitoring in the field. The effectiveness of the proposed time-frequency analysis algorithm based on GM-CWT to reliably extract the impulsive traffic events is validated using a numerical investigation. Subsequently, the developed WLASS and algorithm are validated through a field deployment on a steel highway bridge in Kansas City, KS, USA.