bluuebunny/crossref_metadata_2025 huggingface dataset:
Systematic Comparison of Hospital-Wide Standard and Model-Based Therapeutic Drug Monitoring of Vancomycin in Adults
Authors: Gastmans Heleen , Dreesen Erwin , Wicha Sebastian G. , Dia Nada , Spreuwers Ellen , Dompas Annabel , Allegaert Karel , Desmet Stefanie , Lagrou Katrien , Peetermans Willy E. , Debaveye Yves , Spriet Isabel , Gijsen Matthias
Published: July 2022

We aimed to evaluate the predictive performance and predicted doses of a single-model approach or several multi-model approaches compared with the standard therapeutic drug monitoring (TDM)-based vancomycin dosing. We performed a hospital-wide monocentric retrospective study in adult patients treated with either intermittent or continuous vancomycin infusions. Each patient provided two randomly selected pairs of two consecutive vancomycin concentrations. A web-based precision dosing software, TDMx, was used to evaluate the model-based approaches. In total, 154 patients contributed 308 pairs. With standard TDM-based dosing, only 48.1% (148/308) of all of the second concentrations were within the therapeutic range. Across the model-based approaches we investigated, the mean relative bias and relative root mean square error varied from −5.36% to 3.18% and from 24.8% to 28.1%, respectively. The model averaging approach according to the squared prediction errors showed an acceptable bias and was the most precise. According to this approach, the median (interquartile range) differences between the model-predicted and prescribed doses, expressed as mg every 12 h, were 113 [−69; 427] mg, −70 [−208; 120], mg and 40 [−84; 197] mg in the case of subtherapeutic, supratherapeutic, and therapeutic exposure at the second concentration, respectively. These dose differences, along with poor target attainment, suggest a large window of opportunity for the model-based TDM compared with the standard TDM-based vancomycin dosing. Implementation studies of model-based TDM in routine care are warranted.
Integration of In Silico Strategies for Drug Repositioning towards P38α Mitogen-Activated Protein Kinase (MAPK) at the Allosteric Site
Authors: Suriya Utid , Mahalapbutr Panupong , Rungrotmongkol Thanyada
Published: July 2022

P38α mitogen-activated protein kinase (p38α MAPK), one of the p38 MAPK isoforms participating in a signaling cascade, has been identified for its pivotal role in the regulation of physiological processes such as cell proliferation, differentiation, survival, and death. Herein, by shedding light on docking- and 100-ns dynamic-based screening from 3210 FDA-approved drugs, we found that lomitapide (a lipid-lowering agent) and nilotinib (a Bcr-Abl fusion protein inhibitor) could alternatively inhibit phosphorylation of p38α MAPK at the allosteric site. All-atom molecular dynamics simulations and free energy calculations including end-point and QM-based ONIOM methods revealed that the binding affinity of the two screened drugs exhibited a comparable level as the known p38α MAPK inhibitor (BIRB796), suggesting the high potential of being a novel p38α MAPK inhibitor. In addition, noncovalent contacts and the number of hydrogen bonds were found to be corresponding with the great binding recognition. Key influential amino acids were mostly hydrophobic residues, while the two charged residues including E71 and D168 were considered crucial ones due to their ability to form very strong H-bonds with the focused drugs. Altogether, our contributions obtained here could be theoretical guidance for further conducting experimental-based preclinical studies necessary for developing therapeutic agents targeting p38α MAPK.
Recent Advances in Aerobic Photo-Oxidation over Small-Sized IB Metal Nanoparticles
Authors: Zhang Yifei , Wang Meng , Li Gao
Published: July 2022

Aerobic photo-oxidation is a kind of green catalytic process that give valuable chemicals because of its mild reaction conditions and high product selectivity. Recently, small-sized IB metal nanoparticles (NPs; e.g., Cu, Ag, and Au, sized 1–3 nm) upon the surface of titanium oxide show excellent photocatalytic performance. The introduction of IB metal NPs can enhance the separation of photo-generated holes/electrons during photo-oxidations. In this account, we summarize the recent progress of small-sized IB metal NPs catalyzed by aerobic photo-oxidations, including the conversion of methanol, ethanol, sulfide, and benzylamine. More importantly, the structure–property correlations at the atomic level are detailed and discussed, e.g., the insights into the activation of oxygen and the identification of catalytic active sites. Future investigations are needed to carry out and reveal the catalytic mechanisms and conversion pathways.
The Performance of Orbital Angular Momentum Mode (|l| = 1~3) Amplification Based on Ring-Core Erbium-Doped Fibers
Authors: Liu Shuaishuai , Zhang Liang , Jiang Qi , Xue Xiaobo , Wen Jianxiang , Chen Wei , Pang Fufei , Wang Tingyun
Published: July 2022

We demonstrated that a ring-core erbium-doped fiber amplifier (RC-EDFA) can support orbital angular momentum (OAM) modes with topological charges (|l| = 1~3). The dependence of the characteristics on the length of the RC-EDF was investigated experimentally, including an investigation of gain and 3 dB gain bandwidth over the whole C band (i.e., 1530~1565 nm). The 3 dB gain bandwidth was improved to 21 nm. At a signal wavelength of 1550 nm, the maximum gain of all signal modes was up to 30.1 dB. Differential modal gain was maintained below approximately 1.3 dB.
Perturbative QCD Core of Hadrons and Color Transparency Phenomena
Authors: Frankfurt Leonid , Strikman Mark
Published: July 2022

In the current paper, we argue that the ground state of a hadron contains a significant perturbative quantum chromodynamics (pQCD) core as the result of color gauge invariance and the values of chiral and gluon vacuum condensates. The evaluation within the method of dispersion sum rules (DSR) of the vacuum matrix elements of the correlator of local currents with the proper quantum numbers leads to the value of the radius of the pQCD core of a nucleon of about 0.4–0.5 fm. The selection of the initial and final states allows to select processes in which the pQCD core of the projectile gives the dominant contribution to the process. It is explained that the transparency of nuclear matter for the propagation of a spatially small and color-neutral wave packet of quarks and gluons—a color transparency (CT) phenomenon—for a group of hard processes off nuclear targets can be derived in the form of the QCD factorization theorem accounting for the color screening phenomenon. Based on the success of the method of DSR, we argue that a pQCD core in a hadron wave function is surrounded by the layer consisting of quarks interacting with quark and gluon condensates. As a result, in the quasi-elastic processes e+A→e′+N+(A−1)∗, the quasi-Feynman mechanism could be dominating in a wide range of the momentum transfer squared, Q2. In this scenario, a virtual photon is absorbed by a single quark, which carries a large fraction of the momentum of the nucleon and dominates in a wide range of Q2. CT should reveal itself in these processes at extremely large Q2 as the consequence of the presence of the Sudakov form factors, which squeeze a nucleon.
The Cholesterol-Modulating Effect of the New Herbal Medicinal Recipe from Yellow Vine (Coscinium fenestratum (Goetgh.)), Ginger (Zingiber officinale Roscoe.), and Safflower (Carthamus tinctorius L.) on Suppressing PCSK9 Expression to Upregulate LDLR Expression in HepG2 Cells
Authors: Ongtanasup Tassanee , Prommee Nuntika , Jampa Onkamon , Limcharoen Thanchanok , Wanmasae Smith , Nissapatorn Veeranoot , Paul Alok K. , Pereira Maria de Lourdes , Wilairatana Polrat , Nasongkla Norased , Eawsakul Komgrit
Published: July 2022

PCSK9 is a promising target for developing novel cholesterol-lowering drugs. We developed a recipe that combined molecular docking, GC-MS/MS, and real-time PCR to identify potential PCSK9 inhibitors for herb ratio determination. Three herbs, Carthamus tinctorius, Coscinium fenestratum, and Zingiber officinale, were used in this study. This work aimed to evaluate cholesterol-lowering through a PCSK9 inhibitory mechanism of these three herbs for defining a suitable ratio. Chemical constituents were identified using GC-MS/MS. The PCSK9 inhibitory potential of the compounds was determined using molecular docking, real-time PCR, and Oil red O staining. It has been shown that most of the active compounds of C. fenestratum and Z. officinale inhibit PCSK9 when extracted with water, and C. fenestratum has been shown to yield tetraacetyl-d-xylonic nitrile (27.92%) and inositol, 1-deoxy-(24.89%). These compounds could inhibit PCSK9 through the binding of 6 and 5 hydrogen bonds, respectively, while the active compound in Z. officinale is 2-Formyl-9-[.beta.-d-ribofuranosyl] hypoxanthine (4.37%) inhibits PCSK9 by forming 8 hydrogen bonds. These results suggest that a recipe comprising three parts C. fenestratum, two parts Z. officinale, and one part C. tinctorius is a suitable herbal ratio for reducing lipid levels in the bloodstream through a PCSK9 inhibitory mechanism.
Pleiotropic Effect of the compactum Gene and Its Combined Effects with Other Loci for Spike and Grain-Related Traits in Wheat
Authors: Wen Mingxing , Su Jiaxuan , Jiao Chengzhi , Zhang Xu , Xu Tao , Wang Tong , Liu Xiaoxue , Wang Zongkuan , Sun Li , Yuan Chunxia , Wang Haiyan , Wang Xiue , Xiao Jin
Published: July 2022

Club wheat (Triticum aestivum ssp. compactum) with a distinctly compact spike morphology was conditioned by the dominant compactum (C) locus on chromosome 2D and resulted in a redistribution of spike yield components. The disclosure of the genetic basis of club wheat was a prerequisite for the development of widely adapted, agronomically competitive club wheat cultivars. In this study, we used a recombinant inbred line population derived from a cross between club wheat Hiller and modern cultivar Yangmai 158 to construct a genetic linkage map and identify quantitative trait loci associated with 15 morphological traits. The club allele acted in a semi-dominant manner and the C gene was mapped to 370.12–406.29 Mb physical region on the long arm of 2D. Apart from compact spikes, C exhibited a pleiotropic effect on ten other agronomic traits, including plant height, three spike-related traits and six grain-related traits. The compact spike phenotype was correlated with decreased grain size and weight, but with an increase in floret fertility and grain number. These pleiotropic effects make club wheat have compatible spike weight with a normal spike from common wheat. The genetic effects of various gene combinations of C with four yield-related genes, including Ppd-D1, Vrn-D3, Rht-B1b and Rht8, were evaluated. C had no epistatic interaction with any of these genes, indicating that their combinations would have an additive effect on other agronomically important traits. Our research provided a theoretical foundation for the potentially effective deployment of C gene into modern breeding varieties in combination with other favorable alleles.
Complete Genome Sequence of an Isolate of Passiflora chlorosis virus from Passion Fruit (Passiflora edulis Sims)
Authors: Fresnillo Patricia , Jover-Gil Sara , Samach Alon , Candela Héctor
Published: July 2022

We report the first complete genome sequence of an isolate of Passiflora chlorosis virus (PaCV), a member of the Potyviridae family, identified in passion fruit (Passiflora edulis Sims) plants grown in Israel. The assembled genome is 9672 nucleotides long and encodes a 3084 amino acids polyprotein that is predicted to be proteolytically cleaved into 10 mature peptides. Our analysis of the genome sequence shows that PaCV is a distinct species, sharing 68.5% nucleotide sequence identity and 71.5% amino acid sequence identity with isolates of the bean common mosaic necrosis virus (BCMNV), the most closely related virus classified within the genus Potyvirus. Using quantitative PCR, we detected the virus in RNA samples from leaves exhibiting symptoms of infection, with higher levels in clearly chlorotic leaves, but not in those from healthy leaves.
Research on Friction and Wear Properties of Rubber Composites by Adding Glass Fiber during Mixing
Authors: Han Deshang , Chen Yihui , Pan Yi , Wang Chuansheng , Zhang Dewei
Published: July 2022

GF/rubber composites have sound insulation characteristics, heat resistance, good corrosion resistance, and high mechanical strength. The compounding machine’s long working hours will inevitably wear the metal on the end face of the compounding machine. The wear of the end face metal will increase the gap between the chamber and the end face, which will lead to material leakage, reduce the mixing effect, and eventually affect the performance of GF/rubber composites. To ensure the implementation of GF/rubber composites, it is necessary to study the frictional wear behavior of GF/rubber composites on metals. In this paper, the effect of blending rubber with different amounts of GF on the frictional wear of metal on the end face was analyzed from the perspective of the formulation process, and the ratio of corrosion wear and abrasive wear was calculated for the first time.
Increasing the Efficiency of a Control System for Detecting the Type and Amount of Oil Product Passing through Pipelines Based on Gamma-Ray Attenuation, Time Domain Feature Extraction, and Artificial Neural Networks
Authors: Mayet Abdulilah Mohammad , Alizadeh Seyed Mehdi , Kakarash Zana Azeez , Al-Qahtani Ali Awadh , Alanazi Abdullah K. , Grimaldo Guerrero John William , Alhashimi Hala H. , Eftekhari-Zadeh Ehsan
Published: July 2022

Instantaneously determining the type and amount of oil product passing through pipelines is one of the most critical operations in the oil, polymer and petrochemical industries. In this research, a detection system is proposed in order to monitor oil pipelines. The system uses a dual-energy gamma source of americium-241 and barium-133, a test pipe, and a NaI detector. This structure is implemented in the Monte Carlo N Particle (MCNP) code. It should be noted that the results of this simulation have been validated with a laboratory structure. In the test pipe, four oil products—ethylene glycol, crude oil, gasoil, and gasoline—were simulated two by two at various volume percentages. After receiving the signal from the detector, the feature extraction operation was started in order to provide suitable inputs for training the neural network. Four time characteristics—variance, fourth order moment, skewness, and kurtosis—were extracted from the received signal and used as the inputs of four Radial Basis Function (RBF) neural networks. The implemented neural networks were able to predict the volume ratio of each product with great accuracy. High accuracy, low cost in implementing the proposed system, and lower computational cost than previous detection methods are among the advantages of this research that increases its applicability in the oil industry. It is worth mentioning that although the presented system in this study is for monitoring of petroleum fluids, it can be easily used for other types of fluids such as polymeric fluids.
Biosorption Potential of Microbial and Residual Biomass of Saccharomyces pastorianus Immobilized in Calcium Alginate Matrix for Pharmaceuticals Removal from Aqueous Solutions
Authors: Rusu Lăcrămioara , Grigoraș Cristina-Gabriela , Simion Andrei-Ionuț , Suceveanu Elena-Mirela , Istrate Bogdan , Harja Maria
Published: July 2022

Two types of biosorbents, based on Saccharomyces pastorianus immobilized in calcium alginate, were studied for the removal of pharmaceuticals from aqueous solutions. Synthetized biocomposite materials were characterized chemically and morphologically, both before and after simulated biosorption. Ethacridine lactate (EL) was chosen as a target molecule. The process performance was interpreted as a function of initial solution pH, biosorbent dose, and initial pharmaceutical concentration. The results exhibited that the removal efficiencies were superior to 90% for both biosorbents, at the initial pH value of 4.0 and biosorbent dose of 2 g/L for all EL initial concentrations tested. Freundlich, Temkin, Hill, Redlich-Peterson, Sips, and Toth isotherms were used to describe the experimental results. The kinetic data were analyzed using kinetic models, such as pseudo-first order, pseudo-second order, Elovich, and Avrami, to determine the kinetic parameters and describe the transport mechanisms of EL from aqueous solution onto biosorbents. Among the tested equations, the best fit is ensured by the pseudo-second-order kinetics model for both biosorbents, with the correlation coefficient having values higher than 0.996. The many potential advantages and good biosorptive capacity of Saccharomyces pastorianus biomass immobilized in calcium alginate recommend these types of biocomposite materials for the removal of pharmaceuticals from aqueous solutions.
A Supramolecular Hydrogel Based on Copolymers of Acrylic Acid and Maleic Anhydride Derivatives with Terpyridine Motifs
Authors: Chi Zheng , Ma Chenchen , He Ziyuan , Ma Zihan , Chen Xuegang , Huang Zhaoge
Published: July 2022

A kind of terpyridine derivative (NH2-Tpy) in which the amino was incorporated by a short alkyl chain was synthesized. Through grafting of terpyridine units into the hydrophilic copolymers of maleic anhydride and acrylic acid PAAMa via the reaction of the amino groups in NH2-Tpy and the maleic anhydride units, a series of gelator polymers—P1, P2, and P3—containing different contents of terpyridine units was synthesized. Under coordination of Ni2+ and terpyridine ligands in linear polymers, the supramolecular hydrogels H1, H2, and H3 with different cross-linking degrees were prepared. The linear polymers P1–P3 had a strong absorption peak at about 290 nm in the UV-vis spectra which was attributed to π–π* transition, and there was a new peak at about 335 nm led by the metal-to-ligands charge transfer (MLCT) when coordinated with Ni2+ ions. According to the rheological behaviors, the storage modulus (G′) was larger than the loss modulus (G′′). These hydrogels showed typical gel-like characteristics when the terpyridine content of the hydrogels exceeded 10%, and the hydrogels showed liquid-like characteristics when the terpyridine content of the hydrogels was less than 7%. The results of the micromorphological investigation of the xerogels from SEM illustrated the metal–terpyridine coordination cross-linking could have an important influence on the microstructures of the resulting hydrogels. Furthermore, these hydrogels based on supramolecular cross-links exhibited reversible solution–gel transition at different environmental temperatures. At the same time, the equilibrium swelling of the supramolecular hydrogels was 8.0–12.3 g/g, which increased with the decrease in the content of the terpyridine units in the resulting hydrogels.
Isolation, Characterization, and Breast Cancer Cytotoxic Activity of Gyrophoric Acid from the Lichen Umbilicaria muhlenbergii
Authors: Mohammadi Mahshid , Zambare Vasudeo , Suntres Zacharias , Christopher Lew
Published: July 2022

Lichens produce a large variety of secondary metabolites with diverse bioactivities, chemical structures, and physicochemical properties. For this reason, there is a growing interest in the use of lichen-derived bioactive molecules for drug discovery and development. Here, we report on the isolation, identification, and cytotoxic evaluation of gyrophoric acid (GA) from the lichen Umbilicaria muhlenbergii, a largely unexplored and scantly described lichen species. A simple purification protocol was developed for the fractionation of lichen crude extracts with silica gel column chromatography using solvents with changing polarity. GA was identified in one of the fractions with Fourier transform infrared spectroscopy (FTIR), ion trap mass spectrometry (MS), and nuclear magnetic resonance spectroscopy (1H-NMR and 13C-NMR). The FTIR spectra demonstrated the presence of aromatic and ester functional groups C=C, C-H, and C=O bonds, with the most remarkable signals recorded at 1400 cm−1 for the aromatic region, at 1400 cm−1 for the CH3 groups, and at 1650 cm−1 for the carbonyl groups in GA. The MS spectra showed a molecular ion [M-1]− at (m/z) 467 with a molecular weight of 468.4 and the molecular formula C24H20O10. that correspond to GA. The 1H-NMR and 13C-NMR spectra verified the chemical shifts that are typical for GA. GA reduced the cell viability of breast cancer cells from the MCF-7 cell line by 98%, which is indicative of the strong cytotoxic properties of GA and its significant potential to serve as a potent anticancer drug.
A Long-Term Decarbonisation Modelling and Optimisation Approach for Transport Sector Planning Considering Modal Shift and Infrastructure Construction: A Case Study of China
Authors: Li Chenxi , Liu Pei , Li Zheng
Published: July 2022

Reducing direct carbon emissions in the transport sector is crucial for carbon neutrality. It is a considerable challenge to achieve substantial CO2 emissions reductions while satisfying rapidly growing traffic demands. Previous studies cannot be applied directly in long-term planning for the transport sector with rapid demand growth. To bridge this gap, a multi-regional model is proposed in this paper to quantify the optimal decarbonisation path for the transport sector in order to save costs. Considering modal shift and infrastructure construction, this model regards the transport sector as a whole and China is taken as a case study. The results show that electricity and hydrogen will be the major fuels of the transport sector in the future, accounting for 45 percent and 25 percent of fuel demands in 2060. This means that the electricity used by the transport sector accounts for 10 percent of the electricity consumed by the whole of society. The results reflect that freight transport has reached a CO2 emissions peak, while passenger transport will reach its own CO2 emissions peak around 2041. Giving priority to decarbonisation in freight transport can save 5 percent of the transition cost. The results also suggest that modal shift can save at most 7 percent of the transition cost.
MR-LINAC-Guided Adaptive Radiotherapy for Gastric MALT: Two Case Reports and a Literature Review
Authors: Song Yajun , Li Zhenjiang , Wang Huadong , Zhang Yun , Yue Jinbo
Published: July 2022

It is still very challenging to use conventional radiation therapy techniques to treat stomach tumors, although image-guided radiotherapy, mainly by kV X-ray imaging techniques, has become routine in the clinic. This is because the stomach is one of the most deformable organs, and thus it is vulnerable to respiratory motions, daily diet, and body position changes. In addition, X-ray radiographs and CT volumetric images have low contrast in soft tissues. In contrast, magnetic resonance imaging (MRI) techniques provide good contrast in images of soft tissues. The emerging MR-guided radiotherapy, based on the MR-LINAC system, may have the potential to solve the above difficulties due to its unique advantages. The real-time imaging feature and the high-contrast of soft tissues MR images provided by the MR-LINAC system have facilitated the therapeutic adaptive planning. Online learning capabilities could be used to optimize the automatic delineation of the target organ or tissue prior to each radiotherapy session. This could greatly improve the accuracy and efficiency of the target delineation in adaptive planning. In this clinical case report, we elaborated a workflow for the diagnosis and treatment of two patients with gastric mucosa-associated lymphoid tissue (MALT) lymphoma. One patient underwent MR-guided daily adaptive radiotherapy based on daily automated segmentation using the novel artificial intelligence (AI) technique for gastric delineation.
Toward Avoiding Misalignment: Dimensional Synthesis of Task-Oriented Upper-Limb Hybrid Exoskeleton
Authors: Gupta Sakshi , Agrawal Anupam , Singla Ekta
Published: July 2022

One of the primary reasons for wearable exoskeleton rejection is user discomfort caused by misalignment between the coupled system, i.e., the human limb and the exoskeleton. The article focuses primarily on the solution strategies for misalignment issues. The purpose of this work is to facilitate rehabilitative exercise-based exoskeletons for neurological and muscular disorder patients, which can aid a user in following the appropriate natural trajectory with the least amount of misalignment. A double four-bar planar configuration is used for this purpose. The paper proposes a methodology for developing an optimum task-oriented upper-limb hybrid exoskeleton with low active degrees-of-freedom (dof) that enables users to attain desired task space locations (TSLs) while maintaining an acceptable range of kinematic performance. Additionally, the study examines the influence of an extra restriction placed at the elbow motion and the compatibility of connected systems. The findings and discussion indicate the usefulness of the proposed concept for upper-limb rehabilitation.
Turning the Tide on Mapping Marginal Mangroves with Multi-Dimensional Space–Time Remote Sensing
Authors: Hickey Sharyn M. , Radford Ben
Published: July 2022

Mangroves are a globally important ecosystem experiencing significant anthropogenic and climate impacts. Two subtypes of mangrove are particularly vulnerable to climate-induced impacts (1): tidally submerged forests and (2) those that occur in arid and semi-arid regions. These mangroves are either susceptible to sea level rise or occur in conditions close to their physiological limits of temperature and freshwater availability. The spatial extent and impacts on these mangroves are poorly documented, because they have structural and environmental characteristics that affect their ability to be detected with remote sensing models. For example, tidally submerged mangroves occur in areas with large tidal ranges, which limits their visibility at high tide, and arid mangroves have sparse canopy cover and a shorter stature that occur in fringing and narrow stands parallel to the coastline. This study introduced the multi-dimensional space–time randomForest method (MSTRF) that increases the detectability of these mangroves and applies this on the North-west Australian coastline where both mangrove types are prevalent. MSTRF identified an optimal four-year period that produced the most accurate model (Accuracy of 80%, Kappa value 0.61). This model was able to detect an additional 32% (76,048 hectares) of mangroves that were previously undocumented in other datasets. We detected more mangrove cover using this timeseries combination of annual median composite Landsat images derived from scenes across the whole tidal cycle but also over climatic cycles such as EÑSO. The median composite images displayed less spectral differences in mangroves in the intertidal and arid zones compared to individual scenes where water was present during the tidal cycle or where the chlorophyll reflectance was low during hot and dry periods. We found that the MNDWI (Modified Normalised Water Index) and GCVI (Green Chlorophyll Vegetation Index) were the best predictors for deriving the mangrove layer using randomForest.
S-NPP VIIRS Lunar Calibrations over 10 Years in Reflective Solar Bands (RSB)
Authors: Choi Taeyoung , Cao Changyong , Shao Xi , Wang Wenhui
Published: July 2022

Since 28 October 2011, the VIIRS Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (S-NPP) has operated over 10 years and successfully generated scientific global images for the Earth’s environment and climate studies. Besides thermal and day night bands, VIIRS has 14 reflective solar bands (RSBs) that cover a spectral range of 0.41 µm to 2.25 µm. The primary and daily source of calibration for the RSBs is the Solar Diffuser (SD) as an onboard calibrator, and its degradations are tracked by the Solar Diffuser Stability Monitor (SDSM). Alternatively, monthly scheduled lunar calibration has provided long-term on-orbit trends that validate the corresponding SD-based calibration results. In this paper, on-orbit lunar calibration and comparison results are focused on, in conjunction with the SD calibrations that are performed by the National Oceanic and Atmospheric Administration (NOAA) VIIRS team. In addition, a recent study showed that there is increasing striping in the VIIRS images in the RSBs caused by the non-uniform SD degradation. The estimation of the SD non-uniformity and a mitigation method is proposed along with the striping reductions.
GPR Data Processing and Interpretation Based on Artificial Intelligence Approaches: Future Perspectives for Archaeological Prospection
Authors: Küçükdemirci Melda , Sarris Apostolos
Published: July 2022

Ground penetrating radar (GPR) is a well-established technique used in archaeological prospection and it requires a number of specialized routines for signal and image processing to enhance the data acquired and lead towards a better interpretation of them. Computer-aided techniques have advanced the interpretation of GPR data, dealing with a wide range of operations aiming towards locating, imaging, and diagnosis/interpretation. This article will discuss the novel and recent applications of machine learning (ML) and deep learning (DL) techniques, under the artificial intelligence umbrella, for processing GPR measurements within archaeological contexts, and their potential, limitations, and possible future prospects.
A Method for SRTM DEM Elevation Error Correction in Forested Areas Using ICESat-2 Data and Vegetation Classification Data
Authors: Li Yi , Fu Haiqiang , Zhu Jianjun , Wu Kefu , Yang Panfeng , Wang Li , Gao Shijuan
Published: July 2022

The past decade has witnessed the rapid development of the SRTM (Shuttle Radar Topography Mission) DEM (digital elevation model) in engineering applications and scientific research. The near-global SRTM DEM was generated based on radar interference theory. The latest version of the SRTM DEM with a resolution of 1 arc-second has been widely used in various applications. However, many studies have shown the poor elevation accuracy of the SRTM DEM in forested areas. Recent developments in the field of spaceborne lidar have provided an additional chance to correct the elevation error of the SRTM DEM in forested areas. We developed an easy-to-use method to correct the elevation error of the SRTM DEM based on the spatial interpolation method using the recent Ice, Cloud and land Elevation Satellite-2 data. First, an ICESat-2 terrain control point selection criterion was proposed to reject some erroneous ICESat-2 terrains caused by many factors. Second, we derived the elevation correction surface based on the interpolation method using the refined ICESat-2 terrain. Finally, a corrected SRTM DEM of forested areas was generated through the obtained elevation correction surface. The proposed method was tested in the typical forested area located in Massachusetts, USA. The results show that the RMSE of the selected terrain control points in vegetation areas and non-vegetation areas are 1.03 and 0.68 m, respectively. The corrected SRTM DEM have an RMSE of 4.2 m which is significantly less than that of the original SRTM DEM with an RMSE of 9.8 m, which demonstrates the proposed method is feasible to correct the elevation error in forested areas. It can be concluded that the proposed method obviously decreases the elevation error of the original SRTM DEM.
Analysis of PM2.5 Variations Based on Observed, Satellite-Derived, and Population-Weighted Concentrations
Authors: Fang Xin , Li Shenxin , Xiong Liwei , Zou Bin
Published: July 2022

Fine particulate matter (PM2.5), which can cause adverse human health effects, has been proven as the first air pollutant in China. In situ observations with ground-level monitoring and satellite-based concentrations have been used to analyze the variations in PM2.5. However, variation analyses based on these two kinds of measurement have mainly focused on the concentration itself and ignored the effects on the population. Therefore, this study not only investigated these two kinds of measurements, but also performed weighted population analyses to study the variations in PM2.5. Firstly, daily models of timely structure adaptive modeling (TSAM) were constructed to simulate satellite-derived PM2.5 levels from January 2013 to December 2016. Secondly, population-weighted concentrations were calculated based on TSAM-derived PM2.5 surfaces. Finally, observed, TSAM-derived, and population-weighted concentrations were used to analyze the variations in PM2.5. The results showed the different importance of various input parameters; AOD had the highest rank. Additionally, TSAM models demonstrated good performance, fitting R ranging from 0.86 to 0.91, and validating R from 0.82 to 0.89. According to the air quality standard in China, TSAM-derived PM2.5 showed that the increase in area lower than Level II was 29.03% and the increase in population was only 14.81%. This indicates that the air quality exhibited an overall improvement in spatial perspective, but some areas with high population density showed a relatively low improvement due to uneven distributions in China. The population-weighted PM2.5 concentration could better represent the health threats of air pollutants compared with in situ observations.
Personalized Activity Recognition with Deep Triplet Embeddings
Authors: Burns David , Boyer Philip , Arrowsmith Colin , Whyne Cari
Published: July 2022

A significant challenge for a supervised learning approach to inertial human activity recognition is the heterogeneity of data generated by individual users, resulting in very poor performance for some subjects. We present an approach to personalized activity recognition based on deep feature representation derived from a convolutional neural network (CNN). We experiment with both categorical cross-entropy loss and triplet loss for training, and describe a novel loss function based on subject triplets. We evaluate these methods on three publicly available inertial human activity recognition datasets (MHEALTH, WISDM, and SPAR) comparing classification accuracy, out-of-distribution activity detection, and generalization to new activity classes. The proposed triplet algorithm achieved an average 96.7% classification accuracy across tested datasets versus the 87.5% achieved by the baseline CNN algorithm. We demonstrate that personalized algorithms, and, in particular, the proposed novel triplet loss algorithms, are more robust to inter-subject variability and thus exhibit better performance on classification and out-of-distribution detection tasks.
Sentiment Analysis: An ERNIE-BiLSTM Approach to Bullet Screen Comments
Authors: Hsieh Yen-Hao , Zeng Xin-Ping
Published: July 2022

Sentiment analysis is one of the fields of affective computing, which detects and evaluates people’s psychological states and sentiments through text analysis. It is an important application of text mining technology and is widely used to analyze comments. Bullet screen videos have become a popular way for people to interact and communicate while watching online videos. Existing studies have focused on the form, content, and function of bullet screen comments, but few have examined bullet screen comments using natural language processing. Bullet screen comments are short text messages of different lengths and ambiguous emotional information, which makes it extremely challenging in natural language processing. Hence, it is important to understand how we can use the characteristics of bullet screen comments and sentiment analysis to understand the sentiments expressed and trends in bullet screen comments. This study poses the following research question: how can one analyze the sentiments ex-pressed in bullet screen comments accurately and effectively? This study mainly proposes an ERNIE-BiLSTM approach for sentiment analysis on bullet screen comments, which provides effective and innovative thinking for the sentiment analysis of bullet screen comments. The experimental results show that the ERNIE-BiLSTM approach has a higher accuracy rate, precision rate, recall rate, and F1-score than other methods.
Positioning and Tracking of Multiple Humans Moving in Small Rooms Based on a One-Transmitter–Two-Receiver UWB Radar Configuration
Authors: Fortes Jana , Švingál Michal , Porteleky Tamás , Jurík Patrik , Drutarovský Miloš
Published: July 2022

The paper aims to propose a sequence of steps that will allow multi-person tracking with a single UWB radar equipped with the minimal antenna array needed for trilateration. Its localization accuracy is admittedly limited, but on the other hand, thoughtfully chosen placement of antennas can increase the detectability of several humans moving in their immediate vicinity and additionally decrease the computational complexity of the signal processing methods. It is shown that the UWB radar measuring with high rate and fine range resolution in conjunction with properly tuned processing parameters can continually track people even in the case when their radar echoes are crossing or merging. Emphasis is given to the simplified method of the time-of-arrival (TOA) estimation and association and the novel method needed for antenna height compensation. The performance of the proposed human tracking framework is evaluated for the experimental scenario with three people moving closely in a small room. A quantitative analysis of the estimated target tracks confirms the benefits of suggested high antenna placement and application of new signal processing methods in the form of decreasing the mean localization error and increasing the frequency of correct target position estimations.
Automatic Extraction of Muscle Parameters with Attention UNet in Ultrasonography
Authors: Katakis Sofoklis , Barotsis Nikolaos , Kakotaritis Alexandros , Economou George , Panagiotopoulos Elias , Panayiotakis George
Published: July 2022

Automatically delineating the deep and superficial aponeurosis of the skeletal muscles from ultrasound images is important in many aspects of the clinical routine. In particular, finding muscle parameters, such as thickness, fascicle length or pennation angle, is a time-consuming clinical task requiring both human labour and specialised knowledge. In this study, a multi-step solution for automating these tasks is presented. A process to effortlessly extract the aponeurosis for automatically measuring the muscle thickness has been introduced as a first step. This process consists mainly of three parts. In the first part, the Attention UNet has been incorporated to automatically delineate the boundaries of the studied muscles. Afterwards, a specialised post-processing algorithm was utilised to improve (and correct) the segmentation results. Lastly, the calculation of the muscle thickness was performed. The proposed method has achieved similar to a human-level performance. In particular, the overall discrepancy between the automatic and the manual muscle thickness measurements was equal to 0.4 mm, a significant result that demonstrates the feasibility of automating this task. In the second step of the proposed methodology, the fascicle’s length and pennation angle are extracted through an unsupervised pipeline. Initially, filtering is applied to the ultrasound images to further distinguish the tissues from the other muscle structures. Later, the well-known K-Means algorithm is used to isolate them successfully. As the last step, the dominant angle of the segmented muscle tissues is reported and compared with manual measurements. The proposed pipeline is showing very promising results in the evaluated dataset. Specifically, in the calculation of the pennation angle, the overall discrepancy between the automatic and the manual measurements was less than 2.22° (degrees), once more comparable with the human-level performance. Finally, regarding the fascicle length measurements, the results were divided based on the muscle properties. In the muscles where a large portion (or all) of the fascicles are located between the upper and lower aponeuroses, the proposed pipeline exhibits superb performance; otherwise, overall accuracy deteriorates due to errors caused by the trigonometric approximations needed for the length calculation.
Blockchain–Cloud Integration: A Survey
Authors: Khanna Abhirup , Sah Anushree , Bolshev Vadim , Burgio Alessandro , Panchenko Vladimir , Jasiński Marek
Published: July 2022

Over the last couple of years, Blockchain technology has emerged as a game-changer for various industry domains, ranging from FinTech and the supply chain to healthcare and education, thereby enabling them to meet the competitive market demands and end-user requirements. Blockchain technology gained its popularity after the massive success of Bitcoin, of which it constitutes the backbone technology. While blockchain is still emerging and finding its foothold across domains, Cloud computing is comparatively well defined and established. Organizations such as Amazon, IBM, Google, and Microsoft have extensively invested in Cloud and continue to provide a plethora of related services to a wide range of customers. The pay-per-use policy and easy access to resources are some of the biggest advantages of Cloud, but it continues to face challenges like data security, compliance, interoperability, and data management. In this article, we present the advantages of integrating Cloud and blockchain technology along with applications of Blockchain-as-a-Service. The article presents itself with a detailed survey illustrating recent works combining the amalgamation of both technologies. The survey also talks about blockchain–cloud services being offered by existing Cloud Service providers.
Human Grasp Mechanism Understanding, Human-Inspired Grasp Control and Robotic Grasping Planning for Agricultural Robots
Authors: Zheng Wei , Guo Ning , Zhang Baohua , Zhou Jun , Tian Guangzhao , Xiong Yingjun
Published: July 2022

As the end execution tool of agricultural robots, the manipulator directly determines whether the grasping task can be successfully completed. The human hand can adapt to various objects and achieve stable grasping, which is the highest goal for manipulator design and development. Thus, this study combines a multi-sensor fusion tactile glove to simulate manual grasping, explores the mechanism and characteristics of the human hand, and formulates rational grasping plans. According to the shape and size of fruits and vegetables, the grasping gesture library is summarized to facilitate the matching of optimal grasping gestures. By analyzing inter-finger curvature correlations and inter-joint pressure correlations, we investigated the synergistic motion characteristics of the human hand. In addition, the force data were processed by the wavelet transform algorithms and then the thresholds for sliding detection were set to ensure robust grasping. The acceleration law under the interaction with the external environment during grasping was also discussed, including stable movement, accidental collision, and placement of the target position. Finally, according to the analysis and summary of the manual gripping mechanism, the corresponding pre-gripping planning was designed to provide theoretical guidance and ideas for the gripping of robots.
Self-Supervised Sidewalk Perception Using Fast Video Semantic Segmentation for Robotic Wheelchairs in Smart Mobility
Authors: Pradeep Vishnu , Khemmar Redouane , Lecrosnier Louis , Duchemin Yann , Rossi Romain , Decoux Benoit
Published: July 2022

The real-time segmentation of sidewalk environments is critical to achieving autonomous navigation for robotic wheelchairs in urban territories. A robust and real-time video semantic segmentation offers an apt solution for advanced visual perception in such complex domains. The key to this proposition is to have a method with lightweight flow estimations and reliable feature extractions. We address this by selecting an approach based on recent trends in video segmentation. Although these approaches demonstrate efficient and cost-effective segmentation performance in cross-domain implementations, they require additional procedures to put their striking characteristics into practical use. We use our method for developing a visual perception technique to perform in urban sidewalk environments for the robotic wheelchair. We generate a collection of synthetic scenes in a blending target distribution to train and validate our approach. Experimental results show that our method improves prediction accuracy on our benchmark with tolerable loss of speed and without additional overhead. Overall, our technique serves as a reference to transfer and develop perception algorithms for any cross-domain visual perception applications with less downtime.
Non-Deep Active Learning for Deep Neural Networks
Authors: Kawano Yasufumi , Nota Yoshiki , Mochizuki Rinpei , Aoki Yoshimitsu
Published: July 2022

One way to improve annotation efficiency is active learning. The goal of active learning is to select images from many unlabeled images, where labeling will improve the accuracy of the machine learning model the most. To select the most informative unlabeled images, conventional methods use deep neural networks with a large number of computation nodes and long computation time, but we propose a non-deep neural network method that does not require any additional training for unlabeled image selection. The proposed method trains a task model on labeled images, and then the model predicts unlabeled images. Based on this prediction, an uncertainty indicator is generated for each unlabeled image. Images with a high uncertainty index are considered to have a high information content, and are selected for annotation. Our proposed method is based on a very simple and powerful idea: select samples near the decision boundary of the model. Experimental results on multiple datasets show that the proposed method achieves higher accuracy than conventional active learning methods on multiple tasks and up to 14 times faster execution time from 1.2 × 106 s to 8.3 × 104 s. The proposed method outperforms the current SoTA method by 1% accuracy on CIFAR-10.
Miniature Wide-Band Noise-Canceling CMOS LNA
Authors: Galante-Sempere David , del Pino Javier , Khemchandani Sunil Lalchand , García-Vázquez Hugo
Published: July 2022

In this paper, a wide-band noise-canceling (NC) current conveyor (CC)-based CMOS low-noise amplifier (LNA) is presented. The circuit employs a CC-based approach to obtain wide-band input matching without the need for bulky inductances, allowing broadband performance with a very small area used. The NC technique is applied by subtracting the input transistor’s noise contribution to the output and achieves a noise figure (NF) reduction from 4.8 dB to 3.2 dB. The NC LNA is implemented in a UMC 65-nm CMOS process and occupies an area of only 160 × 80 μm2. It achieves a stable frequency response from 0 to 6.2 GHz, a maximum gain of 15.3 dB, an input return loss (S11) < −10 dB, and a remarkable IIP3 of 7.6 dBm, while consuming 18.6 mW from a ±1.2 V DC supply. Comparisons with similar works prove the effectiveness of this new implementation, showing that the circuit obtains a noteworthy performance trade-off.