bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of the Polyphenolic Composition and Bioactivities of Three Native Cabo Verde Medicinal Plants
Authors: P. Essoh Anyse , Liberal Ângela , Fernandes Ângela , Dias Maria Inês , Pereira Carla , Mandim Filipa , Moldão-Martins Margarida , Cravo Pedro , Duarte Maria Paula , Moura Mónica , Romeiras Maria M. , Barros Lillian
Published: September 2022

The use of medicinal plants in a variety of health conditions remains essential for the discovery of new treatments. The present study aimed to investigate the bioactive properties of three native plants from Cabo Verde Islands, namely Artemisia gorgonum Webb, Sideroxylon marginatum (Decne. ex Webb) Cout., and Tamarix senegalensis DC., contributing to the characterization of less-known medicinal plants and their potential benefits for human health. Known compounds, such as kaempferol, quercetin, caffeyolquinic, and apigenin derivatives, among others, were detected in the plant species under study. Overall, all species demonstrated good antioxidant capacity, especially the ethanolic extracts of A. gorgonum (EC50 = 0.149 mg/mL) in TBARS assay. Moreover, the ethanolic extracts of the studied plants showed cytotoxic properties against tumor cells, and again the A. gorgonum extract proved to be the most effective in inhibiting tumor growth, mainly in the CaCO2 (GI50 = 17.3 μg/mL) and AGS (GI50 = 18.2 μg/mL) cell lines. Only the ethanolic extracts of T. senegalensis and S. marginatum demonstrated anti-inflammatory activity, albeit weak (EC50 = 35 and 43 μg/mL, respectively). The present study contributed to increased knowledge about the bioactive properties of these plants commonly used in traditional medicine, some of which was discussed for the first time, opening new perspectives for their use in a wider range of health conditions, especially in African countries, where access to modern health care is more limited.
Oral Nanomedicines for siRNA Delivery to Treat Inflammatory Bowel Disease
Authors: Shinn Jongyoon , Lee Juyeon , Lee Seon Ah , Lee Seon Ju , Choi Ah Hyun , Kim Jung Seo , Kim Su Jin , Kim Hyo Jin , Lee Cherin , Kim Yejin , Kim Joohyeon , Choi Jonghee , Jung Byungchae , Kim Taeho , Nam HyeonTaek , Kim Hyungjun , Lee Yonghyun
Published: September 2022

RNA interference (RNAi) therapies have significant potential for the treatment of inflammatory bowel diseases (IBD). Although administering small interfering RNA (siRNA) via an oral route is desirable, various hurdles including physicochemical, mucus, and cellular uptake barriers of the gastrointestinal tract (GIT) impede both the delivery of siRNA to the target site and the action of siRNA drugs at the target site. In this review, we first discuss various physicochemical and biological barriers in the GI tract. Furthermore, we present recent strategies and the progress of oral siRNA delivery strategies to treat IBD. Finally, we consider the challenges faced in the use of these strategies and future directions of oral siRNA delivery strategies.
Molecular Mechanisms of Cassia fistula against Epithelial Ovarian Cancer Using Network Pharmacology and Molecular Docking Approaches
Authors: Kanwal Aqsa , Azeem Farrukh , Nadeem Habibullah , Ashfaq Usman Ali , Aadil Rana Muhammad , Kober A. K. M. Humayun , Rajoka Muhammad Shahid Riaz , Rasul Ijaz
Published: September 2022

Epithelial ovarian cancer (EOC) is one of the deadliest reproductive tract malignancies that form on the external tissue covering of an ovary. Cassia fistula is popular for its anti-inflammatory and anticarcinogenic properties in conventional medications. Nevertheless, its molecular mechanisms are still unclear. The current study evaluated the potential of C. fistula for the treatment of EOC using network pharmacology approach integrated with molecular docking. Eight active constituents of C. fistula were obtained from two independent databases and the literature, and their targets were retrieved from the SwissTargetPrediction. In total, 1077 EOC associated genes were retrieved from DisGeNET and GeneCardsSuite databases, and 800 potential targets of eight active constituents of C. fistula were mapped to the 1077 EOC targets and intersected targets from two databases. Ultimately, 98 potential targets were found from C. fistula for EOC. Finally, the protein–protein interaction network (PPI) topological interpretation revealed AKT1, CTNNB1, ESR1, and CASP3 as key targets. This is the first time four genes have been found against EOC from C. fistula. The major enriched pathways of these candidate genes were established by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) investigations. To confirm the network pharmacology findings, the molecular docking approach demonstrated that active molecules have higher affinity for binding to putative targets for EOC suppression. More pharmacological and clinical research is required for the development of a drug to treat EOC.
Fabrication, In Vitro, and In Vivo Assessment of Eucalyptol-Loaded Nanoemulgel as a Novel Paradigm for Wound Healing
Authors: Rehman Anis , Iqbal Muhammad , Khan Barkat A. , Khan Muhammad Khalid , Huwaimel Bader , Alshehri Sameer , Alamri Ali H. , Alzhrani Rami M. , Bukhary Deena M. , Safhi Awaji Y. , Hosny Khaled M.
Published: September 2022

Wounds are the most common causes of mortality all over the world. Topical drug delivery systems are more efficient in treating wounds as compared to oral delivery systems because they bypass the disadvantages of the oral route. The aim of the present study was to formulate and evaluate in vitro in vivo nanoemulgels loaded with eucalyptol for wound healing. Nanoemulsions were prepared using the solvent emulsification diffusion method by mixing an aqueous phase and an oil phase, and a nanoemulgel was then fabricated by mixing nanoemulsions with a gelling agent (Carbopol 940) in a 1:1 ratio. The nanoemulgels were evaluated regarding stability, homogeneity, pH, viscosity, Fourier-transform infrared spectroscopy (FTIR), droplet size, zeta potential, polydispersity index (PDI), spreadability, drug content, in vitro drug release, and in vivo study. The optimized formulation, F5, exhibited pH values between 5 and 6, with no significant variations at different temperatures, and acceptable homogeneity and spreadability. F5 had a droplet size of 139 ± 5.8 nm, with a low polydispersity index. FTIR studies showed the compatibility of the drug with the excipients. The drug content of F5 was 94.81%. The percentage of wound contraction of the experimental, standard, and control groups were 100% ± 0.015, 98.170% ± 0.749, and 70.846% ± 0.830, respectively. Statistically, the experimental group showed a significant difference (p < 0.03) from the other two groups. The results suggest that the formulated optimized dosage showed optimum stability, and it can be considered an effective wound healing alternative.
A Review of Non-Invasive Drug Delivery through Respiratory Routes
Authors: Zhang Yong-Bo , Xu Dong , Bai Lu , Zhou Yan-Ming , Zhang Han , Cui Yuan-Lu
Published: September 2022

With rapid and non-invasive characteristics, the respiratory route of administration has drawn significant attention compared with the limitations of conventional routes. Respiratory delivery can bypass the physiological barrier to achieve local and systemic disease treatment. A scientometric analysis and review were used to analyze how respiratory delivery can contribute to local and systemic therapy. The literature data obtained from the Web of Science Core Collection database showed an increasing worldwide tendency toward respiratory delivery from 1998 to 2020. Keywords analysis suggested that nasal and pulmonary drug delivery are the leading research topics in respiratory delivery. Based on the results of scientometric analysis, the research hotspots mainly included therapy for central nervous systems (CNS) disorders (Parkinson’s disease, Alzheimer’s disease, depression, glioblastoma, and epilepsy), tracheal and bronchial or lung diseases (chronic obstructive pulmonary disease, asthma, acute lung injury or respiratory distress syndrome, lung cancer, and idiopathic pulmonary fibrosis), and systemic diseases (diabetes and COVID-19). The study of advanced preparations contained nano drug delivery systems of the respiratory route, drug delivery barriers investigation (blood-brain barrier, BBB), and chitosan-based biomaterials for respiratory delivery. These results provided researchers with future research directions related to respiratory delivery.
Antifungal Activity and Biochemical Profiling of Exudates from Germinating Maize Nostrano di Storo Local Variety
Authors: Rosa Stefano , De Benedetti Stefano , Mazzini Stefania , Borgonovo Gigliola , Bona Elisa , Cavaletto Maria , Corsetto Paola Antonia , Ghidoli Martina , Pilu Salvatore Roberto , Scarafoni Alessio
Published: September 2022

Plant pathogens are responsible for important damages to valuable crops causing important economic losses. Agrobiodiversity protection is crucial for the valorization of local varieties that could possess higher resistance to biotic and abiotic stress. At the beginning of germination, seeds are susceptible to pathogens attacks, thus they can release endogenous antimicrobial compounds of different natures in the spermosphere, to contrast proliferation of microorganisms. The work aimed at characterizing the maize of local variety Nostrano di Storo seed exudates secreted during the first phases of germination, to identify compounds active in the defense towards pathogens. Storo seed exudates were proven to inhibit F. verticilloides germination. In order to investigate the cause of the described effect, compositional profiling of the exudates was performed through NMR, lipidomic, and proteomic analyses. This study suggests an important role of microbial endophytic communities in the protection of the seed during the early phases of the germination process and their interplay with fatty acids released by the seeds, rather than a specific antifungal compound. The valorization of agronomically acceptable maize lines with pre-harvest enhanced resistances to pathogens contamination could lead, in the near future, to commercially available varieties potentially requiring more limited chemical protective treatments.
Vascular Plants from the Journey through Portugal (1797–1801) by Hoffmannsegg and Link at the Herbarium of the Real Jardín Botánico of Madrid
Authors: Medina Leopoldo , Aedo Carlos
Published: September 2022

During the journey through Portugal by Hoffmannsegg and Link (1797–1801), these authors collected an appreciable number of specimens, most of which have been lost. Their collections are relevant since they were used by themselves or by other authors to describe numerous species. In the herbarium of the Real Jardín Botánico of Madrid, 70 specimens from this journey have been located. In the archive of this institution the letters that Hoffmannsegg and Link sent to Cavanilles accompanying these plants have also been located. The analysis of these letters, the herbarium labels and of the protologues has permitted to establish that 15 specimens are original material, four of which had already been proposed as lectotypes by other authors (Airochloa caudata Link, Silene fuscata Link ex Brot., Silene micrantha Link ex Otth and Silene pernoctans Link). The designation of a neotype for Stipa gigantea Link should be superseded, because an original material has been found. Thus, a lectotype for this taxon is proposed.
Genome-Wide Identification, Characterization, and Expression Analysis of Glutamate Receptor-like Gene (GLR) Family in Sugarcane
Authors: Zhang Jing , Cui Tianzhen , Su Yachun , Zang Shoujian , Zhao Zhennan , Zhang Chang , Zou Wenhui , Chen Yanling , Cao Yue , Chen Yao , Que Youxiong , Chen Niandong , Luo Jun
Published: September 2022

The plant glutamate receptor-like gene (GLR) plays a vital role in development, signaling pathways, and in its response to environmental stress. However, the GLR gene family has not been comprehensively and systematically studied in sugarcane. In this work, 43 GLR genes, including 34 in Saccharum spontaneum and 9 in the Saccharum hybrid cultivar R570, were identified and characterized, which could be divided into three clades (clade I, II, and III). They had different evolutionary mechanisms, the former was mainly on the WGD/segmental duplication, while the latter mainly on the proximal duplication. Those sugarcane GLR proteins in the same clade had a similar gene structure and motif distribution. For example, 79% of the sugarcane GLR proteins contained all the motifs, which proved the evolutionary stability of the sugarcane GLR gene family. The diverse cis-acting regulatory elements indicated that the sugarcane GLRs may play a role in the growth and development, or under the phytohormonal, biotic, and abiotic stresses. In addition, GO and KEGG analyses predicted their transmembrane transport function. Based on the transcriptome data, the expression of the clade III genes was significantly higher than that of the clade I and clade II. Furthermore, qRT-PCR analysis demonstrated that the expression of the SsGLRs was induced by salicylic acid (SA) treatment, methyl jasmonic acid (MeJA) treatment, and abscisic acid (ABA) treatment, suggesting their involvement in the hormone synthesis and signaling pathway. Taken together, the present study should provide useful information on comparative genomics to improve our understanding of the GLR genes and facilitate further research on their functions.
Arbuscular Mycorrhizal Fungi Mediated Alleviation of Drought Stress via Non-Enzymatic Antioxidants: A Meta-Analysis
Authors: Chandrasekaran Murugesan
Published: September 2022

Drought stress constrains plant cell metabolism and induces the production of reactive oxygen species (ROS). In response to drought stress, plants induce a series of physiological and biochemical changes, scavenging ROS. Among soil microbes, arbuscular mycorrhizal fungi (AMF) are found to be effective ameliorators of ROS under drought-stress conditions. However, the comprehensive roles of the oxidative stress ameliorators mediated by AMF in alleviating drought stress are not studied in detail. The present study aims to determine the oxidative stress ameliorators using meta-analysis highlighting AMF inoculation efficacy on drought stress alleviation. The results confirmed that AMF inoculation had a significant reduction in hydrogen peroxide (H2O2), malondialdehyde (MDA), and electrolyte leakage (EL). Nevertheless, proline accumulation was found to have a non-significant correlation with AMF inoculation. Further, carotenoids and soluble sugars increased positively in AMF-inoculated plants under drought stress and there was a subsequent reduction of abscisic acid (ABA). The results of the meta-analysis reveal the benefits of AMF inoculation with reduced H2O2 levels leading to reduced lipid peroxidation (MDA) and increased membrane stability (EL). Thus, the present assessment reveals the sequence of events involved in eliciting drought stress alleviation due to AMF inoculation.
Synthesis and Performance Evaluation of a Novel Heat and Salt-Resistant Gel Plugging Agent
Authors: Li Xuejiao , Fu Meilong , Liu Jie , Xiao Qi , Tang Wenhao , Yang Guike
Published: September 2022

Tahe oil field is a typical fissure cave carbonate reservoir with a temperature of up to 120~140 °C and a total salinity of formation water of (20~25) × 104 mg/L. In this paper, AM/AMPS was selected as the polymer, 1, 5-dihydroxy naphthol as the cross-linking agent, and polypropylene fiber as the system stabilizer to synthesize a novel gel plugging agent independently; the gel has good thermal stability at a high temperature of 130 °C and increased salinity of 20 × 104 mg/L, and has a dense and relatively stable three-dimensional network structure under a scanning electron microscope. The performance evaluation of the gel plugging agent indicated that: the gel dehydration rate increased gradually with the increase in temperature and salinity, making it suitable for reservoirs with temperatures below 140 °C and formation water salinity below 250,000 mg/L; the viscosity of the gel bulk was 125.3 mPa∙s, the post-gelatinizing viscosity was 42,800 mPa∙s, and the gelatinizing time at 120 °C, 130 °C and 140 °C was 10–20 h, 8–18 h, and 7–16 h, respectively.
Impact of Traces of Hydrogen Sulfide on the Efficiency of Ziegler–Natta Catalyst on the Final Properties of Polypropylene
Authors: Hernández-Fernández Joaquín , Cano Heidi , Aldas Miguel
Published: September 2022

Sulfur compounds are removed from propylene through purification processes. However, these processes are not 100% effective, so low concentrations of compounds such as H2S may be present in polymer-grade propylene. This article studies the effects of H2S content on polypropylene polymerization through the controlled dosage of this compound with concentrations between 0.07 and 5 ppm and its monitoring during the process to determine possible reaction mechanisms and evaluate variations in properties of the material by TGA, FTIR, MFI, and XDR analysis. It was found that the fluidity index increases directly proportional to the concentration of H2S. In addition, the thermo-oxidative degradation is explained by means of the proposed reaction mechanisms of the active center of the Ziegler–Natta catalyst with the H2S molecule and the formation of substances with functional groups such as alcohol, ketones, aldehydes, CO, and CO2 by the oxidation of radical complexes. This study shows for the first time a reaction mechanism between the active center formed for polymerization and H2S, in addition to showing how trace impurities in the raw materials can affect the process, highlighting the importance of optimizing the processes of removal and purification of polymer-grade materials.
Effects of Adding Methods of Fluorane Microcapsules and Shellac Resin Microcapsules on the Preparation and Properties of Bifunctional Waterborne Coatings for Basswood
Authors: Wang Lin , Han Yan , Yan Xiaoxing
Published: September 2022

In this paper, urea-formaldehyde resin microcapsules with shellac resin as core material were prepared by in-situ polymerization. Morphologies of shellac resin microcapsules were characterized by optical microscope (OM) and scanning electron microscope (SEM). Both microcapsules were spherical in shape. The encapsulation property of shellac resin was proved by Fourier transform infrared (FTIR). Shellac resin microcapsules and fluorane microcapsules were added to waterborne primer or topcoat at the same time to prepare waterborne coatings with thermochromic and self-healing dual functions. The effects of microcapsules on optical properties, mechanical properties, self-healing properties, anti-aging performance, and thermoreversible discolouration mechanism of coating films were studied. These results showed that the topcoat with 10.0% fluorane microcapsules and 5.0% shellac resin microcapsules had a better comprehensive performance. At this time, the colour of coating transformed yellow into colourless at 32 °C, and it had a good colour recovery. Shellac resin microcapsules endowed the coating with self-healing performance, and the self-healing rate was 35.9%. The research results provide a reference for the progression of multifunctional wood coatings.
Advances and Challenges in Biopolymer-Based Films
Authors: Roy Swarup , Rhim Jong-Whan
Published: September 2022

Today, biobased polymers derived from sustainable and renewable natural sources are of great interest as an alternative to control the severe damage already caused by petro-chemical-based polymers [...]
Role of Maleic Anhydride-Grafted Poly(lactic acid) in Improving Shape Memory Properties of Thermoresponsive Poly(ethylene glycol) and Poly(lactic acid) Blends
Authors: Nonkrathok Wasana , Trongsatitkul Tatiya , Suppakarn Nitinat
Published: September 2022

Generally, poly(ethylene glycol) (PEG) is added to poly(lactic acid) (PLA) to reduce brittleness and improve mechanical properties. However, shape memory properties of PEG/PLA blends suffered due to the blend’s incompatibility. To enhance shape memory abilities of the blends, 0.45% maleic anhydride-grafted poly(lactic acid) (PLA-g-MA) was used as a compatibilizer. Thermal and mechanical properties, morphologies, microstructures, and shape memory properties of the blends containing different PLA-g-MA contents were investigated. The compatibilized blend with 2 wt% PLA-g-MA exhibited enhanced tensile modulus, strength, and elongation at break, as well as a lower glass transition temperature and degree of crystallinity than the uncompatibilized blend. Results revealed that PLA-g-MA improved interfacial adhesion between phases and promoted chain entanglement. Shape fixity performance of the compatibilized blends were comparable to that of neat PLA. The compatibilized blend containing 2 wt% PLA-g-MA possessed the best shape fixity and recovery performance. Although a high recovery temperature was expected to enhance the recovery of the PEG/PLA blends, the compatibilized blends can be recovered to their original shape at a lower temperature than the PLA. This study illustrated the possibility of optimizing PLA properties to meet requirements necessary for biomedical applications.
Proportional Similarity-Based Openmax Classifier for Open Set Recognition in SAR Images
Authors: Giusti Elisa , Ghio Selenia , Oveis Amir Hosein , Martorella Marco
Published: September 2022

Most of the existing Non-Cooperative Target Recognition (NCTR) systems follow the “closed world” assumption, i.e., they only work with what was previously observed. Nevertheless, the real world is relatively “open” in the sense that the knowledge of the environment is incomplete. Therefore, unknown targets can feed the recognition system at any time while it is operational. Addressing this issue, the Openmax classifier has been recently proposed in the optical domain to make convolutional neural networks (CNN) able to reject unknown targets. There are some fundamental limitations in the Openmax classifier that can end up with two potential errors: (1) rejecting a known target and (2) classifying an unknown target. In this paper, we propose a new classifier to increase the robustness and accuracy. The proposed classifier, which is inspired by the limitations of the Openmax classifier, is based on proportional similarity between the test image and different training classes. We evaluate our method by radar images of man-made targets from the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. Moreover, a more in-depth discussion on the Openmax hyper-parameters and a detailed description of the Openmax functioning are given.
SEAN: A Simple and Efficient Attention Network for Aircraft Detection in SAR Images
Authors: Han Ping , Liao Dayu , Han Binbin , Cheng Zheng
Published: September 2022

Due to the unique imaging mechanism of synthetic aperture radar (SAR), which leads to a discrete state of aircraft targets in images, its detection performance is vulnerable to the influence of complex ground objects. Although existing deep learning detection algorithms show good performance, they generally use a feature pyramid neck design and large backbone network, which reduces the detection efficiency to some extent. To address these problems, we propose a simple and efficient attention network (SEAN) in this paper, which takes YOLOv5s as the baseline. First, we shallow the depth of the backbone network and introduce a structural re-parameterization technique to increase the feature extraction capability of the backbone. Second, the neck architecture is designed by using a residual dilated module (RDM), a low-level semantic enhancement module (LSEM), and a localization attention module (LAM), substantially reducing the number of parameters and computation of the network. The results on the Gaofen-3 aircraft target dataset show that this method achieves 97.7% AP at a speed of 83.3 FPS on a Tesla M60, exceeding YOLOv5s by 1.3% AP and 8.7 FPS with 40.51% of the parameters and 86.25% of the FLOPs.
RFI Detection and Mitigation for Advanced Correlators in Interferometric Radiometers
Authors: Perez-Portero Adrian , Querol Jorge , Camps Adriano , Martin-Neira Manuel , Suess Martin , Ramirez Juan Ignacio , Zurita Alberto , Closa Josep , Oliva Roger , Onrubia Raul
Published: September 2022

This work presents the first RFI detection and mitigation algorithm for the interferometric radiometers that will be implemented in its correlator unit. The algorithm operates in the time and frequency domains, applying polarimetric and statistical tests in both domains, and exhibiting a tunable and arbitrary low probability of false alarm. It is scalable to a configurable number of receivers, and it is optimized in terms of quantization bits and the implementation of the cross-correlations in the time or frequency domains for hardware resource saving. New features of this algorithm are the computation of the Stokes parameters per frequency bin in the Short-Time Fourier Transform and a new parameter called Polarimetric Kurtosis. If RFI is detected in one domain or in both, it is removed using the calculated blanking masks. The optimum algorithm parameters are computed, such as length of the FFTs, the threshold selection for a given probability of false alarm, and the selection of the blanking masks. Last, an important result refers to the application of Parseval’s theorem for the computation of the cross-correlations in the frequency domain, instead of in the time domain, which is more efficient and leads to smaller errors even when using moderate quantization levels. The algorithm has been developed in the framework of the ESA’s technology preparation for a potential L-band radiometer mission beyond SMOS. However, it is also applicable to (polarimetric) real aperture radiometers, and its performance would improve if more than one bit is used in the signal quantization.
Triangle Distance IoU Loss, Attention-Weighted Feature Pyramid Network, and Rotated-SARShip Dataset for Arbitrary-Oriented SAR Ship Detection
Authors: Xu Zhijing , Gao Rui , Huang Kan , Xu Qihui
Published: September 2022

In synthetic aperture radar (SAR) images, ship targets are characterized by varying scales, large aspect ratios, dense arrangements, and arbitrary orientations. Current horizontal and rotation detectors fail to accurately recognize and locate ships due to the limitations of loss function, network structure, and training data. To overcome the challenge, we propose a unified framework combining triangle distance IoU loss (TDIoU loss), an attention-weighted feature pyramid network (AW-FPN), and a Rotated-SARShip dataset (RSSD) for arbitrary-oriented SAR ship detection. First, we propose a TDIoU loss as an effective solution to the loss-metric inconsistency and boundary discontinuity in rotated bounding box regression. Unlike recently released approximate rotational IoU losses, we derive a differentiable rotational IoU algorithm to enable back-propagation of the IoU loss layer, and we design a novel penalty term based on triangle distance to generate a more precise bounding box while accelerating convergence. Secondly, considering the shortage of feature fusion networks in connection pathways and fusion methods, AW-FPN combines multiple skip-scale connections and attention-weighted feature fusion (AWF) mechanism, enabling high-quality semantic interactions and soft feature selections between features of different resolutions and scales. Finally, to address the limitations of existing SAR ship datasets, such as insufficient samples, small image sizes, and improper annotations, we construct a challenging RSSD to facilitate research on rotated ship detection in complex SAR scenes. As a plug-and-play scheme, our TDIoU loss and AW-FPN can be easily embedded into existing rotation detectors with stable performance improvements. Experiments show that our approach achieves 89.18% and 95.16% AP on two SAR image datasets, RSSD and SSDD, respectively, and 90.71% AP on the aerial image dataset, HRSC2016, significantly outperforming the state-of-the-art methods.
Autonomous Trajectory Generation Comparison for De-Orbiting with Multiple Collision Avoidance
Authors: Raigoza Karla , Sands Timothy
Published: September 2022

Over the past four decades, space debris has been identified as a growing hazard for near-Earth space systems. With limited access to space debris tracking databases and only recent policy advancements made to secure a sustainable space environment and mission architecture, this manuscript aims to establish an autonomous trajectory maneuver to de-orbit spacecrafts back to Earth using collision avoidance techniques for the purpose of decommissioning or re-purposing spacecrafts. To mitigate the risk of colliding with another object, the spacecraft attitude slew maneuver requires high levels of precision. Thus, the manuscript compares two autonomous trajectory generations, sinusoidal and Pontragin’s method. In order to determine the Euler angles (roll, pitch, and yaw) necessary for the spacecraft to safely maneuver around space debris, the manuscript incorporates way-point guidance as a collision avoidance approach. When the simulation compiled with both sinusoidal and Pontryagin trajectories, there were differences within the Euler angle spacecraft tracking that could be attributed to the increased fuel efficiency by over five orders of magnitude and lower computation time by over 15 min for that of Pontryagin’s trajectory compared with that of the sinusoidal trajectory. Overall, Pontryagin’s method produced an autonomous trajectory that is more optimal by conserving 37.9% more fuel and saving 40.5% more time than the sinusoidal autonomous trajectory.
A Systematic Literature Review of Predictive Maintenance for Defence Fixed-Wing Aircraft Sustainment and Operations
Authors: Scott Michael J. , Verhagen Wim J. C. , Bieber Marie T. , Marzocca Pier
Published: September 2022

In recent decades, the increased use of sensor technologies, as well as the increase in digitalisation of aircraft sustainment and operations, have enabled capabilities to detect, diagnose, and predict the health of aircraft structures, systems, and components. Predictive maintenance and closely related concepts, such as prognostics and health management (PHM) have attracted increasing attention from a research perspective, encompassing a growing range of original research papers as well as review papers. When considering the latter, several limitations remain, including a lack of research methodology definition, and a lack of review papers on predictive maintenance which focus on military applications within a defence context. This review paper aims to address these gaps by providing a systematic two-stage review of predictive maintenance focused on a defence domain context, with particular focus on the operations and sustainment of fixed-wing defence aircraft. While defence aircraft share similarities with civil aviation platforms, defence aircraft exhibit significant variation in operations and environment and have different performance objectives and constraints. The review utilises a systematic methodology incorporating bibliometric analysis of the considered domain, as well as text processing and clustering of a set of aligned review papers to position the core topics for subsequent discussion. This discussion highlights state-of-the-art applications and associated success factors in predictive maintenance and decision support, followed by an identification of practical and research challenges. The scope is primarily confined to fixed-wing defence aircraft, including legacy and emerging aircraft platforms. It highlights that challenges in predictive maintenance and PHM for researchers and practitioners alike do not necessarily revolve solely on what can be monitored, but also covers how robust decisions can be made with the quality of data available.
Aeroengine Working Condition Recognition Based on MsCNN-BiLSTM
Authors: Zheng Jinsong , Peng Jingbo , Wang Weixuan , Li Shuaiguo
Published: September 2022

Aeroengine working condition recognition is a pivotal step in engine fault diagnosis. Currently, most research on aeroengine condition recognition focuses on the stable condition. To identify the aeroengine working conditions including transition conditions and better achieve the fault diagnosis of engines, a recognition method based on the combination of multi-scale convolutional neural networks (MsCNNs) and bidirectional long short-term memory neural networks (BiLSTM) is proposed. Firstly, the MsCNN is used to extract the multi-scale features from the flight data. Subsequently, the spatial and channel weights are corrected using the weight adaptive correction module. Then, the BiLSTM is used to extract the temporal dependencies in the data. The Focal Loss is used as the loss function to improve the recognition ability of the model for confusable samples. L2 regularization and DropOut strategies are employed to prevent overfitting. Finally, the established model is used to identify the working conditions of an engine sortie, and the recognition results of different models are compared. The overall recognition accuracy of the proposed model reaches over 97%, and the recognition accuracy of transition conditions reaches 94%. The results show that the method based on MsCNN–BiLSTM can effectively identify the aeroengine working conditions including transition conditions accurately.
Sensing with Femtosecond Laser Filamentation
Authors: Qi Pengfei , Qian Wenqi , Guo Lanjun , Xue Jiayun , Zhang Nan , Wang Yuezheng , Zhang Zhi , Zhang Zeliang , Lin Lie , Sun Changlin , Zhu Liguo , Liu Weiwei
Published: September 2022

Femtosecond laser filamentation is a unique nonlinear optical phenomenon when high-power ultrafast laser propagation in all transparent optical media. During filamentation in the atmosphere, the ultrastrong field of 1013–1014 W/cm2 with a large distance ranging from meter to kilometers can effectively ionize, break, and excite the molecules and fragments, resulting in characteristic fingerprint emissions, which provide a great opportunity for investigating strong-field molecules interaction in complicated environments, especially remote sensing. Additionally, the ultrastrong intensity inside the filament can damage almost all the detectors and ignite various intricate higher order nonlinear optical effects. These extreme physical conditions and complicated phenomena make the sensing and controlling of filamentation challenging. This paper mainly focuses on recent research advances in sensing with femtosecond laser filamentation, including fundamental physics, sensing and manipulating methods, typical filament-based sensing techniques and application scenarios, opportunities, and challenges toward the filament-based remote sensing under different complicated conditions.
Joint Estimation for Time Delay and Direction of Arrival in Reconfigurable Intelligent Surface with OFDM
Authors: Du Jinzhi , Cui Weijia , Ba Bin , Jian Chunxiao , Zhang Liye
Published: September 2022

Recently, the joint estimation for time delay (TD) and direction of arrival (DOA) has suffered from the high complexity of processing multi-dimensional signal models and the ineffectiveness of correlated/coherent signals. In order to improve this situation, a joint estimation method using orthogonal frequency division multiplexing (OFDM) and a uniform planar array composed of reconfigurable intelligent surface (RIS) is proposed. First, the time-domain coding function of the RIS is combined with the multi-carrier characteristic of the OFDM signal to construct the coded channel frequency response in tensor form. Then, the coded channel frequency response covariance matrix is decomposed by CANDECOMP/PARAFAC (CPD) to separate the signal subspaces of TD and DOA. Finally, we perform a one-dimensional (1D) spectral search for TD values and a two-dimensional (2D) spectral search for DOA values. Compared to previous efforts, this algorithm not only enhances the adaptability of coherent signals, but also greatly decreases the complexity. Simulation results indicate the robustness and effectiveness for the proposed algorithm in independent, coherent, and mixed multipath environments and low signal-to-noise ratio (SNR) conditions.
Weak and Maneuvering Target Detection with Long Observation Time Based on Segment Fusion for Narrowband Radar
Authors: Wei Shaopeng , Dai Yan , Zhang Qiang
Published: September 2022

Detecting high-speed and maneuvering targets is challenging in early warning radar applications. Modern early warning radar has many functions such as detection, tracking, imaging, and recognition which need a high signal-to-noise ratio (SNR). Thus, long-time coherent integration is a necessary method to realize high SNR requirements. However, high-speed and maneuverable motion cause range and Doppler migration, which brings about serious coherent integration loss. Traditional integration methods usually have the drawbacks of model mismatching and high computational complexity. This paper establishes a novel long coherent processing interval (CPI) integration algorithm that detects maneuvering and weak targets which have a low reflection cross-section (RCS) and low echo SNR. The range and Doppler migration problems are solved via a layer integration by blending the association in a tracking-before-detection (TBD) technique. Compact SNR gain is achieved with a target information transmission mechanism and an updated constant false alarm ratio (CFAR) threshold. The algorithm is applicable in multiple target scenarios by considering different velocity ambiguities and maneuvers. A simulation and real-measured experiments confirm the effectiveness of the algorithm.
Improving the Accuracy of an R-CNN-Based Crack Identification System Using Different Preprocessing Algorithms
Authors: Zhao Mian , Shi Peixin , Xu Xunqian , Xu Xiangyang , Liu Wei , Yang Hao
Published: September 2022

The accurate intelligent identification and detection of road cracks is a key issue in road maintenance, and it has become popular to perform this task through the field of computer vision. In this paper, we proposed a deep learning-based crack detection method that initially uses the idea of image sparse representation and compressed sensing to preprocess the datasets. Only the pixels that represent the crack features remain, while most pixels of non-crack features are relatively sparse, which can significantly improve the accuracy and efficiency of crack identification. The proposed method achieved good results based on the limited datasets of crack images. Various algorithms were tested, namely, linear smooth, median filtering, Gaussian smooth, and grayscale threshold, where the optimal parameters of the various algorithms were analyzed and trained with faster regions with convolutional neural network features (faster R-CNN). The results of the experiments showed that the proposed method has good robustness, with higher detection efficiency in the presence of, for example, road markings, shallow cracks, multiple cracks, and blurring. The result shows that the improvement of mean average precision (mAP) can reach 5% compared with the original method.
Performance Analysis of NB-IoT Uplink in Low Earth Orbit Non-Terrestrial Networks
Authors: Kim Min-Gyu , Jo Han-Shin
Published: September 2022

The 3rd Generation Partnership Project (3GPP) narrowband Internet of Things (NB-IoT) over non-terrestrial networks (NTN) is the most promising candidate technology supporting 5G massive machine-type communication. Compared to geostationary earth orbit, low earth orbit (LEO) satellite communication has the advantage of low propagation loss, but suffers from high Doppler shift. The 3GPP proposes Doppler shift pre-compensation for each beam region of the satellite. However, user equipment farther from the beam center has significant residual Doppler shifts even after pre-compensation, which degrades link performance. This study proposes residual Doppler shift compensation by adding demodulation reference signal symbols and reducing satellite beam coverage. The block error rate (BLER) data are obtained using link-level simulation with the proposed technique. Since the communication time provided by a single LEO satellite moving fast is short, many LEO satellites are necessary for seamless 24-h communication. Therefore, with the BLER data, we analyze the link budget for actual three-dimensional orbits with a maximum of 162 LEO satellites. We finally investigate the effect of the proposed technique on performance metrics such as the per-day total service time and maximum persistent service time, considering the number of satellites and the satellite spacing. The results show that a more prolonged and continuous communication service is possible with significantly fewer satellites using the proposed technique.
Electric Vehicle Charging Station Location-Routing Problem with Time Windows and Resource Sharing
Authors: Wang Yong , Zhou Jingxin , Sun Yaoyao , Wang Xiuwen , Zhe Jiayi , Wang Haizhong
Published: September 2022

Electric vehicles (EVs) are widely applied in logistics companies’ urban logistics distribution, as fuel prices increase and environmental awareness grows. This study introduces an EV charging station (CS) location-routing problem with time windows and resource sharing (EVCS-LRPTWRS). Resource sharing, among multiple depots within multiple service periods is proposed to adjust the transportation resource configuration for a sustainable logistics development. Solving the EVCS-LRPTWRS involves a periodic CS location selection and a multi-depot multi-period EV routing optimization. A bi-objective nonlinear programming model is proposed to formulate the EVCS-LRPTWRS with a minimum total operating cost and number of EVs. A hybrid algorithm combining the Gaussian mixture clustering algorithm (GMCA) with the improved nondominated sorting genetic algorithm-II (INSGA-II) is designed to address the EVCS-LRPTWRS. The GMCA is employed to assign customers to appropriate depots in various service periods in order to reduce the computational complexity. The INSGA-II is adopted to obtain the Pareto optimal solutions by using the CS insertion operation to select CS locations and integrating the elite retention mechanism to ensure a stable and excellent performance. The superiority of the hybrid algorithm is proven by comparison with the other three algorithms (i.e., multi-objective genetic algorithm, multi-objective particle swarm optimization, and multi-objective ant colony optimization). An empirical study of the EVCS-LRPTWRS in Chongqing City, China is conducted. Then, four types of service period divisions and three scenarios of resource sharing modes are further analyzed and discussed. The empirical results demonstrate the validity and practicability of the proposed solution method in realizing a sustainable operation in EV distribution networks.
Power to the Learner: Towards Human-Intuitive and Integrative Recommendations with Open Educational Resources
Authors: Bulathwela Sahan , Pérez-Ortiz María , Yilmaz Emine , Shawe-Taylor John
Published: September 2022

Educational recommenders have received much less attention in comparison with e-commerce- and entertainment-related recommenders, even though efficient intelligent tutors could have potential to improve learning gains and enable advances in education that are essential to achieving the world’s sustainability agenda. Through this work, we make foundational advances towards building a state-aware, integrative educational recommender. The proposed recommender accounts for the learners’ interests and knowledge at the same time as content novelty and popularity, with the end goal of improving predictions of learner engagement in a lifelong-learning educational video platform. Towards achieving this goal, we (i) formulate and evaluate multiple probabilistic graphical models to capture learner interest; (ii) identify and experiment with multiple probabilistic and ensemble approaches to combine interest, novelty, and knowledge representations together; and (iii) identify and experiment with different hybrid recommender approaches to fuse population-based engagement prediction to address the cold-start problem, i.e., the scarcity of data in the early stages of a user session, a common challenge in recommendation systems. Our experiments with an in-the-wild interaction dataset of more than 20,000 learners show clear performance advantages by integrating content popularity, learner interest, novelty, and knowledge aspects in an informational recommender system, while preserving scalability. Our recommendation system integrates a human-intuitive representation at its core, and we argue that this transparency will prove important in efforts to give agency to the learner in interacting, collaborating, and governing their own educational algorithms.
Energy Analysis and Verification of a Constant-Pressure Elastic-Strain Energy Accumulator Based on Exergy Method
Authors: Du Hongwang , Bian Xin , Xiong Wei
Published: September 2022

Focusing on the low energy-storage efficiency and unstable energy output of existing accumulators, this paper proposes a novel constant-pressure elastic-strain energy accumulator based on the rubber material hyperelastic effect. The proposed accumulator can store and release energy at a constant pressure. Based on the exergy analysis method, the charging/discharging energy storage efficiency of a constant-pressure elastic-strain energy accumulator was analyzed. Then, the Mullins effect on the rubber airbag over multiple charging/discharging cycles was studied. Finally, a test platform was established to verify the energy storage efficiency, as well as the expansion and contraction pressure stability of the rubber accumulator during charging/discharging cycles. The experimental results showed that the energy storage efficiency calculation by the exergy analysis method was more accurate compared with the enthalpy analysis method. In tests with more than 200 cycles, the rubber airbag energy storage efficiency was always higher than 76%, and the expansion and contraction pressure errors at a steady state were less than 2.92 and 1.79 kPa, respectively. The results showed that the rubber airbag could be used as an effective energy storage component, which is very meaningful for energy recovery in pneumatic or hydraulic systems.
Building Health and Wellness Service Experience Extension: A Case Study of Bangkok, Thailand
Authors: Meeprom Supawat , Chancharat Surachai
Published: September 2022

This study examines the relationship between customer engagement, customer evaluation of service offerings, trust in health and wellness services, quality of life, and experience extension based on usable data obtained from 360 international customers via a self-administration approach to health and wellness service providers in Thailand. A two-step approach was employed to evaluate the relationship between the proposed relationship. AMOS software was used. The results found that customer engagement affected service evaluation of health and wellness providers and customers’ quality of life, but customer engagement had no significance in predicting trust and experience extension. Furthermore, the results revealed that service evaluation increased trust in the health and wellness services, quality of life, and experience extension. In addition, customers’ quality of life affected experience extension, but trust had no significant impact on experience extension. The mediating role of trust and quality of life mediates the relationship between customer engagement and experience extension. These findings can provide a deeper understanding of customer engagement and service evaluation of health and wellness for increasing customer experience extension.