bluuebunny/crossref_metadata_2025 huggingface dataset:
Intercropping Cover Crops for a Vital Ecosystem Service: A Review of the Biocontrol of Insect Pests in Tea Agroecosystems
Authors: Pokharel Sabin Saurav , Yu Han , Fang Wanping , Parajulee Megha N. , Chen Fajun
Published: June 2023

The intercropping of cover crops has been adopted in several agroecosystems, including tea agroecosystems, which promotes ecological intensification. Prior studies have shown that growing cover crops in tea plantations provided different ecological services, including the biocontrol of pests. Cover crops enrich soil nutrients, reduce soil erosion, suppress weeds and insect pests, and increase the abundance of natural enemies (predators and parasitoids). We have reviewed the potential cover crops that can be incorporated into the tea agroecosystem, particularly emphasizing the ecological services of cover crops in pest control. Cover crops were categorized into cereals (buckwheat, sorghum), legumes (guar, cowpea, tephrosia, hairy indigo, and sunn hemp), aromatic plants (lavender, marigold, basil, and semen cassiae), and others (maize, mountain pepper, white clover, round-leaf cassia, and creeping indigo). Legumes and aromatic plants are the most potent cover crop species that can be intercropped in monoculture tea plantations due to their exceptional benefits. These cover crop species improve crop diversity and help with atmospheric nitrogen fixation, including with the emission of functional plant volatiles, which enhances the diversity and abundance of natural enemies, thereby assisting in the biocontrol of tea insect pests. The vital ecological services rendered by cover crops to monoculture tea plantations, including regarding the prevalent natural enemies and their pivotal role in the biocontrol of insect pests in the tea plantation, have also been reviewed. Climate-resilient crops (sorghum, cowpea) and volatile blends emitting aromatic plants (semen cassiae, marigold, flemingia) are recommended as cover crops that can be intercropped in tea plantations. These recommended cover crop species attract diverse natural enemies and suppress major tea pests (tea green leaf hopper, white flies, tea aphids, and mirid bugs). It is presumed that the incorporation of cover crops within the rows of tea plantations will be a promising strategy for mitigating pest attacks via the conservation biological control, thereby increasing tea yield and conserving agrobiodiversity. Furthermore, a cropping system with intercropped cover crop species would be environmentally benign and offer the opportunity to increase natural enemy abundance, delaying pest colonization and/or preventing pest outbreaks for pest management sustainability.
Aroma Compounds in Essential Oils: Analyzing Chemical Composition Using Two-Dimensional Gas Chromatography–High Resolution Time-of-Flight Mass Spectrometry Combined with Chemometrics
Authors: Koljančić Nemanja , Vyviurska Olga , Špánik Ivan
Published: June 2023

Analyzing essential oils is a challenging task for chemists because their composition can vary depending on various factors. The separation potential of volatile compounds using enantioselective two-dimensional gas chromatography coupled with high-resolution time-of-flight mass spectrometry (GC×GC–HRTOF-MS) with three different stationary phases in the first dimension was evaluated to classify different types of rose essential oils. The results showed that selecting only ten specific compounds was enough for efficient sample classification instead of the initial 100 compounds. The study also investigated the separation efficiencies of three stationary phases in the first dimension: Chirasil-Dex, MEGA-DEX DET—β, and Rt-βDEXsp. Chirasil-Dex had the largest separation factor and separation space, ranging from 47.35% to 56.38%, while Rt-βDEXsp had the smallest, ranging from 23.36% to 26.21%. MEGA-DEX DET—β and Chirasil-Dex allowed group-type separation based on factors such as polarity, H-bonding ability, and polarizability, whereas group-type separation with Rt-βDEXsp was almost imperceptible. The modulation period was 6 s with Chirasil-Dex and 8 s with the other two set-ups. Overall, the study showed that analyzing essential oils using GC×GC–HRTOF-MS with a specific selection of compounds and stationary phase can be effective in classifying different oil types.
Effect of Glutamic Acid and 6-benzylaminopurine on Flower Bud Biostimulation, Fruit Quality and Antioxidant Activity in Blueberry
Authors: Pérez-León María Itzel , González-Fuentes José Antonio , Valdez-Aguilar Luis Alonso , Benavides-Mendoza Adalberto , Alvarado-Camarillo Daniela , Castillo-Chacón Carlos Estuardo
Published: June 2023

Blueberry is a highly demanded and consumed fruit due to its beneficial effects on human health, because of its bioactive compounds with a high antioxidant capacity. The interest in increasing the yield and quality of blueberries has led to the application of some innovative techniques such as biostimulation. The objective of this research was to assess the effect of the exogenous application of glutamic acid (GLU) and 6-benzylaminopurine (6-BAP) as biostimulants on flower bud sprouting, fruit quality, and antioxidant compounds in blueberry cv. Biloxi. The application of GLU and 6-BAP positively affected bud sprouting, fruit quality, and antioxidant content. The application of 500 and 10 mg L−1 GLU and 6-BAP, respectively, increased the number of flower buds, while 500 and 20 mg L−1 generated fruits with higher content of flavonoids, vitamin C, and anthocyanins and higher enzymatic activity of catalase and ascorbate peroxidase enzymes. Hence, the application of these biostimulants is an effective way to enhance the yield and fruit quality of blueberries.
Development, Validation, and Application of Reverse Transcription Real-Time and Droplet Digital PCR Assays for the Detection of the Potyviruses Watermelon Mosaic Virus and Zucchini Yellow Mosaic Virus in Cucurbits
Authors: Luigi Marta , Manglli Ariana , Corrado Carla Libia , Tiberini Antonio , Costantini Elisa , Ferretti Luca , Tomassoli Laura , Bertin Sabrina
Published: June 2023

Among the cucurbit-infecting viruses, watermelon mosaic virus (WMV) and zucchini yellow mosaic virus (ZYMV) (Potyvirus: Potyviridae) are responsible for severe symptoms on cucumber, melon, watermelon, and zucchini cultivations worldwide. In this study, reverse transcription real-time PCR (real-time RT-PCR) and droplet-digital PCR (RT-ddPCR) assays targeting the coat protein (CP) genes of WMV and ZYMV were developed and validated according to the international standards of plant pest diagnosis (EPPO PM 7/98 (5)). First, the diagnostic performance of WMV-CP and ZYMV-CP real-time RT-PCRs was evaluated, and the assays displayed an analytical sensitivity of 10−5 and 10−3, respectively. The tests also showed an optimal repeatability, reproducibility and analytical specificity, and were reliable for the virus detection in naturally infected samples and across a wide range of cucurbit hosts. Based on these results, the real-time RT-PCR reactions were adapted to set up RT-ddPCR assays. These were the first RT-ddPCR assays aiming at the detection and quantification of WMV and ZYMV and showed a high sensitivity, being able to detect until 9 and 8 copies/µL of WMV or ZYMV, respectively. The RT-ddPCRs allowed the direct estimation of the virus concentrations and opened to a broad range of applications in disease management, such as the evaluation of partial resistance in breeding processes, identification of antagonistic/synergistic events, and studies on the implementation of natural compounds in the integrated management strategies.
CRISPR/Cas9 Based Cell-Type Specific Gene Knock-Out in Arabidopsis Roots
Authors: Li Meng , Niu Xufang , Li Shuang , Fu Shasha , Li Qianfang , Xu Meizhi , Wang Chunhua , Wu Shuang
Published: June 2023

CRISPR/Cas9 (hereafter Cas9)-mediated gene knockout is one of the most important tools for studying gene function. However, many genes in plants play distinct roles in different cell types. Engineering the currently used Cas9 system to achieve cell-type-specific knockout of functional genes is useful for addressing the cell-specific functions of genes. Here we employed the cell-specific promoters of the WUSCHEL RELATED HOMEOBOX 5 (WOX5), CYCLIND6;1 (CYCD6;1), and ENDODERMIS7 (EN7) genes to drive the Cas9 element, allowing tissue-specific targeting of the genes of interest. We designed the reporters to verify the tissue-specific gene knockout in vivo. Our observation of the developmental phenotypes provides strong evidence for the involvement of SCARECROW (SCR) and GIBBERELLIC ACID INSENSITIVE (GAI) in the development of quiescent center (QC) and endodermal cells. This system overcomes the limitations of traditional plant mutagenesis techniques, which often result in embryonic lethality or pleiotropic phenotypes. By allowing cell-type-specific manipulation, this system has great potential to help us better understand the spatiotemporal functions of genes during plant development.
Haplotype-Based Genome-Wide Association Analysis Using Exome Capture Assay and Digital Phenotyping Identifies Genetic Loci Underlying Salt Tolerance Mechanisms in Wheat
Authors: Pasam Raj K. , Kant Surya , Thoday-Kennedy Emily , Dimech Adam , Joshi Sameer , Keeble-Gagnere Gabriel , Forrest Kerrie , Tibbits Josquin , Hayden Matthew
Published: June 2023

Soil salinity can impose substantial stress on plant growth and cause significant yield losses. Crop varieties tolerant to salinity stress are needed to sustain yields in saline soils. This requires effective genotyping and phenotyping of germplasm pools to identify novel genes and QTL conferring salt tolerance that can be utilised in crop breeding schemes. We investigated a globally diverse collection of 580 wheat accessions for their growth response to salinity using automated digital phenotyping performed under controlled environmental conditions. The results show that digitally collected plant traits, including digital shoot growth rate and digital senescence rate, can be used as proxy traits for selecting salinity-tolerant accessions. A haplotype-based genome-wide association study was conducted using 58,502 linkage disequilibrium-based haplotype blocks derived from 883,300 genome-wide SNPs and identified 95 QTL for salinity tolerance component traits, of which 54 were novel and 41 overlapped with previously reported QTL. Gene ontology analysis identified a suite of candidate genes for salinity tolerance, some of which are already known to play a role in stress tolerance in other plant species. This study identified wheat accessions that utilise different tolerance mechanisms and which can be used in future studies to investigate the genetic and genic basis of salinity tolerance. Our results suggest salinity tolerance has not arisen from or been bred into accessions from specific regions or groups. Rather, they suggest salinity tolerance is widespread, with small-effect genetic variants contributing to different levels of tolerance in diverse, locally adapted germplasm.
The Use of Essential Oil Embedded in Polylactic Acid/Chitosan-Based Film for Mango Post-Harvest Application against Pathogenic Fungi
Authors: Gunny Ahmad Anas Nagoor , Leem Siew Juan , Makhtar Muaz Mohd Zaini , Zainuddin Nor’Izzah , Mohd Roslim Muhammad Huzaifah , Raja Hashim Raja Hasnida , Pusphanathan Kavita , Siddiqui Masoom Raza , Alam Mahboob , Rafatullah Mohd
Published: June 2023

Mango has a high global demand. Fruit fungal disease causes post-harvest mango and fruit losses. Conventional chemical fungicides and plastic prevent fungal diseases but they are hazardous to humans and the environment. Direct application of essential oil for post-harvest fruit control is not a cost-effective approach. The current work offers an eco-friendly alternative to controlling the post-harvest disease of fruit using a film amalgamated with oil derived from Melaleuca alternifolia. Further, this research also aimed to assess the mechanical, antioxidant, and antifungal properties of the film infused with essential oil. ASTM D882 was performed to determine the tensile strength of the film. The antioxidant reaction of the film was assessed using the DPPH assay. In vitro and in vivo tests were used to evaluate the inhibitory development of the film against pathogenic fungi, by comparing the film with different levels of essential oil together with the treatment of the control and chemical fungicide. Disk diffusion was used to evaluate mycelial growth inhibition, where the film incorporated with 1.2 wt% essential oil yielded the best results. For in vivo testing of wounded mango, the disease incidence was successfully reduced. For in vivo testing of unwounded mango to which the film incorporated with essential oil was applied, although some quality parameters such as the color index were not significantly affected, weight loss was reduced, soluble solid content was increased, and firmness was increased, compared to the control. Thus, the film incorporated with essential oil (EO) from M. alternifolia can be an environmentally friendly alternative to the conventional approach and the direct application of essential oil to control post-harvest disease in mango.
Crosslinked Chitosan Films Supplemented with Randia sp. Fruit Extract
Authors: López-Saucedo Felipe , Buendía-González Leticia , Magaña Héctor , Flores-Rojas Guadalupe Gabriel , Bucio Emilio
Published: June 2023

This work proposes the development of a polymer film made up of affordable components for its use as a healthcare material. Chitosan, itaconic acid, and Randia capitata fruit extract (Mexican variation) are the unique ingredients of this biomaterial prospect. Chitosan (from crustacean chitin) is crosslinked with itaconic acid, and in situ added R. capitata fruit extract in a one-pot reaction carried out in water as the sole solvent. Structurally, the film formed is an ionically crosslinked composite characterized by IR spectroscopy and thermal analysis (DSC and TGA); cell viability was also performed in vitro using fibroblasts BALB/3T3. Dry and swollen films were analyzed to determine affinity and stability in water. This chitosan-based hydrogel is designed as a wound dressing due to the combined properties of the chitosan with R. capitata fruit extract, which has potential as bioactive material due to its properties in epithelial regeneration.
Fabrication of Transparent PEGylated Antifouling Coatings via One-Step Pyrogallol Deposition
Authors: Yeh Shang-Lin , Deval Piyush , Tsai Wei-Bor
Published: June 2023

Antifouling coatings are critical for many biomedical devices. A simple and universal technique used to anchor antifouling polymers is important in order to expand its applications. In this study, we introduced the pyrogallol (PG)-assisted immobilization of poly(ethylene glycol) (PEG) to deposit a thin antifouling layer on biomaterials. Briefly, biomaterials were soaked in a PG/PEG solution and PEG was immobilized onto the biomaterial surfaces via PG polymerization and deposition. The kinetics of PG/PEG deposition started with the deposition of PG on the substrates, followed by the addition of a PEG-rich adlayer. However, prolonged coating added a top-most PG-rich layer, which deteriorated the antifouling efficacy. By controlling the amounts of PG and PEG and the coating time, the PG/PEG coating was able to reduce more than 99% of the adhesion of L929 cells and the adsorption of fibrinogen. The ultrathin (tens of nanometers) and smooth PG/PEG coating was easily deposited onto a wide variety of biomaterials, and the deposition was robust enough to survive harsh sterilization conditions. Furthermore, the coating was highly transparent and allowed most of the UV and Vis light to pass through. The technique has great potential to be applied to biomedical devices that need a transparent antifouling coating, such as intraocular lenses and biosensors.
Assessment of Biosecurity Practices and Its Status in Small- and Medium-Scale Commercial Poultry Farms in Arsi and East Showa Zones, Oromia, Ethiopia
Authors: Tsegaye Dereje , Tamir Berhan , Gebru Getachew
Published: June 2023

Disease prevalence and seasonal outbreaks are challenging the poultry industry in Ethiopia. Proper and sustainable implementation of biosecurity practices is important to reverse such problems. This study was conducted in commercial poultry farms in two zones of Ethiopia to investigate farm characteristics, implementation of biosecurity practices, and biosecurity status (BS) using a structured questionnaire. The variables were grouped into three biosecurity factors, including conceptual, structural, and operational biosecurity, based on their homogeneity. Descriptive and inferential statistics were used to summarize the results. Most commercial farms were owned by males (69.7%). The majority of the farms (40.3%) were located at a distance <50 m from residential areas. Farm owners do not provide biosecurity training to their employees (68.8%), which results in poor biosecurity implementation. The mean conceptual, structural, and operational BS were 50.4 ± 11.62, 63.27 ± 10.51, and 44.69 ± 13.04, respectively, indicating operational biosecurity measurements were less implemented. Overall, the BS indicated that 40.7% of the farms have BS < 50% questing for interventions. Farm characteristics and biosecurity measurements were positively associated with BS, which shows substantial room for improvement. Owners’ education, occupation, experience, farm flock size, and training were significantly associated with BS (p < 0.05). A disease prevention strategy through biosecurity improvement is an economical means for controlling poultry disease prevalence.
Development of Tea Tree Oil Based Nanoemulgel Loaded with Azithromycin for Enhancing the Antibacterial Activity
Authors: Khalifa Nasrin E. , Abdallah Marwa H. , Elghamry Hanaa A. , Khojali Weam M. A. , Khafagy El-Sayed , El-Sayed El-Horany Hemat , Shawky Seham
Published: June 2023

Azithromycin (AZ) is an azalide macrolide antibiotic that is frequently employed for treating bacterial skin infections. It suffers from limited oral bioavailability, which results from incomplete absorption or extensive first-pass metabolism. Therefore, preparing azithromycin formulations for topical administration is highly recommended to avoid first-pass metabolism and to boost the concentration of the drug on the skin. The objective of our investigation was to formulate and evaluate the efficacy of AZ-loaded nanoemulgel as an antimicrobial drug. The physical appearance, spreadability, viscosity, particle size, in vitro drug release, ex vivo permeation investigations, and antimicrobial efficiency of the prepared formulations were evaluated. The prepared formulation loaded with AZ exhibited good physical quality. AZ-loaded nanoemulgel had a greater ex vivo drug permeation across rabbit skin than other formulations (AZ-loaded gel and AZ-loaded emulgel), revealing improved drug permeation and greater transdermal flux in addition to enhanced antibacterial efficacy (p < 0.05). Overall, our findings imply that tea-tree-oil-based nanoemulgel would be a promising delivery system for enhancing the antimicrobial efficiency of azithromycin.
Numerical Simulation of Wormhole Propagation with Foamed-Viscoelastic-Surfactant Acid in Carbonate Acidizing
Authors: Zhang Lufeng , Wang Haibo , Zhou Fujian , Mou Jianye
Published: June 2023

Successful matrix acidizing for extremely thick carbonate reservoirs with long horizontal well sections and strong heterogeneity requires efficient temporary plugging and diverting of acid fluid, ensuring acid fluid distribution to each production layer. Foamed-viscoelastic-surfactant (Foamed-VES) acid combines the benefits of both foam acid and viscoelastic surfactant (VES) acid, integrating foam plugging and viscous plugging. It can achieve uniform acid distribution in highly heterogeneous reservoirs. However, little research has been conducted on the wormhole propagation law of foamed-VES acid. To address this gap, this study established a mathematical model of foamed-VES acid wormhole propagation based on the dual-scale model. The model was coupled with a random porosity distribution generated with geological statistical software. The effects of different factors on foamed-VES acid etching were simulated. Numerical results show that foamed-VES acid can stimulate low-permeability reservoirs with a permeability differential of 20. Its inherent mechanism lies in the synergy of foam plugging and VES viscous plugging. This study enhances our understanding of the acid diversion mechanism of foamed-VES acid, providing a theoretical foundation for on-site acidizing treatment.
Mercury Prediction in Urban Soils by Remote Sensing and Relief Data Using Machine Learning Techniques
Authors: Suleymanov Azamat , Suleymanov Ruslan , Kulagin Andrey , Yurkevich Marija
Published: June 2023

This article aims to explore the use of machine learning (ML) methods for mapping the distribution of mercury (Hg) content in topsoil, using the city of Ufa (Russia) and adjacent areas as an example. For this purpose, a soil dataset of 250 points sampled from a 0–20 cm depth on different land uses, including residential, industrial and undisturbed (forests and parks), was used. Random Forest (RF), Extreme Gradient Boosting (XGboost), Cubist and k-Nearest Neighbor (kNN) ML techniques were employed to model and map the Hg concentrations. We used remote sensing data (RSD) and topographic attributes as explanatory variables. ML models were calibrated and validated using the leave-one-out cross-validation approach. The Hg content varied from 0.005 to 0.58 mg/kg and was characterized by very high variability. According to the MAE and RMSE metrics, the RF method resulted in the most accurate spatial prediction for the Hg content (0.029 and 0.065 mg/kg, respectively), while the XGBoost approach showed the lowest prediction efficiency (0.032 and 0.073 mg/kg, respectively). The results showed that the slope map, spectral index MSI and Sentinel-2A band B11 were the key variables in explaining the variability of Hg content. We found that higher uncertainty values of soil Hg were found in croplands, urban residential and industrial areas, which supports the view that spatial modelling of HM in urban landscapes is challenging. The present study provides insights into the potential of digital soil mapping techniques in combination with RSD and terrain variables for identifying areas at risk of Hg contamination in urban areas, which can inform land-use planning and management strategies to protect human health and the environment.
Refocusing Swing Ships in SAR Imagery Based on Spatial-Variant Defocusing Property
Authors: Wang Jin , Leng Xiangguang , Sun Zhongzhen , Zhang Xi , Ji Kefeng
Published: June 2023

Synthetic aperture radar (SAR) is an essential tool for maritime surveillance in all weather conditions and at night. Ships are often affected by sea breezes and waves, generating a three-dimensional (3D) swinging motion. The 3D swing ship can thereby become severely defocused in SAR images, making it extremely difficult to recognize them. However, refocusing 3D swing ships in SAR imagery is challenging with traditional approaches due to different phase errors at each scattering point on the ship. In order to solve this problem, a novel method for refocusing swing ships in SAR imagery based on the spatial-variant defocusing property is proposed in this paper. Firstly, the spatial-variant defocusing property of a 3D swing ship is derived according to the SAR imaging mechanism. Secondly, considering the spatial-variant defocusing property, each azimuth line of the SAR 3D swing ship image is modeled as a multi-component linear frequency modulation (MC-LFM) signal. Thirdly, Fractional Autocorrelation (FrAc) is implemented in order to quickly calculate the optimal rotation order set for each azimuth line. Thereafter, Fractional Fourier Transform (FrFT) is performed on the azimuth lines to refocus their linear frequency modulation (LFM) components one by one. Finally, the original azimuth lines are replaced in the SAR image with their focused signals to generate the refocused SAR image. The experimental results from a large amount of simulated data and real Gaofen-3 data show that the proposed algorithm can overcome the spatial-variant defocusing of 3D swing ships. Compared with state-of-the-art algorithms, our approach reduces the image entropy by an order of magnitude, leading to a visible improvement in image quality, which makes it possible to recognize swing ships in SAR images.
Static High Target-Induced False Alarm Suppression in Circular Synthetic Aperture Radar Moving Target Detection Based on Trajectory Features
Authors: Shen Wenjie , Ding Fan , Wang Yanping , Li Yang , Sun Jinping , Lin Yun , Jiang Wen , Wang Shuo
Published: June 2023

The new mode of Circular Synthetic Aperture Radar (CSAR) has several advantages including multi-aspect and long-time observation, which can generate high-frame-rate image sequences to detect moving targets with a single-channel system. Nonetheless, due to CSAR being sensitive to 3D structures, static high targets are observed in scene display rotational motion within CSAR subaperture image sequences. Such motion can cause false alarms rising when utilizing image sequence-based moving target detection methods like logarithm background subtraction (LBS). To address this issue, this paper first thoroughly analyzes the moving target and static high target’s difference for the trajectory in an image sequence. Two new trajectory features of the rotation angle and moving distance are proposed to differentiate them. Based on the features, a new false alarm suppression method is proposed. The method first utilizes LBS to obtain coarse binary detection results comprising both moving and static high targets, then employs morphological filtering to eliminate noise. Next, DBSCAN and target tracking steps are employed to extract the trajectory features of the target and false alarm. Finally, false alarms are suppressed with trajectory-based feature discriminators to output detection results. The W-band CSAR open dataset is used to validate the proposed method’s effectiveness.
Monitoring Forest Cover Dynamics Using Orthophotos and Satellite Imagery
Authors: Blaga Lucian , Ilieș Dorina Camelia , Wendt Jan A. , Rus Ioan , Zhu Kai , Dávid Lóránt Dénes
Published: June 2023

The assessment of changes in forest coverage is crucial for managing protected forest areas, particularly in the face of climate change. This study monitored forest cover dynamics in a 6535 ha mountain area located in north-west Romania as part of the Apuseni Natural Park from 2003 to 2019. Two approaches were used: vectorization from orthophotos and Google Earth images (in 2003, 2005, 2009, 2012, 2014, 2016, 2017, and 2019) and satellite imagery (Landsat 5 TM, 7 ETM, and 8 OLI) pre-processed to Surface Reflectance (SR) format from the same years. We employed four standard classifiers: Support Vector Machine (SVM), Random Forest (RF), Maximum Likelihood Classification (MLC), Spectral Angle Mapper (SAM), and three combined methods: Linear Spectral Unmixing (LSU) with Natural Breaks (NB), Otsu Method (OM) and SVM, to extract and classify forest areas. Our study had two objectives: 1) to accurately assess changes in forest cover over a 17-year period and 2) to determine the most efficient methods for extracting and classifying forest areas. We validated the results using performance metrics that quantify both thematic and spatial accuracy. Our results indicate a 9% loss of forest cover in the study area, representing 577 ha with an average decrease ratio of 33.9 ha/year−1. Of all the methods used, SVM produced the best results (with an average score of 88% for Overall Quality (OQ)), followed by RF (with a mean value of 86% for OQ).
An Intelligent Detection Method for Small and Weak Objects in Space
Authors: Yuan Yuman , Bai Hongyang , Wu Panfeng , Guo Hongwei , Deng Tianyu , Qin Weiwei
Published: June 2023

In the case of a boom in space resource development, space debris will increase dramatically and cause serious problems for the spacecraft in orbit. To address this problem, a novel context sensing-YOLOv5 (CS-YOLOv5) is proposed for small and weak space object detection, which could realize the extraction of local context information and the enhancement and fusion of spatial information. To enhance the expression ability of feature information and the identification ability of the network, we propose the cross-layer context fusion module (CCFM) through multiple branches in parallel to learn the context information of different scales. At the same time, to map the small-scale features sequentially to the features of the previous layer, we design the adaptive weighting module (AWM) to assist the CCFM in further enhancing the expression of features. Additionally, to solve the problem that the spatial information of small objects is easily lost, we designed the spatial information enhancement module (SIEM) to adaptively learn the weak spatial information of small objects that need to be protected. To further enhance the generalization ability of CS-YOLOv5, we propose a contrast mosaic data augmentation to enrich the diversity of the sample. Extensive experiments are conducted on self-built datasets, which strongly prove the effectiveness of our method in space object detection.
The Distribution of Surface Soil Moisture over Space and Time in Eastern Taylor Valley, Antarctica
Authors: Salvatore Mark R. , Barrett John E. , Fackrell Laura E. , Sokol Eric R. , Levy Joseph S. , Kuentz Lily C. , Gooseff Michael N. , Adams Byron J. , Power Sarah N. , Knightly J. Paul , Matul Haley M. , Szutu Brian , Doran Peter T.
Published: June 2023

Available soil moisture is thought to be the limiting factor for most ecosystem processes in the cold polar desert of the McMurdo Dry Valleys (MDVs) of Antarctica. Previous studies have shown that microfauna throughout the MDVs are capable of biological activity when sufficient soil moisture is available (~2–10% gravimetric water content), but few studies have attempted to quantify the distribution, abundance, and frequency of soil moisture on scales beyond that of traditional field work or local field investigations. In this study, we present our work to quantify the soil moisture content of soils throughout the Fryxell basin using multispectral satellite remote sensing techniques. Our efforts demonstrate that ecologically relevant abundances of liquid water are common across the landscape throughout the austral summer. On average, the Fryxell basin of Taylor Valley is modeled as containing 1.5 ± 0.5% gravimetric water content (GWC) across its non-fluvial landscape with ~23% of the landscape experiencing an average GWC > 2% throughout the study period, which is the observed limit of soil nematode activity. These results indicate that liquid water in the soils of the MDVs may be more abundant than previously thought, and that the distribution and availability of liquid water is dependent on both soil properties and the distribution of water sources. These results can also help to identify ecological hotspots in the harsh polar Antarctic environment and serve as a baseline for detecting future changes in the soil hydrological regime.
CGC-Net: A Context-Guided Constrained Network for Remote-Sensing Image Super Resolution
Authors: Zheng Pengcheng , Jiang Jianan , Zhang Yan , Zeng Chengxiao , Qin Chuanchuan , Li Zhenghao
Published: June 2023

In remote-sensing image processing tasks, images with higher resolution always result in better performance on downstream tasks, such as scene classification and object segmentation. However, objects in remote-sensing images often have low resolution and complex textures due to the imaging environment. Therefore, effectively reconstructing high-resolution remote-sensing images remains challenging. To address this concern, we investigate embedding context information and object priors from remote-sensing images into current deep learning super-resolution models. Hence, this paper proposes a novel remote-sensing image super-resolution method called Context-Guided Constrained Network (CGC-Net). In CGC-Net, we first design a simple but effective method to generate inverse distance maps from the remote-sensing image segmentation maps as prior information. Combined with prior information, we propose a Global Context-Constrained Layer (GCCL) to extract high-quality features with global context constraints. Furthermore, we introduce a Guided Local Feature Enhancement Block (GLFE) to enhance the local texture context via a learnable guided filter. Additionally, we design a High-Frequency Consistency Loss (HFC Loss) to ensure gradient consistency between the reconstructed image (HR) and the original high-quality image (HQ). Unlike existing remote-sensing image super-resolution methods, the proposed CGC-Net achieves superior visual results and reports new state-of-the-art (SOTA) performance on three popular remote-sensing image datasets, demonstrating its effectiveness in remote-sensing image super-resolution (RSI-SR) tasks.
Vegetation Types Variations to the South of Ngoring Lake from 2013 to 2020, Analyzed by Hyperspectral Imaging
Authors: Liu Xiaole , Wang Guangjun , Shi Yu , Liang Sihai , Jia Jinzhang
Published: June 2023

Studying the variation in vegetation types within the source region of the Yellow River (SRYR) is of great significance for understanding the response of vegetation to climate change and human activities on the Qinghai-Tibet Plateau (QTP) permafrost. In order to understand the characteristics of the variation in vegetation associations in the SRYR under the influence of climate and human activities, two hyperspectral remote sensing images from HJ-1A in 2013 and OHS-3C in 2020 were used to extract the vegetation types located in the area south of Ngoring Lake, covering 437.11 km2 in Maduo County, from the perspective of vegetation associations. Here, the hybrid spectral CNN (HybridSN) model, which is dependent on both spatial and spectral information, was used for vegetation association classifications. On this basis, the variations in vegetation associations from 2013 to 2020 were studied using the transition matrix, and the variation in noxious weeds across different altitude and slope gradients was analyzed. As an example, Thermopsis lanceolata’s spatial distribution pattern and diffusion mechanism were analyzed. The results showed that (1) in addition to noxious weeds, herbage such as Poa poophagorum, Stipa purpurea, Kobresia humilis, and Carex moorcroftii increased, indicating that the overall ecological environment tended to improve, which may be attributed mainly to the development of a warm and humid climate. (2) Most of the noxious weeds were located at low altitudes with an area increase in the 4250–4400 m altitude range and a decrease in the 4400–4500 m altitude range. More attention should be given to the fact that the noxious weeds area increased from 2.88 km2 to 9.02 km2 between 2013 and 2020, which was much faster than that of herbage and may threaten local livestock development. (3) The Thermopsis lanceolate association characterized by an aggregated distribution tended to spread along roads, herdsmen sites, and degraded swamps, which were mainly affected by human activities and swamp degradation.
Decomposed Dissimilarity Measure for Evaluation of Digital Image Denoising
Authors: Maliński Łukasz
Published: June 2023

A new approach to the evaluation of digital image denoising algorithms is presented. In the proposed method, the mean absolute error (MAE) is decomposed into three components that reflect the different cases of denoising imperfections. Moreover, aim plots are described, which are designed to be a very clear and intuitive form of presentation of the new decomposed measure. Finally, examples of the application of the decomposed MAE and the aim plots in the evaluation of impulsive noise removal algorithms are presented. The decomposed MAE measure is a hybrid of the image dissimilarity measure and detection performance measures. It provides information about sources of errors such as pixel estimation errors, unnecessary altered pixels, or undetected and uncorrected distorted pixels. It measures the impact of these factors on the overall correction performance. The decomposed MAE is suitable for the evaluation of algorithms that perform a detection of the distortion that affects only a certain fraction of the image pixels.
Sparse Reconstruction of Sound Field Using Bayesian Compressive Sensing and Equivalent Source Method
Authors: Xiao Yue , Yuan Lei , Wang Junyu , Hu Wenxin , Sun Ruimin
Published: June 2023

To solve the problem of sound field reconstruction with fewer measurement points, a sound field reconstruction method based on Bayesian compressive sensing is proposed. In this method, a sound field reconstruction model based on a combination of the equivalent source method and sparse Bayesian compressive sensing is established. The MacKay iteration of the relevant vector machine is used to infer the hyperparameters and estimate the maximum a posteriori probability of both the sound source strength and noise variance. The optimal solution for sparse coefficients with an equivalent sound source is determined to achieve the sparse reconstruction of the sound field. The numerical simulation results demonstrate that the proposed method has higher accuracy over the entire frequency range compared to the equivalent source method, indicating a better reconstruction performance and wider frequency applicability with undersampling. Moreover, in environments with low signal-to-noise ratios, the proposed method exhibits significantly lower reconstruction errors than the equivalent source method, indicating a superior anti-noise performance and greater robustness in sound field reconstruction. The experimental results further verify the superiority and reliability of the proposed method for sound field reconstruction with limited measurement points.
Multiform Informed Machine Learning Based on Piecewise and Weibull for Engine Remaining Useful Life Prediction
Authors: Zhou Shuang , Yao Yunan , Liu Aihua , Wang Fan , Chen Lu , Xiong Ruolan
Published: June 2023

Informed machine learning (IML), which strengthens machine learning (ML) models by incorporating external knowledge, can get around issues like prediction outputs that do not follow natural laws and models, hitting optimization limits. It is therefore of significant importance to investigate how domain knowledge of equipment degradation or failure can be incorporated into machine learning models to achieve more accurate and more interpretable predictions of the remaining useful life (RUL) of equipment. Based on the informed machine learning process, the model proposed in this paper is divided into the following three steps: (1) determine the sources of the two types of knowledge based on the device domain knowledge, (2) express the two forms of knowledge formally in Piecewise and Weibull, respectively, and (3) select different ways of integrating them into the machine learning pipeline based on the results of the formal expression of the two types of knowledge in the previous step. The experimental results show that the model has a simpler and more general structure than existing machine learning models and that it has higher accuracy and more stable performance in most datasets, particularly those with complex operational conditions, which demonstrates the effectiveness of the method in this paper on the C-MAPSS dataset and assists scholars in properly using domain knowledge to deal with the problem of insufficient training data.
Indoor Scene Recognition Mechanism Based on Direction-Driven Convolutional Neural Networks
Authors: Daou Andrea , Pothin Jean-Baptiste , Honeine Paul , Bensrhair Abdelaziz
Published: June 2023

Indoor location-based services constitute an important part of our daily lives, providing position and direction information about people or objects in indoor spaces. These systems can be useful in security and monitoring applications that target specific areas such as rooms. Vision-based scene recognition is the task of accurately identifying a room category from a given image. Despite years of research in this field, scene recognition remains an open problem due to the different and complex places in the real world. Indoor environments are relatively complicated because of layout variability, object and decoration complexity, and multiscale and viewpoint changes. In this paper, we propose a room-level indoor localization system based on deep learning and built-in smartphone sensors combining visual information with smartphone magnetic heading. The user can be room-level localized while simply capturing an image with a smartphone. The presented indoor scene recognition system is based on direction-driven convolutional neural networks (CNNs) and therefore contains multiple CNNs, each tailored for a particular range of indoor orientations. We present particular weighted fusion strategies that improve system performance by properly combining the outputs from different CNN models. To meet users’ needs and overcome smartphone limitations, we propose a hybrid computing strategy based on mobile computation offloading compatible with the proposed system architecture. The implementation of the scene recognition system is split between the user’s smartphone and a server, which aids in meeting the computational requirements of CNNs. Several experimental analysis were conducted, including to assess performance and provide a stability analysis. The results obtained on a real dataset show the relevance of the proposed approach for localization, as well as the interest in model partitioning in hybrid mobile computation offloading. Our extensive evaluation demonstrates an increase in accuracy compared to traditional CNN scene recognition, indicating the effectiveness and robustness of our approach.
Non-Destructive Measurements for 3D Modeling and Monitoring of Large Buildings Using Terrestrial Laser Scanning and Unmanned Aerial Systems
Authors: Nap Mircea-Emil , Chiorean Silvia , Cira Calimanut-Ionut , Manso-Callejo Miguel-Ángel , Păunescu Vlad , Șuba Elemer-Emanuel , Sălăgean Tudor
Published: June 2023

Along with the development and improvement of measuring technologies and techniques in recent times, new methods have appeared to model and monitor the behavior of land and constructions over time. The main purpose of this research was to develop a new methodology to model and monitor large buildings in a non-invasive way. The methods proposed in this research are non-destructive and can be used to monitor the behavior of buildings over time. A method of comparing point clouds obtained using terrestrial laser scanning combined with aerial photogrammetric methods was used in this study. The advantages and disadvantages of using non-destructive measurement techniques over the classic methods were also analyzed. With a building located in the University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca campus as a case study and with the help of the proposed methods, the deformations over time of the facades of that building were determined. As one of the main conclusions of this case study, it can be stated that the proposed methods are adequate to model and monitor the behavior of constructions over time, ensuring a satisfactory degree of precision and accuracy. The methodology can be successfully applied to other similar projects.
Exploring the Limits of Early Predictive Maintenance in Wind Turbines Applying an Anomaly Detection Technique
Authors: Jankauskas Mindaugas , Serackis Artūras , Šapurov Martynas , Pomarnacki Raimondas , Baskys Algirdas , Hyunh Van Khang , Vaimann Toomas , Zakis Janis
Published: June 2023

The aim of the presented investigation is to explore the time gap between an anomaly appearance in continuously measured parameters of the device and a failure, related to the end of the remaining resource of the device-critical component. In this investigation, we propose a recurrent neural network to model the time series of the parameters of the healthy device to detect anomalies by comparing the predicted values with the ones actually measured. An experimental investigation was performed on SCADA estimates received from different wind turbines with failures. A recurrent neural network was used to predict the temperature of the gearbox. The comparison of the predicted temperature values and the actual measured ones showed that anomalies in the gearbox temperature could be detected up to 37 days before the failure of the device-critical component. The performed investigation compared different models that can be used for temperature time-series modeling and the influence of selected input features on the performance of temperature anomaly detection.
DDD TinyML: A TinyML-Based Driver Drowsiness Detection Model Using Deep Learning
Authors: Alajlan Norah N. , Ibrahim Dina M.
Published: June 2023

Driver drowsiness is one of the main causes of traffic accidents today. In recent years, driver drowsiness detection has suffered from issues integrating deep learning (DL) with Internet-of-things (IoT) devices due to the limited resources of IoT devices, which pose a challenge to fulfilling DL models that demand large storage and computation. Thus, there are challenges to meeting the requirements of real-time driver drowsiness detection applications that need short latency and lightweight computation. To this end, we applied Tiny Machine Learning (TinyML) to a driver drowsiness detection case study. In this paper, we first present an overview of TinyML. After conducting some preliminary experiments, we proposed five lightweight DL models that can be deployed on a microcontroller. We applied three DL models: SqueezeNet, AlexNet, and CNN. In addition, we adopted two pretrained models (MobileNet-V2 and MobileNet-V3) to find the best model in terms of size and accuracy results. After that, we applied the optimization methods to DL models using quantization. Three quantization methods were applied: quantization-aware training (QAT), full-integer quantization (FIQ), and dynamic range quantization (DRQ). The obtained results in terms of the model size show that the CNN model achieved the smallest size of 0.05 MB using the DRQ method, followed by SqueezeNet, AlexNet MobileNet-V3, and MobileNet-V2, with 0.141 MB, 0.58 MB, 1.16 MB, and 1.55 MB, respectively. The result after applying the optimization method was 0.9964 accuracy using DRQ in the MobileNet-V2 model, which outperformed the other models, followed by the SqueezeNet and AlexNet models, with 0.9951 and 0.9924 accuracies, respectively, using DRQ.
Reverse Sexism and Its Impact on Job Satisfaction and Career Planning: Italian Validation of the “Belief in Sexism Shift Scale”
Authors: Morando Martina , Zehnter Miriam Katharina , Platania Silvia
Published: June 2023

The belief that men are the new victims of sexism and anti-male bias is gaining traction globally. The concept of reverse sexism, called the belief in sexism shift, is a new and particularly insidious form of contemporary anti-female sexism that combines the prejudice of hostile sexism with the subtlety of modern sexism. Facilitating the cross-cultural study of a rising form of sexism, in this paper, we provide an Italian translation of the BSS and examine its psychometric properties. In Study 1, we confirmed that the Italian BSS scale has the same uni-dimensional factor structure as the English version. In Study 2, we established that the Italian BSS scale measures the same construct among women and men. In Study 3, we found that the Italian BSS scale was a better predictor, compared to other measures of sexism, of numerous perceived career constraints. Subsequently, explorative analyses revealed that BSS escalated the effect of perceived career constraints on perceived job satisfaction and development opportunities among women and men. Together, our results suggest that BSS is a prevalent form of sexism in Italy that has the potential to negatively affect women and men.