bluuebunny/crossref_metadata_2025 huggingface dataset:
The Impact of Relapses on Pain and Quality of Life in Patients with Multiple Sclerosis Treated with Corticosteroids
Authors: Rakusa Martin , Chataway Jeremy , Hardy Todd A.
Published: September 2023

Background: We assessed the prevalence and risks associated with pain during and after a multiple sclerosis (MS) relapse, and the impact of pain on quality of life (QoL), in MS patients. Methods: 117 patients suffering an acute MS relapse were evaluated with clinician- and patient-reported outcomes, including the expanded disability status scale (EDSS), Multiple Sclerosis Impact Scale (MSIS-29), and MS Walking scale-12 (MSWS-12). Relapse-related pain was assessed via the short-form 36 (SF-36) questionnaire upon first visit (relapse onset) and at 6 weeks after treatment with intravenous methylprednisolone (follow-up visit). Results: Pain was present in 80% of patients at relapse onset. Patients with pain were more impaired physically (higher mean scores on MSIS-29phys and MSWS-12 and lower mean scores on SF-36 role physical, physical, and vitality scales) at relapse and six weeks after. In total, 74% of patients with MS relapse reported a poorer QoL due to pain. A lower psychological well-being was correlated with greater pain (MSIS29psy score). An increased number of prior relapses was a predictor of more pain at relapse onset. Conclusions: Pain was common at the time of MS relapse and improved, but was still significant, six weeks after treatment with corticosteroids. Further studies are required to better understand relapse-related pain.
In Vitro and In Vivo Antimelanogenesis Effects of Leaf Essential Oil from Agathis dammara
Authors: Ho Yu-Tung , Liu I-Hsuan , Chang Shang-Tzen , Wang Sheng-Yang , Chang Hui-Ting
Published: September 2023

Agathis species are widely distributed around Southeast Asia, Australasia, South Pacific islands, and etc. Traditionally, Agathis species have been used as the folk medicines, the common ethnopharmacological uses of Agathis genus are the treatments of headache and myalgia. This study aims to investigate the chemical composition of Agathis dammara (Lamb.) Rich. leaf essential oil and to explore its antimelanogenesis effect. The chemical constituents of leaf essential oil are analyzed using gas chromatography-mass spectrometry (GC-MS), the major constituents of leaf essential oil are sesquiterpenoids. The major constituents are δ-cadinene (16.12%), followed by γ-gurjunene (15.57%), 16-kaurene (12.43%), β-caryophyllene (8.58%), germacrene D (8.53%), and γ-cadinene (5.33%). As for the in vitro antityrosinase activity, leaf essential oil inhibit the tyrosinase activity of mushroom when the substrate is 3,4-dihydroxyphenylalanine (L-DOPA). Leaf essential oil prevents tyrosinase from acting as diphenolase and catalyzing L-DOPA to dopaquinone, and converting into dark melanin pigments. A. dammara leaf essential oil also exhibits the in vivo antimelanogenesis effect, leaf essential oil reduces 43.48% of melanin formation in zebrafish embryos at the concentration of 50 μg/mL. Results reveal A. dammara leaf essential oil has the potential for developing the skin whitening drug and depigmentation ingredient for hyperpigmentary disorders.
The Contribution of Antimicrobial Peptides to Immune Cell Function: A Review of Recent Advances
Authors: Li Hanxiao , Niu Junhui , Wang Xiaoli , Niu Mingfu , Liao Chengshui
Published: September 2023

The development of novel antimicrobial agents to replace antibiotics has become urgent due to the emergence of multidrug-resistant microorganisms. Antimicrobial peptides (AMPs), widely distributed in all kingdoms of life, present strong antimicrobial activity against a variety of bacteria, fungi, parasites, and viruses. The potential of AMPs as new alternatives to antibiotics has gradually attracted considerable interest. In addition, AMPs exhibit strong anticancer potential as well as anti-inflammatory and immunomodulatory activity. Many studies have provided evidence that AMPs can recruit and activate immune cells, controlling inflammation. This review highlights the scientific literature focusing on evidence for the anti-inflammatory mechanisms of different AMPs in immune cells, including macrophages, monocytes, lymphocytes, mast cells, dendritic cells, neutrophils, and eosinophils. A variety of immunomodulatory characteristics, including the abilities to activate and differentiate immune cells, change the content and expression of inflammatory mediators, and regulate specific cellular functions and inflammation-related signaling pathways, are summarized and discussed in detail. This comprehensive review contributes to a better understanding of the role of AMPs in the regulation of the immune system and provides a reference for the use of AMPs as novel anti-inflammatory drugs for the treatment of various inflammatory diseases.
Phytochemical Cue for the Fitness Costs of Herbicide-Resistant Weeds
Authors: Li Hong-Yu , Guo Yan , Jin Bo-Yan , Yang Xue-Fang , Kong Chui-Hua
Published: September 2023

Despite increasing knowledge of the fitness costs of viability and fecundity involved in the herbicide-resistant weeds, relatively little is known about the linkage between herbicide resistance costs and phytochemical cues in weed species and biotypes. This study demonstrated relative fitness and phytochemical responses in six herbicide-resistant weeds and their susceptible counterparts. There were significant differences in the parameters of viability (growth and photosynthesis), fecundity fitness (flowering and seed biomass) and a ubiquitous phytochemical (–)-loliolide levels between herbicide-resistant weeds and their susceptible counterparts. Fitness costs occurred in herbicide-resistant Digitaria sanguinalis and Leptochloa chinensis but they were not observed in herbicide-resistant Alopecurus japonicas, Eleusine indica, Ammannia arenaria, and Echinochloa crus-galli. Correlation analysis indicated that the morphological characteristics of resistant and susceptible weeds were negatively correlated with (–)-loliolide concentration, but positively correlated with lipid peroxidation malondialdehyde and total phenol contents. Principal component analysis showed that the lower the (–)-loliolide concentration, the stronger the adaptability in E. crus-galli and E. indica. Therefore, not all herbicide-resistant weeds have fitness costs, but the findings showed several examples of resistance leading to improved fitness even in the absence of herbicides. In particular, (–)-loliolide may act as a phytochemical cue to explain the fitness cost of herbicide-resistant weeds by regulating vitality and fecundity.
Melatonin Reverses High-Temperature-Stress-Inhibited Photosynthesis in the Presence of Excess Sulfur by Modulating Ethylene Sensitivity in Mustard
Authors: Iqbal Noushina , Sehar Zebus , Fatma Mehar , Khan Sheen , Alvi Ameena Fatima , Mir Iqbal R. , Masood Asim , Khan Nafees A.
Published: September 2023

Melatonin is a pleiotropic, nontoxic, regulatory biomolecule with various functions in abiotic stress tolerance. It reverses the adverse effect of heat stress on photosynthesis in plants and helps with sulfur (S) assimilation. Our research objective aimed to find the influence of melatonin, along with excess sulfur (2 mM SO42−), in reversing heat stress’s impacts on the photosynthetic ability of the mustard (Brassica juncea L.) cultivar SS2, a cultivar with low ATP-sulfurylase activity and a low sulfate transport index (STI). Further, we aimed to substantiate that the effect was a result of ethylene modulation. Melatonin in the presence of excess-S (S) increased S-assimilation and the STI by increasing the ATP-sulfurylase (ATP-S) and serine acetyltransferase (SAT) activity of SS2, and it enhanced the content of cysteine (Cys) and methionine (Met). Under heat stress, melatonin increased S-assimilation and diverted Cys towards the synthesis of more reduced glutathione (GSH), utilizing excess-S at the expense of less methionine and ethylene and resulting in plants’ reduced sensitivity to stress ethylene. The treatment with melatonin plus excess-S increased antioxidant enzyme activity, photosynthetic-S use efficiency (p-SUE), Rubisco activity, photosynthesis, and growth under heat stress. Further, plants receiving melatonin and excess-S in the presence of norbornadiene (NBD; an ethylene action inhibitor) under heat stress showed an inhibited STI and lower photosynthesis and growth. This suggested that ethylene was involved in the melatonin-mediated heat stress reversal effects on photosynthesis in plants. The interaction mechanism between melatonin and ethylene is still elusive. This study provides avenues to explore the melatonin–ethylene-S interaction for heat stress tolerance in plants.
Virus-Induced Gene Silencing in the Tea Plant (Camellia sinensis)
Authors: Yang Wei , Chen Xianya , Chen Jiahao , Zheng Peng , Liu Shaoqun , Tan Xindong , Sun Binmei
Published: September 2023

The recent availability of a number of tea plant genomes has sparked substantial interest in using reverse genetics to explore gene function in tea (Camellia sinensis). However, a hurdle to this is the absence of an efficient transformation system, and virus-induced gene silencing (VIGS), a transient transformation system, could be an optimal choice for validating gene function in the tea plant. In this study, phytoene desaturase (PDS), a carotenoid biosynthesis gene, was used as a reporter to evaluate the VIGS system. The injection sites of the leaves (leaf back, petiole, and stem) for infiltration were tested, and the results showed that petiole injection had the most effective injection, without leading to necrotic lesions that cause the leaves to drop. Tea leaves were inoculated with Agrobacterium harboring a tobacco rattle virus plasmid (pTRV2) containing a CsPDS silencing fragment. The tea leaves exhibited chlorosis symptoms 7–14 days after inoculation, depending on the cultivar. In the chlorosis plants, the coat protein (CP) of tobacco rattle virus (TRV) was detected and coincided with the lower transcription of CsPDS and reduced chlorophyll content compared with the empty vector control, with 81.82% and 54.55% silencing efficiency of ‘LTDC’ and ‘YSX’, respectively. These results indicate that the VIGS system with petiole injection could quickly and effectively silence a gene in tea plants.
Genome-Wide Identification and Expression Analysis of RLCK-VII Subfamily Genes Reveal Their Roles in Stress Responses of Upland Cotton
Authors: Cen Yuhan , Geng Shiyi , Gao Linying , Wang Xinyue , Yan Xin , Hou Yuxia , Wang Ping
Published: September 2023

Receptor-like cytoplasmic kinase VII (RLCK-VII) subfamily members are vital players in plant innate immunity and are also involved in plant development and abiotic stress tolerance. As a widely cultivated textile crop, upland cotton (Gossypium hirsutum) attaches great importance to the cotton industry worldwide. To obtain details of the composition, phylogeny, and putative function of RLCK-VII genes in upland cotton, genome-wide identification, evolutionary event analysis, and expression pattern examination of RLCK-VII subfamily genes in G. hirsutum were performed. There are 129 RLCK-VII members in upland cotton (GhRLCKs) and they were divided into nine groups based on their phylogenetic relationships. The gene structure and sequence features are relatively conserved within each group, which were divided based on their phylogenetic relationships, and consistent with those in Arabidopsis. The phylogenetic analysis results showed that RLCK-VII subfamily genes evolved in plants before the speciation of Arabidopsis and cotton, and segmental duplication was the major factor that caused the expansion of GhRLCKs. The diverse expression patterns of GhRLCKs in response to abiotic stresses (temperature, salt, and drought) and V. dahliae infection were observed. The candidates that may be involved in cotton’s response to these stresses are highlighted. GhRLCK7 (GhRLCK7A and D), which is notably induced by V. dahliae infection, was demonstrated to positively regulate cotton defense against V. dahliae by the loss-of-function assay in cotton. These findings shed light on the details of the RLCK-VII subfamily in cotton and provide a scaffold for the further function elucidation and application of GhRLCKs for the germplasm innovation of cotton.
Shell Deformities in the Green-Lipped Mussel Perna viridis: Occurrence and Potential Environmental Stresses on the West Coast of Peninsular Malaysia
Authors: Yap Chee Kong , Ahmad Wakid Sarini , Chew Jia Ming , Sutra Jumria , Syazwan Wan Mohd , Aziz Nor Azwady Abd , Mustafa Muskhazli , Nulit Rosimah , Okamura Hideo , Horie Yoshifumi , Ong Meng Chuan , Ismail Mohamad Saupi , Dwi Setyawan Ahmad , Kumar Krishnan , Zakaly Hesham M. H. , Cheng Wan Hee
Published: September 2023

The green-lipped mussel Perna viridis’ sensitive nature and characteristic as a benthos organism that filters the sediment in its environment make it one of the possible bioindicators for pollution in the aquatic ecosystem. The present study aimed to determine the percentages of total shell deformities in comparison to the past data in the coastal waters of Peninsular Malaysia. It was found that several types of discontinuous, continuous, and unexplained shell abnormalities contributed to the overall range of shell deformities of 15.8–87.5%, which was greater in comparison to that (0.0–36.8%). The present study showed that the highest overall proportion of shell abnormalities occurred in Teluk Jawa, whereas the lowest percentages were found in Kampung (Kg.) Pasir Puteh. The regulative mechanisms at the well-known polluted sites at Kg. Pasir Puteh could be the explanation. Further research should be conducted to determine the degree of heavy metal that may be the source of these malformations in the mussel shells.
Tuning the Ferroelectric Response of Sandwich-Structured Nanocomposites with the Coordination of Ba0.6Sr0.4TiO3 Nanoparticles and Boron Nitride Nanosheets to Achieve Excellent Discharge Energy Density and Efficiency
Authors: Yi Zhihui , Wang Zhuo , Wu Dan , Xue Ying
Published: September 2023

With the rapid development of new electronic products and sustainable energy systems, there is an increasing demand for electrical energy storage devices such as electrostatic capacitors. In order to comprehensively improve the dielectric, insulating, and energy storage properties of PVDF-based composites, sandwich-structured composites were prepared by layer-by-layer solution casting. The outer layers of the sandwich structure composite are both PVDF/boron nitride nanosheet composites, and the middle layer is a PVDF/Ba0.6Sr0.4TiO3 nanoparticles composite. The structural and electrical properties of the sandwich-structured composites were characterized and analyzed. The results show that when the volume percentage of Ba0.6Sr0.4TiO3 nanoparticles in the middle layer of the sandwich structure composite is 1 vol.%, the dielectric properties are significantly improved. Its dielectric constant is 8.99 at 10 kHz, the dielectric loss factor is 0.025, and it has better insulating properties and resistance to electrical breakdown. Benefiting from the high breakdown electric field strength and the large maximum electrical displacement, the sandwich-structured composites with 1 vol.% and Ba0.6Sr0.4TiO3 nanoparticles in the middle layer show a superior discharge energy density of 8.9 J/cm3, and excellent charge and discharge energy efficiency of 76%. The sandwich structure composite achieves the goal of simultaneous improvement in breakdown electric field strength and dielectric constant.
Thermal Properties and Temporal Dynamics of Red Latosol (Oxisol) in Sustainable Agriculture and Environmental Conservation
Authors: Jordan Rodrigo Aparecido , Santos Rodrigo Couto , Freitas Ricardo Lordelo , Motomiya Anamari Viegas de Araújo , Geisenhoff Luciano Oliveira , Sanches Arthur Carniato , Ávalo Hélio , Mesquita Marcio , Ferreira Maria Beatriz , Silva Patrícia Costa , Sanches Ítalo Sabião , Sanches Édipo Sabião , Da Silva Jhon Lennon Bezerra , Silva Marcos Vinícius da
Published: September 2023

Understanding and characterizing the relationship between soil and environmental temperatures is crucial for developing effective agricultural management strategies, promoting natural resource conservation, and developing sustainable production systems. Despite the direct impact of the thermal properties of Oxisols on global food production and sustainable agriculture, there is a dearth of research in this area. Therefore, this study aimed to monitor and analyze the thermal behavior of a Red Latosol (Oxisol) in Dracena-SP, Brazil, over two years (from 28 July 2020 to 27 July 2022). Using R software (version 4.3.0) and paired group comparisons, we organized the data into twelve-month sets to estimate monthly soil thermal diffusivity using amplitude, arctangent, and logarithm methods. Soil depth and thermal amplitude showed a temporal pattern characterized by inversely proportional magnitudes that followed an exponential behavior. The thermal amplitude of the Oxisol evaluated decreased with increasing depth, indicating soil thermal damping. In conclusion, the relationship between Oxisol and environmental temperature has significant implications for achieving sustainable agriculture and efficient water and plant resource management.
Optimizing an Algorithm Designed for Sparse-Frequency Waveforms for Use in Airborne Radars
Authors: Hou Ming , Xie Wenchong , Xiong Yuanyi , Li Hu , Qu Qizhe , Lei Zhenshuo
Published: September 2023

Low-frequency bands are an important way to realize stealth target detection for airborne radars. However, in a complex electromagnetic environment; when low-frequency airborne radar operates over land, it will inevitably encounter a lot of unintentional communication and intentional interference, while effective suppression of interference can not be achieved only through the adaptive processing of the receiver. To solve this problem, this paper proposes optimizing an algorithm designed for sparse-frequency waveforms for use in airborne radars. The algorithm establishes a joint objective function based on the criteria of minimizing waveform energy in the spectrum stopband and minimizing the integrated sidelobe level of specified range cells. The waveform is optimized by a cyclic iterative algorithm based on the Fast Fourier Transform (FFT) operation. It can ensure the frequency domain stopband constraint to realize the effective suppression of main-lobe interference while forming lower-range sidelobes at specified range cells to improve the ability to detect dim targets. Theoretical analysis and simulation results have shown that the algorithm has good anti-interference performance.
Low-Illumination Image Enhancement Using Local Gradient Relative Deviation for Retinex Models
Authors: Yang Biao , Zheng Liangliang , Wu Xiaobin , Gao Tan , Chen Xiaolong
Published: September 2023

In order to obtain high-quality images, the application of low-illumination image enhancement techniques plays a vital role in enhancing the overall visual appeal. However, it is particularly difficult to enhance an image while maintaining the original information of the scene. The augmentation method based on Retinex theory is widely considered as one of the representative techniques for such problems, but this method still has some limitations. First of all, noise is easily ignored in the process of model building, and the robustness of the model needs to be improved. Secondly, the image decomposition is less effective, so that part of the image information is not effectively presented. Finally, the optimization procedure is computationally complicated. This paper introduces a novel approach for enhancing low-illumination images by utilizing the relative deviation of local gradients. The proposed method aims to address the challenges associated with low-illumination images and offers a solution to these issues. In this paper, local gradient relative deviation is used as a constraint term and a noise term is added to highlight the image texture and structure and improve the robustness of the models, considering that LP achieves piecewise smoothing with better sparsity compared to the sum norm commonly used by L1 and L2 norms. In this paper, the L2−LP norm is used to constrain the model, which smooths the illumination component and better preserves the details of the reflectance component. In addition, to efficiently solve the optimization problem, the alternating direction multiplier method is chosen to transform the optimization process into the solution of several sub-problems. In comparison to traditional Retinex models, the proposed method excels in its ability to simultaneously enhance the image and suppress noise effectively. The experimental outcomes demonstrate the effectiveness of the proposed model in enhancing both simulated and real data. This approach can be applied to low-illumination remote sensing images to obtain high-quality remote sensing image data.
Improved Global Navigation Satellite System–Multipath Reflectometry (GNSS-MR) Tide Variation Monitoring Using Variational Mode Decomposition Enhancement
Authors: Yang Di , Feng Wei , Huang Dingfa , Li Jianfeng
Published: September 2023

Accuracy and resolution are the two primary challenges that impose limitations on the practical implementation of classical tide-level remote sensing. To improve the accuracy and applicability and increase the temporal resolution of the inversion point near the shore area, the influence of coastal reflection signals in the signal-to-noise ratio (SNR) residual sequence should be weakened significantly. This contribution proposes an anti-interference GNSS Multipath Reflectometry (GNSS-MR) algorithm called VMD_SNR, which is enhanced using variational mode decomposition (VMD). Compared with wavelet decomposition and empirical mode decomposition (EMD) methods, VMD_SNR exhibits superior capabilities in reducing the interference caused by noisy signals. The measurements of ground-based GNSS stations are used to verify the performance improvement in the VMD_SNR algorithm. The results show that the proposed algorithm is better than the wavelet decomposition method and EMD method in terms of accuracy and stability in the shore area, where the effective number is higher than 99% of the total number, and the accuracy is better than 13.80 cm. Moreover, the accuracy improvement is more significant in the high-elevation range, which is 30.16% higher than the wavelet decomposition method and 38.34% higher than the EMD method.
Fast Variational Bayesian Inference for Space-Time Adaptive Processing
Authors: Zhang Xinying , Wang Tong , Wang Degen
Published: September 2023

Space-time adaptive processing (STAP) approaches based on sparse Bayesian learning (SBL) have attracted much attention for the benefit of reducing the training samples requirement and accurately recovering sparse signals. However, it has the problem of a heavy computational burden and slow convergence speed. To improve the convergence speed, the variational Bayesian inference (VBI) is introduced to STAP in this paper. Moreover, to improve computing efficiency, a fast iterative algorithm is derived. By constructing a new atoms selection rule, the dimension of the matrix inverse problem can be substantially reduced. Experiments conducted on the simulated data and measured data verify that the proposed algorithm has excellent clutter suppression and target detection performance.
Estimating Leymus chinensis Loss Caused by Oedaleus decorus asiaticus Using an Unmanned Aerial Vehicle (UAV)
Authors: Du Bobo , Ding Xiaolong , Ji Chao , Lin Kejian , Guo Jing , Lu Longhui , Dong Yingying , Huang Wenjiang , Wang Ning
Published: September 2023

Oedaleus decorus asiaticus is one of the dominant harmful pests in central Inner Mongolia, China. Large-scale outbreaks of this pest create many serious problems in animal husbandry and agriculture. Therefore, understanding the underlying mechanisms between plant losses and Odecorus at different density levels and growth stages can guide the development of monitoring and prediction measures to reduce damage. In this study, an unmanned aerial vehicle (UAV) carrying a camera was employed to collect multi-spectral data. Further, nine vegetation indices (VIs) were analyzed to explore the most suitable indices for estimating plant loss caused by O. decorus in different growth stages. The following results were obtained: (1) The second instar nymphs of O. decorus could promote vegetation growth. As the density level in each cage increased, the biomass of each cage increased (nymph density < 30 nymphs/m2) and then decreased (nymph density ≥ 30 nymphs/m2). When nymph density was greater than 60 nymphs/m2, the biomass in those cages decreased significantly. (2) With respect to the control group, large damage began to emerge during the third instar nymphal stage. In particular, the largest vegetation loss was caused by fourth nymphal larvae. (3) The ratio vegetation index (RVI) appeared as the most excellent index for reflecting Leymus chinensis loss caused by O. decorus at different growth stages. Nevertheless, the difference vegetation index (DVI) was better than the RVI in the fifth instar nymphal stage.
Analysis of the Matchability of Reference Imagery for Aircraft Based on Regional Scene Perception
Authors: Li Xin , Zhang Guo , Cui Hao , Ma Jinhao , Wang Wei
Published: September 2023

Scene matching plays a vital role in the visual positioning of aircraft. The position and orientation of aircraft can be determined by comparing acquired real-time imagery with reference imagery. To enhance precise scene matching during flight, it is imperative to conduct a comprehensive analysis of the reference imagery’s matchability beforehand. Conventional approaches to image matchability analysis rely heavily on features that are manually designed. However, these features are inadequate in terms of comprehensiveness, efficiency, and taking into account the scene matching process, ultimately leading to unsatisfactory results. This paper innovatively proposes a core approach to quantifying matchability by utilizing scene information from imagery. The first proposal for generating image matchability samples through a simulation of the matching process has been developed. The RSPNet network architecture is designed to effectively leverage regional scene perception in order to accurately predict the matchability of reference imagery. This network comprises two core modules: saliency analysis and uniqueness analysis. The attention mechanism employed by saliency analysis module extracts features at different levels and scales, guaranteeing an accurate and meticulous quantification of image saliency. The uniqueness analysis module quantifies image uniqueness by comparing neighborhood scene features. The proposed method is compared with traditional and deep learning methods for experiments based on simulated datasets, respectively. The results demonstrate that RSPNet exhibits significant advantages in terms of accuracy and reliability.
Spatiotemporal Characteristics and Volume Transport of Lagrangian Eddies in the Northwest Pacific
Authors: Yuan Quanmu , Hu Jianyu
Published: September 2023

Mesoscale eddies play a crucial role in the transport of mass, heat, salt and nutrients, exerting significant influence on ocean circulation patterns, biogeochemical processes and the global climate system. Based on Lagrangian-Averaged Vorticity Deviation (LAVD) method, this study applies 27 years (1993–2019) of geostrophic current velocity data to detect Rotationally Coherent Lagrangian Vortices (RCLVs) in the Northwest Pacific (NWP; 10°N–30°N, 115°E–155°E), with the spatiotemporal characteristics of Eulerian Sea Surface Height Eddies (SSH eddies) and RCLVs being compared. A higher number of SSH eddies and RCLVs can be observed in spring and winter, and their inter-annual variations are similar. SSH eddies show higher generation number and larger radius in the Subtropical Countercurrent region, while RCLVs occur more favorably in the ocean basin. The propagation speed distributions of both eddy types are nearly identical and decrease with increasing latitude. Due to the material coherent transport maintained by RCLVs within a finite time interval, the coherent cores of RCLVs are considerably smaller in scale as compared to those of SSH eddies. The average zonal transports induced by SSH eddies and RCLVs are estimated to be −0.82 Sv and −0.51 Sv (1 Sv = 106 m3/s), respectively. For non-overlapping SSH eddies with RCLVs, approximately 80% of the water within the eddy leaks out during the eddy’s lifespan. In the case of overlapping SSH eddies, the ratio of coherent water inside the eddy decreases with increasing radius, and the leakage rate is around 58%. Finally, an examination of 36 shedding RCLVs events from the Kuroshio near the Luzon Strait, which induce an average zonal transport of −0.14 Sv, reveals that 54% of the water within the shedding RCLVs originates from the Kuroshio.
Smartphone MEMS Accelerometer and Gyroscope Measurement Errors: Laboratory Testing and Analysis of the Effects on Positioning Performance
Authors: Capuano Vincenzo , Xu Liangchun , Estrada Benavides Jose
Published: September 2023

Embedding various sensors with powerful computing and storage capabilities in a small communication device, smartphones have become a prominent platform for navigation. With the increasing popularity of Apple CarPlay and Android Auto, smartphones are quickly replacing built-in automotive navigation solutions. On the other hand, smartphones are equipped with low-performance Micro Electro Mechanical Systems (MEMS) sensors to enhance their navigation performance in Global Navigation Satellite System (GNSS)-degraded or -denied environments. Compared with higher-grade inertial navigation systems (INS), MEMS-based INS have a poor navigation performance due to large measurement errors. In this paper, we present laboratory test results on the stochastic and deterministic errors observed in MEMS inertial sensor measurements of five different smartphones from different manufacturers. Then, we describe and discuss the short-term effects of these errors on the pure inertial navigation performance and also on the navigation performance based on the tight coupling of INS with GNSS measurements using a smartphone.
FirebotSLAM: Thermal SLAM to Increase Situational Awareness in Smoke-Filled Environments
Authors: van Manen Benjamin Ronald , Sluiter Victor , Mersha Abeje Yenehun
Published: September 2023

Operating in extreme environments is often challenging due to the lack of perceptual knowledge. During fire incidents in large buildings, the extreme levels of smoke can seriously impede a firefighter’s vision, potentially leading to severe material damage and loss of life. To increase the safety of firefighters, research is conducted in collaboration with Dutch fire departments into the usability of Unmanned Ground Vehicles to increase situational awareness in hazardous environments. This paper proposes FirebotSLAM, the first algorithm capable of coherently computing a robot’s odometry while creating a comprehensible 3D map solely using the information extracted from thermal images. The literature showed that the most challenging aspect of thermal Simultaneous Localization and Mapping (SLAM) is the extraction of robust features in thermal images. Therefore, a practical benchmark of feature extraction and description methods was performed on datasets recorded during a fire incident. The best-performing combination of extractor and descriptor is then implemented into a state-of-the-art visual SLAM algorithm. As a result, FirebotSLAM is the first thermal odometry algorithm able to perform global trajectory optimization by detecting loop closures. Finally, FirebotSLAM is the first thermal SLAM algorithm to be tested in a fiery environment to validate its applicability in an operational scenario.
Augmented-Reality Presentation of Household Sounds for Deaf and Hard-of-Hearing People
Authors: Asakura Takumi
Published: September 2023

Normal-hearing people use sound as a cue to recognize various events that occur in their surrounding environment; however, this is not possible for deaf and hearing of hard (DHH) people, and in such a context they may not be able to freely detect their surrounding environment. Therefore, there is an opportunity to create a convenient device that can detect sounds occurring in daily life and present them visually instead of auditorily. Additionally, it is of great importance to appropriately evaluate how such a supporting device would change the lives of DHH people. The current study proposes an augmented-reality-based system for presenting household sounds to DHH people as visual information. We examined the effect of displaying both the icons indicating sounds classified by machine learning and a dynamic spectrogram indicating the real-time time–frequency characteristics of the environmental sounds. First, the issues that DHH people perceive as problems in their daily lives were investigated through a survey, suggesting that DHH people need to visualize their surrounding sound environment. Then, after the accuracy of the machine-learning-based classifier installed in the proposed system was validated, the subjective impression of how the proposed system increased the comfort of daily life was obtained through a field experiment in a real residence. The results confirmed that the comfort of daily life in household spaces can be improved by combining not only the classification results of machine learning but also the real-time display of spectrograms.
Security–Reliability Analysis of AF Full-Duplex Relay Networks Using Self-Energy Recycling and Deep Neural Networks
Authors: Nguyen Tan N. , Minh Bui Vu , Tran Dinh-Hieu , Le Thanh-Lanh , Le Anh-Tu , Nguyen Quang-Sang , Lee Byung Moo
Published: September 2023

This paper investigates the security–reliability of simultaneous wireless information and power transfer (SWIPT)-assisted amplify-and-forward (AF) full-duplex (FD) relay networks. In practice, an AF-FD relay harvests energy from the source (S) using the power-splitting (PS) protocol. We propose an analysis of the related reliability and security by deriving closed-form formulas for outage probability (OP) and intercept probability (IP). The next contribution of this research is an asymptotic analysis of OP and IP, which was generated to obtain more insight into important system parameters. We validate the analytical formulas and analyze the impact on the key system parameters using Monte Carlo simulations. Finally, we propose a deep learning network (DNN) with minimal computation complexity and great accuracy for OP and IP predictions. The effects of the system’s primary parameters on OP and IP are examined and described, along with the numerical data.
EFC-YOLO: An Efficient Surface-Defect-Detection Algorithm for Steel Strips
Authors: Li Yanshun , Xu Shuobo , Zhu Zhenfang , Wang Peng , Li Kefeng , He Qiang , Zheng Quanfeng
Published: September 2023

The pursuit of higher recognition accuracy and speed with smaller model sizes has been a major research topic in the detection of surface defects in steel. In this paper, we propose an improved high-speed and high-precision Efficient Fusion Coordination network (EFC-YOLO) without increasing the model’s size. Since modifications to enhance feature extraction in shallow networks tend to affect the speed of model inference, in order to simultaneously ensure the accuracy and speed of detection, we add the improved Fusion-Faster module to the backbone network of YOLOv7. Partial Convolution (PConv) serves as the basic operator of the module, which strengthens the feature-extraction ability of shallow networks while maintaining speed. Additionally, we incorporate the Shortcut Coordinate Attention (SCA) mechanism to better capture the location information dependency, considering both lightweight design and accuracy. The de-weighted Bi-directional Feature Pyramid Network (BiFPN) structure used in the neck part of the network improves the original Path Aggregation Network (PANet)-like structure by adding step branches and reducing computations, achieving better feature fusion. In the experiments conducted on the NEU-DET dataset, the final model achieved an 85.9% mAP and decreased the GFLOPs by 60%, effectively balancing the model’s size with the accuracy and speed of detection.
Simulation of Laser Profilometer Measurements in the Presence of Speckle Using Perlin Noise
Authors: Roos-Hoefgeest Sara , Roos-Hoefgeest Mario , Álvarez Ignacio , González Rafael C.
Published: September 2023

In the manufacturing industry, inspection systems play a crucial role in ensuring product quality. High-resolution profilometric sensors have become increasingly popular for inspection due to their ability to provide detailed surface information. However, the development and testing of inspection systems can be costly and time-consuming. This paper presents the development of a simulation of an inspection system using a high-resolution profilometric sensor. A geometrical and noise model is proposed to simulate the readings of any actual profilometric sensor. The model replicates the sensor’s movement on the CAD model of the inspected part. The model incorporates the physical properties of the sensor and combines noise sources from sensor uncertainty and speckle noise induced by the roughness of the material. Our contribution lies in noise modeling. This work proposes a combination of Perlin noise to simulate the speckle noise and Gaussian noise for the uncertainty-related noise. Perlin noise is generated based on the surface roughness parameters of the inspected part. The accuracy of the simulation system is evaluated by comparing the simulated scans with real scans. The results highlight the ability to simulate real scans of different parts, using commercial sensor specifications and the CAD model of the inspected part.
Analysis and Suppression Design of Stray Light Pollution in a Spectral Imager Loaded on a Polar-Orbiting Satellite
Authors: Chen Shuaishuai , Niu Xinhua
Published: September 2023

As the non-imaging light of optical instruments, stray light has an important impact on normal imaging and data quantification applications. The FY-3D Medium Resolution Spectral Imager (MERSI) operates in a sun-synchronous orbit, with a scanning field of view of 110° and a surface imaging width of more than 2300 km, which can complete two coverage observations of global targets per day with high detection efficiency. According to the characteristics of the operating orbit and large-angle scanning imaging of MERSI, a stray light radiation model of the polar-orbiting spectrometer is constructed, and the design requirements of stray light suppression are proposed. Using the point source transmittance (PST) as the merit function of the stray light analysis method, the instrument was simulated with all stray light suppression optical paths, and the effectiveness of stray light elimination measures was verified using the stray light test. In this paper, the full-link method of “orbital stray light radiation model-system, internal and external simulation design-system analysis and actual test comparison verification” is proposed, and there is a maximum decrease in the system’s PST by about 10 times after applying the stray light suppression’s optimization design, which can provide a general method for stray light suppression designs for polar-orbit spectral imagers.
Multiscale Feature-Learning with a Unified Model for Hyperspectral Image Classification
Authors: Arshad Tahir , Zhang Junping , Ullah Inam , Ghadi Yazeed Yasin , Alfarraj Osama , Gafar Amr
Published: September 2023

In the realm of hyperspectral image classification, the pursuit of heightened accuracy and comprehensive feature extraction has led to the formulation of an advance architectural paradigm. This study proposed a model encapsulated within the framework of a unified model, which synergistically leverages the capabilities of three distinct branches: the swin transformer, convolutional neural network, and encoder–decoder. The main objective was to facilitate multiscale feature learning, a pivotal facet in hyperspectral image classification, with each branch specializing in unique facets of multiscale feature extraction. The swin transformer, recognized for its competence in distilling long-range dependencies, captures structural features across different scales; simultaneously, convolutional neural networks undertake localized feature extraction, engendering nuanced spatial information preservation. The encoder–decoder branch undertakes comprehensive analysis and reconstruction, fostering the assimilation of both multiscale spectral and spatial intricacies. To evaluate our approach, we conducted experiments on publicly available datasets and compared the results with state-of-the-art methods. Our proposed model obtains the best classification result compared to others. Specifically, overall accuracies of 96.87%, 98.48%, and 98.62% were obtained on the Xuzhou, Salinas, and LK datasets.
Recent Developments on Drivable Area Estimation: A Survey and a Functional Analysis
Authors: Hortelano Juan Luis , Villagrá Jorge , Godoy Jorge , Jiménez Víctor
Published: September 2023

Most advanced autonomous driving systems (ADS) today rely on the prior creation of high-definition maps (HD maps). This process is expensive and needs to be performed frequently to keep up with the changing conditions of the road environment. Creating accurate navigation maps online is an alternative to reduce the cost and broaden the current operational design domains (ODD) of modern ADS. This paper offers a snapshot of the state of the art in drivable area estimation, which is an essential technology to deploy ADS in ODDs where HD maps are limited or unavailable. The proposed review introduces a novel architecture breakdown that fits learning-based and non-learning-based techniques and allows the analysis of a set of impactful and recent drivable area algorithms. In addition to that, complimentary information for practitioners is provided: (i) an assessment of the influence of modern sensing technologies on the task under study and (ii) a selection of relevant datasets for evaluation and benchmarking purposes.
Design and Implementation of a 2D MIMO OCC System Based on Deep Learning
Authors: Sitanggang Ones Sanjerico , Nguyen Van Linh , Nguyen Huy , Pamungkas Radityo Fajar , Faridh Muhammad Miftah , Jang Yeong Min
Published: September 2023

Optical camera communication (OCC) is one of the most promising optical wireless technology communication systems. This technology has a number of benefits compared to radio frequency, including unlimited spectrum, no congestion due to high usage, and low operating costs. OCC operates in order to transmit an optical signal from a light-emitting diode (LED) and receive the signal with a camera. However, identifying, detecting, and extracting data in a complex area with very high mobility is the main challenge in operating the OCC. In this paper, we design and implement a real-time OCC system that can communicate in high mobility conditions, based on You Only Look Once version 8 (YOLOv8). We utilized an LED array that can be identified accurately and has an enhanced data transmission rate due to a greater number of source lights. Our system is validated in a highly mobile environment with camera movement speeds of up to 10 m/s at 2 m, achieving a bit error rate of 10−2. In addition, this system achieves high accuracy of the LED detection algorithm with mAP0.5 and mAP0.5:0.95 values of 0.995 and 0.8604, respectively. The proposed method has been tested in real time and achieves processing speeds up to 1.25 ms.
Improved YOLOv7 Algorithm for Detecting Bone Marrow Cells
Authors: Cheng Zhizhao , Li Yuanyuan
Published: September 2023

The detection and classification of bone marrow (BM) cells is a critical cornerstone for hematology diagnosis. However, the low accuracy caused by few BM-cell data samples, subtle difference between classes, and small target size, pathologists still need to perform thousands of manual identifications daily. To address the above issues, we propose an improved BM-cell-detection algorithm in this paper, called YOLOv7-CTA. Firstly, to enhance the model’s sensitivity to fine-grained features, we design a new module called CoTLAN in the backbone network to enable the model to perform long-term modeling between target feature information. Then, in order to cooperate with the CoTLAN module to pay more attention to the features in the area to be detected, we integrate the coordinate attention (CoordAtt) module between the CoTLAN modules to improve the model’s attention to small target features. Finally, we cluster the target boxes of the BM cell dataset based on K-means++ to generate more suitable anchor boxes, which accelerates the convergence of the improved model. In addition, in order to solve the imbalance between positive and negative samples in BM-cell pictures, we use the Focal loss function to replace the multi-class cross entropy. Experimental results demonstrate that the best mean average precision (mAP) of the proposed model reaches 88.6%, which is an improvement of 12.9%, 8.3%, and 6.7% compared with that of the Faster R-CNN model, YOLOv5l model, and YOLOv7 model, respectively. This verifies the effectiveness and superiority of the YOLOv7-CTA model in BM-cell-detection tasks.
Silicon-Cantilever-Enhanced Single-Fiber Photoacoustic Acetylene Gas Sensor
Authors: Zhang Zhengyuan , Fan Xinhong , Xu Yufu , Wang Yongqi , Tang Yiyao , Zhao Rui , Li Chenxi , Wang Heng , Chen Ke
Published: September 2023

A single-fiber photoacoustic (PA) sensor with a silicon cantilever beam for trace acetylene (C2H2) gas analysis was proposed. The miniature gas sensor mainly consisted of a microcantilever and a non-resonant PA cell for the real-time detection of acetylene gas. The gas diffused into the photoacoustic cell through the silicon cantilever beam gap. The volume of the PA cell in the sensor was about 14 μL. By using a 1 × 2 fiber optical coupler, a 1532.8 nm distributed feedback (DFB) laser and a white light interference demodulation module were connected to the single-fiber photoacoustic sensor. A silicon cantilever was utilized to improve the performance when detecting the PA signal. To eliminate the interference of the laser-reflected light, a part of the Fabry–Perot (F-P) interference spectrum was used for phase demodulation to achieve the highly sensitive detection of acetylene gas. The minimum detection limit (MDL) achieved was 0.2 ppm with 100 s averaging time. In addition, the calculated normalized noise equivalent absorption (NNEA) coefficient was 4.4 × 10−9 W·cm−1·Hz−1/2. The single-fiber photoacoustic sensor designed has great application prospects in the early warning of transformer faults.