bluuebunny/crossref_metadata_2025 huggingface dataset:
Identification of South African Plant-Based Bioactive Compounds as Potential Inhibitors against the SARS-CoV-2 Receptor
Authors: Mkolo Nqobile Monate , Naidoo Clarissa Marcelle , Kadye Rose , Obi Chikwelu Lawrence , Iweriebor Benson Chucks , Olaokun Oyinlola Oluwunmi , Prinsloo Earl , Zubair Muhammad Sulaiman
Published: June 2024

The expected progress in SARS-CoV-2 vaccinations, as anticipated in 2020 and 2021, has fallen short, exacerbating global disparities due to a lack of universally recognized “safe and effective” vaccines. This study focuses on extracts of South African medicinal plants, Artemisia annua and Artemisia afra, to identify metabolomic bioactive compounds inhibiting the binding of the SARS-CoV-2 spike protein to ACE2 receptors. The extracts were monitored for cytotoxicity using a resazurin cell viability assay and xCELLigence real-time cell analyzer. Chemical profiling was performed using UPLC-MS/MS, orthogonal projection to latent structures (OPLS), and evaluated using principle component analysis (PCA) models. Identified bioactive compounds were subjected to in vitro SARS-CoV-2 enzyme inhibition assay using standard methods and docked into the spike (S) glycoprotein of SARS-CoV-2 using Schrodinger® suite followed by molecular dynamics simulation studies. Cell viability assays revealed non-toxic effects of extracts on HEK293T cells at lower concentrations. Chemical profiling identified 81 bioactive compounds, with compounds like 6″-O-acetylglycitin, 25-hydroxyvitamin D3-26,23-lactone, and sesaminol glucoside showing promising binding affinity. Molecular dynamics simulations suggested less stable binding, but in vitro studies demonstrated the ability of these compounds to interfere with SARS-CoV-2 spike protein’s binding to the human ACE2 receptor. Sesaminol glucoside emerged as the most effective inhibitor against this interaction. This study emphasizes the importance of multiplatform metabolite profiling and chemometrics to understand plant extract composition. This finding is of immense significance in terms of unravelling metabolomics bioactive compounds inhibiting the binding of the SARS-CoV-2 spike protein to ACE2 receptors and holds promise for phytotherapeutics against SARS-CoV-2.
Prognostic Significance of Pan-Immune-Inflammation Value in Patients with HER2-Positive Metastatic Breast Cancer Treated with Trastuzumab Emtansine
Authors: Sahin Taha Koray , Akyildiz Arif , Dogan Osman Talha , Kavgaci Gozde , Guven Deniz Can , Aksoy Sercan
Published: June 2024

Trastuzumab emtansine (T-DM1) is a mainstay therapy for HER2-positive metastatic breast cancer (mBC). However, identifying patients who will benefit most remains a challenge due to the lack of reliable biomarkers. The recently developed pan-immune-inflammation value (PIV), a novel immune-inflammation marker, could aid in this regard, considering the immunomodulatory effects of T-DM1. Therefore, we aimed to evaluate the association between the PIV and the efficacy of T-DM1 in patients with HER2-positive mBC. A total of 122 HER2-positive mBC patients treated with T-DM1 were included. Receiver operating characteristic (ROC) curve analyses were conducted to determine the optimal PIV threshold value for survival prediction. Kaplan–Meier survival curves and Cox regression analyses were used for univariable and multivariable survival analyses, respectively. The median age was 51 years, and 95.1% of the patients had ECOG PS 0-1. The optimal PIV cutoff value was identified as 338 in ROC analyses (AUC: 0.667, 95% CI: 0.569–0.765, p = 0.002). The multivariate analysis revealed that patients in the high-PIV group had significantly shorter OS (HR: 2.332; 95% CI: 1.408–3.861; p = 0.001) and PFS (HR: 2.423; 95% CI: 1.585–3.702; p < 0.001) than patients in the low-PIV group. Additionally, both ORR and DCR were significantly lower in the high-PIV group (36.6% vs. 61.3%, p = 0.011; 56.1% vs. 76.0%, p = 0.027). Our findings suggest that pre-treatment PIV may be a novel prognostic biomarker for HER2-positive mBC patients receiving T-DM1. A low PIV level is associated with more favorable outcomes. Future prospective studies are warranted to validate these findings and explore the potential utility of PIV in aiding treatment decisions.
Research Progress on Mechanical Strength of Rice Stalks
Authors: Yang Huimin , Huang Jiahui , Ye Yuhan , Xu Yuqing , Xiao Yao , Chen Ziying , Li Xinyu , Ma Yingying , Lu Tao , Rao Yuchun
Published: June 2024

As one of the most important food crops in the world, rice yield is directly related to national food security. Lodging is one of the most important factors restricting rice production, and the cultivation of rice varieties with lodging resistance is of great significance in rice breeding. The lodging resistance of rice is directly related to the mechanical strength of the stalks. In this paper, we reviewed the cell wall structure, its components, and its genetic regulatory mechanism, which improved the regulatory network of rice stalk mechanical strength. Meanwhile, we analyzed the new progress in genetic breeding and put forward some scientific problems that need to be solved in this field in order to provide theoretical support for the improvement and application of rice breeding.
Sequential Separation of Essential Oil Components during Hydrodistillation of Fresh Foliage from Azorean Cryptomeria japonica (Cupressaceae): Effects on Antibacterial, Antifungal, and Free Radical Scavenging Activities
Authors: Arruda Filipe , Lima Ana , Wortham Tanner , Janeiro Alexandre , Rodrigues Tânia , Baptista José , Rosa José S. , Lima Elisabete
Published: June 2024

Cryptomeria japonica wood industry generates large amounts of foliage biomass residues. Due to the increasing applications and markets for essential oils (EOs), fresh Azorean C. japonica foliage (Az–CJF) residues are used for local EO production. Hydrodistillation (HD), a common process for obtaining EOs, also provides the possibility to fractionate them. Thus, this study evaluated the in vitro antimicrobial and antioxidant activities of six Az–CJF EO fractions (Frs. 1–6), collected at sequential HD timeframes (HDTs: 0–2, 2–10, 10–30, 30–60, 60–120, and 120–240 min), in comparison to the crude EO, obtained from a non-fractionated HD (0–240 min HDT). Antimicrobial activities were assessed via disc diffusion method against seven bacteria (foodborne and/or human pathogens) and two Penicillium spp. (phytopathogenic fungi), and antioxidant activity was estimated using DPPH and ABTS assays. Concerning the antibacterial activity, all the EO samples were effective only toward Gram-positive bacteria. Fractions 1–3 (<30 min HDT) were the most active, with growth inhibition zones (GIZ) of 7.0–23.3 mm (1.4–2.2 times higher than those of the crude EO), being Bacillus spp. (B. licheniformis and B. subtilis) the most sensitive, followed by Staphylococcus aureus and Micrococcus luteus. Regarding the antifungal activity, Frs. 1–3 also displayed the best activities, but only against P. italicum (GIZ around 9.0 mm), while the crude EO showed no antifungal activity. Overall, the best antimicrobial properties of Frs. 1–3 could be attributed, at least in part, to their highest content in α-pinene and bornyl acetate. On the other hand, Frs. 4–6 (>30 min HDT) exhibited the strongest antioxidant activities (EC50 values: 1.5–2.3 and 1.0–1.7 mg mL−1 for DPPH and ABTS, respectively), being at least 1.3-fold higher than those of the crude EO. The presence of nezukol, elemol, and eudesmol isomers could strongly contribute to the best free radical scavenging properties of Frs. 4–6. In conclusion, HD was found to be an efficient process for obtaining new Az–CJF EO fractions with variable and enhanced bioactivities due to their differential composition, as assessed using GC–MS. Hence, these findings could contribute to increasing the commercial potential of the C. japonica EO industry, namely, the Fr2 and Fr6, which presented the most significant activities and can have potential applications in the food, medical, and agriculture sectors.
Enhancing Micropropagation of Adenophora liliifolia: Insights from PGRs, Natural Extracts, and pH Optimization
Authors: Kovács Zsófia , Portocarrero Liz Kelly , Honfi Péter , Kohut Ildikó , Eisa Eman Abdelhakim , Tilly-Mándy Andrea
Published: June 2024

The endangered plant species Adenophora liliifolia faces threats to its survival in the wild, necessitating the development of effective micropropagation techniques for potential reintroduction efforts. This study demonstrates that Adenophora liliifolia effectively reproduces on MS synthetic medium with diverse plant growth regulators (PGR) and natural extracts, facilitating swift micropropagation for potential future reintroduction endeavors. It highlights the substantial impact of PGR composition and natural extracts on the growth and development of A. liliifolia. The ideal growth medium for A. liliifolia was determined to be ½ MS with specific treatments. Additionally, incorporating silver nitrate (AgNO3) at 5 mg L−1 into the medium led to enhanced root formation and shoot length, albeit excessive concentrations adversely affected root development. Varying concentrations of NAA significantly affected different plant growth parameters, with the 0.1 mg L−1 treatment yielding comparable plant height to the control. Moreover, 50 mL L−1 of coconut water bolstered root formation, while 200 mL L−1 increased shoot formation during in vitro propagation. However, elevated doses of coconut water (CW) impeded root development but stimulated shoot growth. Experiments measuring chlorophyll a + b and carotenoid content indicated higher concentrations in the control group than differing levels of applied coconut water. Optimizing pH levels from 6.8–7 to 7.8–8.0 notably enhanced plant height and root formation, with significant carotenoid accumulation observed at pH 6.8–7. Soil samples from A. liliifolia’s natural habitat exhibited a pH of 6.65. Ultimately, the refined in vitro propagation protocol effectively propagated A. liliifolia, representing a pioneering effort and setting the stage for future restoration initiatives and conservation endeavors.
Comprehensive Modulation of Secondary Metabolites in Terpenoid-Accumulating Mentha spicata L. via UV Radiation
Authors: Crestani Gaia , Večeřová Kristýna , Cunningham Natalie , Badmus Uthman O. , Urban Otmar , Jansen Marcel A. K.
Published: June 2024

In plants, secondary metabolites change in response to environmental conditions. These changes co-regulate resilience to stressful environmental conditions, plant growth and development, and interactions between plants and the wider ecosystem, while also affecting soil carbon storage and atmospheric and climatic conditions. The objective of this study was to determine the association between UV exposure and the contents of key metabolites, including amino acids, phenolics, flavonoids, terpenoids, carotenoids, tocopherols, and phytosterols. Mentha spicata plantlets were grown in tissue culture boxes for 30 days and then exposed to a low dose of broadband UV-B (291–315 nm; 2.8 kJm−2 biologically effective UV) enriched light for eight days. Metabolite contents were quantified either immediately after the final UV exposure, or after seven days of recovery under photosynthetically active radiation. It was found that UV promoted the production of flavonoids (1.8-fold) ahead of phenolic acids (unchanged). Furthermore, the majority of monoterpenes and sesquiterpenes, constituents of valuable mint essential oil, were significantly increased through UV treatment (up to 90-fold for α-linalool). In contrast, the contents of carotenoids and tocopherols did not increase following UV exposure. A comparison between plants sampled immediately after UV exposure and after seven days of recovery showed that there was an overall increase in the content of carotenoids, mono- and sesquiterpenes, phenolics, and amino acids following recovery, while the contents of sterols and tocopherols decreased. These UV-induced changes in metabolite profile may have important consequences for agriculture, ecology, and even the global climate, and they also provide an exciting opportunity to enhance crop value, facilitating the development of improved products with higher levels of essential oils and added benefits of enhanced flavour, colour, and bioactive content.
Variation in Seed Morphological Traits Affects the Dispersal Strategies of Chromolaena odorata Following Invasion
Authors: Li Yangping , Wang Guofen , Geng Yupeng , Li Ju , Feng Yulong
Published: June 2024

Seed germination and dispersal have an important impact on the establishment and spread of invasive plants. Understanding the extent of intraspecific seed trait variations can enhance our understanding of how invasive plants respond to environmental change after introduction and help predict the dynamic of invasive species under future environmental conditions. However, less attention has been given to the variation in seed traits within species as opposed to among species. We compared seed production, seed morphological traits, dispersal ability, and seedling performance of Chromolaena odorata from 10 introduced populations in Asia and 12 native populations in America in a common garden. The results showed that range (introduced vs. native) and climate affected these traits. Compared with the native population, the introduced populations had higher seed numbers per capitula, lighter seeds, and higher potential dispersal ability seeds (lower terminal velocity) but lower germination rates and seedling lengths. Climatic clines in seed numbers per capitula and pappus length were observed; however, the clines in pappus length differed between the introduced and native populations. Trait covariation patterns were also different between both ranges. In the native populations, there was a trade-off between seed numbers per capitula and seed mass, while this relationship was not found for the introduced populations. These results indicate that C. odorata alters the ecological strategy of seed following invasion, which facilitates its establishment and fast dispersal and contributes to successful invasion in the introduced ranges.
Reduce Product Surface Quality Risks by Adjusting Processing Sequence: A Hot Rolling Scheduling Method
Authors: Jiang Tianru , Zhang Nan , Xie Yongyi , Lv Zhimin
Published: June 2024

The hot rolled strip is a basic industrial product whose surface quality is of utmost importance. The condition of hot rolling work rolls that have been worn for a long time is the key factor. However, the traditional scheduling method controls risks to the surface quality by setting fixed rolling length limits and penalty scores, ignoring the wear condition differences caused by various products. This paper addresses this limitation by reconstructing a hot rolling-scheduling model, after developing a model for pre-assessment of the risk to surface quality based on the Weibull failure function, the deformation resistance formula, and real production data from a rolling plant. Additionally, Ant Colony Optimization (referred to as ACO) is employed to implement the scheduling model. The simulation results of the experiments demonstrate that, compared to the original scheduling method, the proposed one significantly reduces the cumulative risk of surface defects on products. This highlights the efficacy of the proposed method in improving scheduling decisions and surface quality of hot rolled strips.
Efficient Use of Secondary Raw Material from the Production of Polyamide Construction Products
Authors: Olszewski Tomasz , Matykiewicz Danuta , Barczewski Mateusz , Andrzejewski Jacek
Published: June 2024

This study aimed to assess the possibility of using post-production waste and the impact of the conditioning method on the mechanical and thermomechanical properties of polyamide injection molded parts. Samples containing 5, 10, and 15 wt.% of ground post-production waste were produced using injection molding technology. The rheological properties by oscillatory rheometry, the melt mass flow rate (MFR), and the thermal stability by thermogravimetric analysis (TGA) of polymer mixtures containing recycled fraction were determined. The samples were conditioned under the following conditions: 24 h and 14 days in distilled water, in a climatic chamber, and aged in a xenon-light-accelerated aging chamber. Then, the impact and static tensile strength and heat deflection temperature (HDT) were assessed. The results show that the addition of post-production waste in the form of grinding does not significantly affect the mechanical and thermomechanical properties of the finished products. This research provides valuable information regarding the possibility of using secondary materials for manufacturing high-performance construction products. Moreover, it was proven that the process of conditioning polyamide samples in a climatic chamber was the most effective and significantly increased the impact strength of the tested material.
Towards an All-Ireland Diamond Open Access Publishing Platform: The PublishOA.ie Project—2022–2024
Authors: Mahony Jane
Published: June 2024

The Government of Ireland has set a target of achieving 100% open access to publicly funded scholarly publications by 2030. As a key element of achieving this objective, the PublishOA.ie project was established to evaluate the feasibility of establishing an all-island [Republic of Ireland and Northern Ireland] digital publishing platform for Diamond Open Access journals and monographs designed to advance best practice and meet the needs of authors, readers, publishers, and research funding organisations in Irish scholarly publishing. It should be noted in this context that there is substantial ‘north–south’ cooperation between public bodies in the Republic of Ireland and Northern Ireland in the United Kingdom, some of whom operate on what is commonly termed an ‘all-island’ basis. The project commenced in November 2022 and will run until November 2024, with the submission of a Final Report. This article originated as an interim project report presented in September 2023 at the PubMet2023 conference in Zadar, Croatia. The project is unique in its mandate to report on the feasibility of a shared platform that will encompass scholarly publishing across the two jurisdictions on the island of Ireland, which are now, post-Brexit, inside and outside the European Union (EU): the Republic of Ireland and Northern Ireland in the United Kingdom. The project is co-led by the Royal Irish Academy (RIA), Ireland’s leading body of experts in the Sciences and Humanities, and the Trinity Long Room Hub Arts & Humanities Research Institute of Trinity College Dublin. There are sixteen partners and affiliates from universities and organisations from the island of Ireland. The feasibility study will be based on a review of the publishing practices in the island of Ireland, with gap analysis on standards, technology, processes, copyright practices, and funding models for Diamond OA, benchmarking against other national platforms, and specifications of the requirements, leading to the delivery of a pilot national publishing platform. A set of demonstrator journals and monographs will be published using the platform, which will be actively trialled by the partner publishers and authors. PublishOA.ie aims to deliver an evidence-based understanding of Irish scholarly publishing and of the requirements of publishers to transition in whole or in part to Diamond OA. This paper provides an interim report on progress on the project as of September 2023, ten months after its commencement.
Inference for the Parameters of a Zero-Inflated Poisson Predictive Model
Authors: Deng Min , Aminzadeh Mostafa S. , So Banghee
Published: June 2024

In the insurance sector, Zero-Inflated models are commonly used due to the unique nature of insurance data, which often contain both genuine zeros (meaning no claims made) and potential claims. Although active developments in modeling excess zero data have occurred, the use of Bayesian techniques for parameter estimation in Zero-Inflated Poisson models has not been widely explored. This research aims to introduce a new Bayesian approach for estimating the parameters of the Zero-Inflated Poisson model. The method involves employing Gamma and Beta prior distributions to derive closed formulas for Bayes estimators and predictive density. Additionally, we propose a data-driven approach for selecting hyper-parameter values that produce highly accurate Bayes estimates. Simulation studies confirm that, for small and moderate sample sizes, the Bayesian method outperforms the maximum likelihood (ML) method in terms of accuracy. To illustrate the ML and Bayesian methods proposed in the article, a real dataset is analyzed.
Reinforcement Learning and Genetic Algorithm-Based Network Module for Camera-LiDAR Detection
Authors: Kim Taek-Lim , Park Tae-Hyoung
Published: June 2024

Cameras and LiDAR sensors have been used in sensor fusion for robust object detection in autonomous driving. Object detection networks for autonomous driving are often trained again by adding or changing datasets aimed at robust performance. Repeat training is necessary to develop an efficient network module. Existing efficient network module development changes to hand design and requires much module design experience. For this, a neural architecture search was designed, but it takes much time and requires optimizing the design process. To solve this problem, we propose a two-stage optimization method for the offspring generation process in a neural architecture search based on reinforcement learning. In addition, we propose utilizing two split datasets to solve the fast convergence problem as the objective function of the genetic algorithm: source data (daytime, sunny) and target data (day/night, adversary weather). The proposed method is an efficient module generation method requiring less time than the NSGA-NET. We confirmed the performance improvement and the convergence speed reduction using the Dense dataset. Through experiments, it was proven that the proposed method generated an efficient module.
A Novel Multi-Beam SAR Two-Dimensional Ambiguity Suppression Method Based on Azimuth Phase Coding
Authors: Xu Yihao , Zhang Fubo , Li Wenjie , Wan Yangliang , Chen Longyong , Jiang Tao
Published: June 2024

In order to address the problems of range ambiguity and azimuth ambiguity in the wide-swath imaging of synthetic aperture radar (SAR), this paper proposes a multi-beam SAR two-dimensional ambiguity suppression method based on azimuth phase coding (APC). The scheme employs an elevation simultaneous multi-beam transmission system with azimuth under-sampling, transmitting different APC waveforms to various range-ambiguous sub-regions. After receiving the echoes, the azimuth digital beamforming (DBF) is used to separate the APC waveform echoes with multi-order Doppler ambiguity, achieving azimuth reconstruction and range ambiguity suppression simultaneously. Finally, the elevation nulling DBF is used to further suppress range ambiguity and obtain the SAR wide-swath image. The superiority of this scheme is reflected in the following aspects: the azimuth DBF simultaneously suppresses azimuth and range ambiguity, the influence of height fluctuations on the ability to suppress range ambiguity is weakened, the use of elevation nulling DBF further enhances the level of range ambiguity suppression, and different range sub-regions can adopt different range resolutions and working modes. The feasibility of this scheme is verified through theoretical analysis and simulation.
Inversion and Analysis of Global Ocean Chlorophyll-a Concentration Based on Temperature Zoning
Authors: He Yanbo , Leng Liang , Ji Xue , Wang Mingchang , Huo Yanping , Li Zheng
Published: June 2024

In recent years, the frequent occurrence of eutrophication problems in water bodies has been caused by changes in the climate environment and overexploitation of natural resources by humans. Chlorophyll-a, as a key indicator for water body assessment, plays an important role in eutrophication research and has a profound impact on the global biogeochemical cycle of the climate process. Studies have shown that temperature can directly or indirectly affect the concentration of chlorophyll-a by influencing the growth of algae and water quality indicators in water bodies. Considering the temperature factor in the inversion of chlorophyll-a concentration is a novel research approach. Based on the influence of temperature on chlorophyll-a concentration, we propose the idea of inverting global ocean chlorophyll-a concentration based on temperature zoning. Using monthly average remote sensing reflectance data from VIIRS (Visible and Infrared Imaging Radiometer Suite), combined with the results of temperature zoning, the OC3V(SST) model was constructed to invert the monthly average chlorophyll-a concentration in the global ocean in October 2018. The OC3V(SST) model has been validated by applying it to the remaining 11 months of January, April, July, and October in 2017, 2018, and 2019, as well as the entire 31-day dataset of October 2018. The results indicate that temperature zonation can significantly improve the inversion accuracy of chlorophyll-a and further explore the spatial distribution patterns of global chlorophyll-a concentrations across various temperature ranges based on monthly averages from the global ocean. Additionally, the study investigates the continuity issues of various models and the correlation between temperature and chlorophyll-a.
Retrospective Analysis of Glacial Lake Outburst Flood (GLOF) Using AI Earth InSAR and Optical Images: A Case Study of South Lhonak Lake, Sikkim
Authors: Yu Yang , Li Bingquan , Li Yongsheng , Jiang Wenliang
Published: June 2024

On 4 October 2023, a glacier lake outburst flood (GLOF) occurred at South Lhonak Lake in the northwest of Sikkim, India, posing a severe threat to downstream lives and property. Given the serious consequences of GLOFs, understanding their triggering factors is urgent. This paper conducts a comprehensive analysis of optical imagery and InSAR deformation results to study changes in the surrounding surface of the glacial lake before and after the GLOF event. To expedite the processing of massive InSAR data, an InSAR processing system based on the SBAS-InSAR data processing flow and the AI Earth cloud platform was developed. Sentinel-1 SAR images spanning from January 2021 to March 2024 were used to calculate surface deformation velocity. The evolution of the lake area and surface variations in the landslide area were observed using optical images. The results reveal a significant deformation area within the moraine encircling the lake before the GLOF, aligning with the area where the landslide ultimately occurred. Further research suggests a certain correlation between InSAR deformation results and multiple factors, such as rainfall, lake area, and slope. We speculate that heavy rainfall triggering landslides in the moraine may have contributed to breaching the moraine dam and causing the GLOF. Although the landslide region is relatively stable overall, the presence of a crack in the toparea of landslide raises concerns about potential secondary landslides. Our study may improve GLOF risk assessment and management, thereby mitigating or preventing their hazards.
Thickness Measurements with EMAT Based on Fuzzy Logic
Authors: Shi Yingjie , Tian Shihui , Jiang Jiahong , Lei Tairan , Wang Shun , Lin Xiaobo , Xu Ke
Published: June 2024

Metal thickness measurements are essential in various industrial applications, yet current non-contact ultrasonic methods face limitations in range and accuracy, hindering the widespread adoption of electromagnetic ultrasonics. This study introduces a novel combined thickness measurement method employing fuzzy logic, with the aim of broadening the applicational scope of the EMAT. Leveraging minimal hardware, this method utilizes the short pulse time-of-flight (TOF) technique for initial thickness estimation, followed by secondary measurements guided by fuzzy logic principles. The integration of measurements from the resonance, short pulse echo, and linear frequency modulation echo extends the measurement range while enhancing accuracy. Rigorous experimental validation validates the method’s effectiveness, demonstrating a measurement range of 0.3–1000.0 mm with a median error within ±0.5 mm. Outperforming traditional methods like short pulse echoes, this approach holds significant industrial potential.
Data Clustering Utilization Technologies Using Medians of Current Values for Improving Arc Sensing in Unstructured Environments
Authors: Kim Hee-Jun , Kim Jeong-Ho , Heo Shin-Nyeong , Jeon Do-Hyung , Kim Won-Suk
Published: June 2024

In the shipbuilding industry, welding automation using welding robots often relies on arc-sensing techniques due to spatial limitations. However, the reliability of the feedback current value, core sensing data, is reduced when welding target workpieces have significant curvature or gaps between curved workpieces due to the control of short-circuit transition, leading to seam tracking failure and subsequent damage to the workpieces. To address these problems, this study proposes a new algorithm, MBSC (median-based spatial clustering), based on the DBSCAN (density-based spatial clustering of applications with noise) clustering algorithm. By performing clustering based on the median value of data in each weaving area and considering the characteristics of the feedback current data, the proposed technique utilizes detected outliers to enhance seam tracking accuracy and responsiveness in unstructured and challenging welding environments. The effectiveness of the proposed technique was verified through actual welding experiments in a yard environment.
Research on Three-Dimensional Reconstruction of Ribs Based on Point Cloud Adaptive Smoothing Denoising
Authors: Zhu Darong , Wang Diao , Chen Yuanjiao , Xu Zhe , He Bishi
Published: June 2024

The traditional methods for 3D reconstruction mainly involve using image processing techniques or deep learning segmentation models for rib extraction. After post-processing, voxel-based rib reconstruction is achieved. However, these methods suffer from limited reconstruction accuracy and low computational efficiency. To overcome these limitations, this paper proposes a 3D rib reconstruction method based on point cloud adaptive smoothing and denoising. We converted voxel data from CT images to multi-attribute point cloud data. Then, we applied point cloud adaptive smoothing and denoising methods to eliminate noise and non-rib points in the point cloud. Additionally, efficient 3D reconstruction and post-processing techniques were employed to achieve high-accuracy and comprehensive 3D rib reconstruction results. Experimental calculations demonstrated that compared to voxel-based 3D rib reconstruction methods, the 3D rib models generated by the proposed method achieved a 40% improvement in reconstruction accuracy and were twice as efficient as the former.
Towards Reliability in Smart Water Sensing Technology: Evaluating Classical Machine Learning Models for Outlier Detection
Authors: Lamrini Mimoun , Ben Mahria Bilal , Chkouri Mohamed Yassin , Touhafi Abdellah
Published: June 2024

In recent years, smart water sensing technology has played a crucial role in water management, addressing the pressing need for efficient monitoring and control of water resources analysis. The challenge in smart water sensing technology resides in ensuring the reliability and accuracy of the data collected by sensors. Outliers are a well-known problem in smart sensing as they can negatively affect the viability of useful analysis and make it difficult to evaluate pertinent data. In this study, we evaluate the performance of four sensors: electrical conductivity (EC), dissolved oxygen (DO), temperature (Temp), and pH. We implement four classical machine learning models: support vector machine (SVM), artifical neural network (ANN), decision tree (DT), and isolated forest (iForest)-based outlier detection as a pre-processing step before visualizing the data. The dataset was collected by a real-time smart water sensing monitoring system installed in Brussels’ lakes, rivers, and ponds. The obtained results clearly show that the SVM outperforms the other models, showing 98.38% F1-score rates for pH, 96.98% F1-score rates for temp, 97.88% F1-score rates for DO, and 98.11% F1-score rates for EC. Furthermore, ANN also achieves a significant results, establishing it as a viable alternative.
Quartz Enhanced Photoacoustic Spectroscopy on Solid Samples
Authors: Falkhofen Judith , Bahr Marc-Simon , Baumann Bernd , Wolff Marcus
Published: June 2024

Quartz-Enhanced Photoacoustic Spectroscopy (QEPAS) is a technique in which the sound wave is detected by a quartz tuning fork (QTF). It enables particularly high specificity with respect to the excitation frequency and is well known for an extraordinarily sensitive analysis of gaseous samples. We have developed the first photoacoustic (PA) cell for QEPAS on solid samples. Periodic heating of the sample is excited by modulated light from an interband cascade laser (ICL) in the infrared region. The cell represents a half-open cylinder that exhibits an acoustical resonance frequency equal to that of the QTF and, therefore, additionally amplifies the PA signal. The antinode of the sound pressure of the first longitudinal overtone can be accessed by the sound detector. A 3D finite element (FE) simulation confirms the optimal dimensions of the new cylindrical cell with the given QTF resonance frequency. An experimental verification is performed with an ultrasound micro-electromechanical system (MEMS) microphone. The presented frequency-dependent QEPAS measurement exhibits a low noise signal with a high-quality factor. The QEPAS-based investigation of three different solid synthetics resulted in a linearly dependent signal with respect to the absorption.
Research on FBG Tactile Sensing Shape Recognition Based on Convolutional Neural Network
Authors: Lu Guan , Shen Zhihui , Cai Ting , Xu Yiming
Published: June 2024

Shape recognition plays a significant role in the field of robot perception. In view of the low efficiency and few types of shape recognition of the fiber tactile sensor applied to flexible skin, a convolutional-neural-network-based FBG tactile sensing array shape recognition method was proposed. Firstly, a sensing array was fabricated using flexible resin and 3D printing technology. Secondly, a shape recognition system based on the tactile sensing array was constructed to collect shape data. Finally, shape classification recognition was performed using convolutional neural network, random forest, support vector machine, and k-nearest neighbor. The results indicate that the tactile sensing array exhibits good sensitivity and perception capability. The shape recognition accuracy of convolutional neural network is 96.58%, which is 6.11%, 9.44%, and 12.01% higher than that of random forest, k-nearest neighbor, and support vector machine. Its F1 is 96.95%, which is 6.3%, 8.73%, and 11.94% higher than random forest, k-nearest neighbor, and support vector machine. The research of FBG shape sensing array based on convolutional neural network provides an experimental basis for shape perception of flexible tactile sensing.
Monocular Depth Estimation via Self-Supervised Self-Distillation
Authors: Hu Haifeng , Feng Yuyang , Li Dapeng , Zhang Suofei , Zhao Haitao
Published: June 2024

Self-supervised monocular depth estimation can exhibit excellent performance in static environments due to the multi-view consistency assumption during the training process. However, it is hard to maintain depth consistency in dynamic scenes when considering the occlusion problem caused by moving objects. For this reason, we propose a method of self-supervised self-distillation for monocular depth estimation (SS-MDE) in dynamic scenes, where a deep network with a multi-scale decoder and a lightweight pose network are designed to predict depth in a self-supervised manner via the disparity, motion information, and the association between two adjacent frames in the image sequence. Meanwhile, in order to improve the depth estimation accuracy of static areas, the pseudo-depth images generated by the LeReS network are used to provide the pseudo-supervision information, enhancing the effect of depth refinement in static areas. Furthermore, a forgetting factor is leveraged to alleviate the dependency on the pseudo-supervision. In addition, a teacher model is introduced to generate depth prior information, and a multi-view mask filter module is designed to implement feature extraction and noise filtering. This can enable the student model to better learn the deep structure of dynamic scenes, enhancing the generalization and robustness of the entire model in a self-distillation manner. Finally, on four public data datasets, the performance of the proposed SS-MDE method outperformed several state-of-the-art monocular depth estimation techniques, achieving an accuracy (δ1) of 89% while minimizing the error (AbsRel) by 0.102 in NYU-Depth V2 and achieving an accuracy (δ1) of 87% while minimizing the error (AbsRel) by 0.111 in KITTI.
A Novel Cost Calculation Method for Manipulator Trajectory Planning
Authors: Fu Leiyang , Li Shaowen
Published: June 2024

It is worthwhile to calculate the execution cost of a manipulator for selecting a planning algorithm to generate trajectories, especially for an agricultural robot. Although there are various off-the-shelf trajectory planning methods, such as pursuing the shortest stroke or the smallest time cost, they often do not consider factors synthetically. This paper uses the state-of-the-art Python version of the Robotics Toolbox for manipulator trajectory planning instead of the traditional D–H method. We propose a cost function with mass, iteration, and residual to assess the effort of a manipulator. We realized three inverse kinematics methods (NR, GN, and LM with variants) and verified our cost function’s feasibility and effectiveness. Furthermore, we compared it with state-of-the-art methods such as Double A* and MoveIt. Results show that our method is valid and stable. Moreover, we applied LM (Chan λ = 0.1) in mobile operation on our agricultural robot platform.
Assessing the Impact of Neuromuscular Electrical Stimulation-Based Fingerboard Training versus Conventional Fingerboard Training on Finger Flexor Endurance in Intermediate to Advanced Sports Climbers: A Randomized Controlled Study
Authors: Dindorf Carlo , Dully Jonas , Berger Joshua , Becker Stephan , Wolf Emanuel , Simon Steven , Bartaguiz Eva , Kemmler Wolfgang , Fröhlich Michael
Published: June 2024

Competitive climbers engage in highly structured training regimens to achieve peak performance levels, with efficient time management as a critical aspect. Neuromuscular electrical stimulation (NMES) training can close the gap between time-efficient conditioning training and achieving optimal prerequisites for peak climbing-specific performances. Therefore, we examined potential neuromuscular adaptations resulting from the NMFES intervention by analyzing the efficacy of twice-weekly NMES-supported fingerboard (hang board) training compared with thrice-weekly conventional fingerboard training over 7 training weeks in enhancing climbing-specific endurance among intermediate to advanced climbers. Participants were randomly divided into the NMES and control groups. Eighteen participants completed the study (14 male, 4 female; mean age: 25.7 ± 5.3 years; mean climbing experience: 6.4 ± 3.4 years). Endurance was assessed by measuring the maximal time athletes could support their body weight (hanging to exhaustion) on a 20 mm-deep ledge at three intervals: pre-, in-between- (after 4 weeks of training), and post-training (after 7 weeks of training). The findings revealed that despite the lower training volume in the NMES group, no significant differences were observed between the NMES and control groups in climbing-specific endurance. Both groups exhibited notable improvements in endurance, particularly after the in-between test. Consequently, a twice-weekly NMES-supported fingerboard training regimen demonstrated non-inferiority to a thrice-weekly conventional training routine. Incorporating NMES into fingerboard workouts could offer time-saving benefits.
CoFormerNet: A Transformer-Based Fusion Approach for Enhanced Vehicle-Infrastructure Cooperative Perception
Authors: Li Bin , Zhao Yanan , Tan Huachun
Published: June 2024

Vehicle–infrastructure cooperative perception is becoming increasingly crucial for autonomous driving systems and involves leveraging infrastructure’s broader spatial perspective and computational resources. This paper introduces CoFormerNet, which is a novel framework for improving cooperative perception. CoFormerNet employs a consistent structure for both vehicle and infrastructure branches, integrating the temporal aggregation module and spatial-modulated cross-attention to fuse intermediate features at two distinct stages. This design effectively handles communication delays and spatial misalignment. Experimental results using the DAIR-V2X and V2XSet datasets demonstrated that CoFormerNet significantly outperformed the existing methods, achieving state-of-the-art performance in 3D object detection.
Self-Sensing Electromechanical System Integrated with the Embedded Displacement Sensor
Authors: Wang Shuxian , Liu Shiyou , Su Zuqiang , Liu Linlin , Tang Zhi
Published: June 2024

Conventionally, the electromechanical system requires the installation of auxiliary displacement sensors and only the amount on the drive part and motion end, which increases volume, cost, and measurement error in the system. This paper presents an integrated measurement method with a sensing head, which takes the equal division characteristics of mechanical structures as part of the sensor, thus, the so-called self-sensing system. Moreover, the displacement is measured by counting the time pulses. The sensing head is integrated with the entire electromechanical system, including the driving, transmitting, and moving parts. Thus, the integration of the sensing part is greatly improved. Taking the rotary table as a special example, and the sensing head embedded into each part of the system, displacement information is obtained by the common processing system and fused by the adaptive weighted average method. The results of the experiment show that the fusion precision of each component is higher than only the motor position information as the feedback. The proposed method is a practical self-sensing technology with significant volume reduction and intelligent control benefits in the industry, especially suitable for extremely small and narrow spaces.
An Embedded Electromyogram Signal Acquisition Device
Authors: Lu Changjia , Xu Xin , Liu Yingjie , Li Dan , Wang Yue , Xian Wenhao , Chen Changbing , Wei Baichun , Tian Jin
Published: June 2024

In this study, we design an embedded surface EMG acquisition device to conveniently collect human surface EMG signals, pursue more intelligent human–computer interactions in exoskeleton robots, and enable exoskeleton robots to synchronize with or even respond to user actions in advance. The device has the characteristics of low cost, miniaturization, and strong compatibility, and it can acquire eight-channel surface EMG signals in real time while retaining the possibility of expanding the channel. This paper introduces the design and function of the embedded EMG acquisition device in detail, which includes the use of wired transmission to adapt to complex electromagnetic environments, light signals to indicate signal strength, and an embedded processing chip to reduce signal noise and perform filtering. The test results show that the device can effectively collect the original EMG signal, which provides a scheme for improving the level of human–computer interactions and enhancing the robustness and intelligence of exoskeleton equipment. The development of this device provides a new possibility for the intellectualization of exoskeleton systems and reductions in their cost.
Collective Memory, Visual Communication, and Symbolic Interactions with Statues: The Case of the Charging Bull of Wall Street
Authors: Mubasher Annosha , Liao Tim F.
Published: June 2024

This research is concerned with individuals’ symbolic interactions with the Charging Bull statue. We chose this statue because it is an extremely popular tourist attraction and the most visited nonhuman statue in New York City. However, there is no research devoted to understanding how visitors interact with the statue. To fill this gap in the literature, we examine people’s interactions as a unique way of communicating with the Charging Bull statue in New York City through a photo analysis approach. Using the general framework of symbolic interactionism, the paper investigates poses, gestures, and more generally interactions of individuals near or with the statue to gain insights into the impact of the Charging Bull beyond a mere visual representation. The findings reveal various forms of interaction, including souvenir photography, active participation, unexpected interactions, and public-made symbolism. This study of these interactions contributes to the literature on collective memory and the study of statues. Additionally, this research highlights the inventiveness of the dynamic relationship between individuals and statues as symbols in the urban environment.
Using Date Palm Residues to Improve Soil Properties: The Case of Compost and Biochar
Authors: Kavvadias Victor , Le Guyader Elie , El Mazlouzi Mohamed , Gommeaux Maxime , Boumaraf Belkacem , Moussa Mohamed , Lamine Hafouda , Sbih Mahtali , Zoghlami Ines Rahma , Guimeur Kamel , Tirichine Aissa , Adelfettah Abid , Marin Beatrice , Morvan Xavier
Published: June 2024

Agricultural residues are generated during the production and processing of agricultural crops. Under modern date palm plantation practices, field operations generate huge quantities of residues, which are discarded with little valorization. The date palm agro-industry produces significant amounts of waste. The accumulation of these residues can cause ecological damage to the oasis ecosystems. There is a lack of comprehensive data on long-term research studies that aim to assess the impact of date palm waste management practices. Composting and/or pyrolysis of date palm residues showed benefits for improving soil physical and chemical properties, particularly in sandy soils. This claim holds particular significance for arid and semi-arid regions, which are characterized by low fertility and are susceptible to soil degradation, accentuated by ongoing climate change. This review summarizes the existing literature concerning the valorization of date palm residues with regards to compost and pyrolysis processes, as well as the impact of their application on soil quality. Further research is required to assess the effects of using date palm residues for better soil amendment management. Research should focus on composting and biochar technologies for date palm residues and their application in arid and semi-arid regions to combat soil erosion and degradation. Increasing the beneficial uses of date palm residues could lead to sustainable and economic growth in dry areas.