bluuebunny/crossref_metadata_2025 huggingface dataset:
Highly Efficient Agrobacterium tumefaciens Mediated Transformation of Oil Palm Using an EPSPS-Glyphosate Selection System
Authors: Ouyang Chao , Jin Xiongxia , Guo Qinghui , Luo Shaojie , Zheng Yusheng , Zou Jixin , An Baoguang , Li Dongdong
Published: November 2024

Oil palm (Elaeis guineensis Jacq.) is the most efficient oil-producing crop globally, yet progress in its research has been hampered by the lack of effective genetic transformation systems. The EPSPS gene, encoding 5-enolpyruvylshikimate-3-phosphate synthase, has been used as a transgenic selection marker in various crops, including rice and soybean. This study evaluated the EPSPS/glyphosate selection system for oil palm transformation. We constructed a binary expression vector, pCGlyDESCLI-C, containing the TIPS-EiEPSPS selection marker from goosegrass and the mScarlet-I red fluorescent reporter gene. This vector was introduced into oil palm embryonic callus (EC) via Agrobacterium-mediated transformation. After optimizing the transformation steps, positive calli were obtained, and integration of the foreign gene into the oil palm genome was confirmed through molecular analysis. Notably, the selection efficiency of the EPSPS/glyphosate selection system exceeded that of the traditional hpt/hygromycin selection system, demonstrating its advantages. Our findings support the effectiveness of the TIPS-EiEPSPS/glyphosate selection system for oil palm genetic transformation, marking its first application in this species and offering a valuable tool for advancing research on this economically significant crop.
Unraveling Quinoa (Chenopodium quinoa Willd.) Defense Against Downy Mildew (Peronospora variabilis): Comparative Molecular Analysis of Resistant “Hualhuas” and Susceptible “Real” Cultivars
Authors: Khalifa Walaa , Khalil Hala Badr , Thabet Marian
Published: November 2024

Quinoa (Chenopodium quinoa Willd.) is a new, promising non-conventional useful crop; however, its susceptibility to downy mildew, caused by Peronospora variabilis, is a key obstacle limiting its productivity in Egypt. Identifying and utilizing resistant quinoa cultivars appear to be reliable and cost-efficient strategies for controlling downy mildew, particularly in resource-limited farmers’ fields. This study aimed to evaluate the differential resistance of the Peruvian “Hualhuas” and Bolivian “Real” quinoa cultivars to P. variabilis infection under laboratory conditions to provide precise insight into their basic defense mechanism(s). Inoculated “Hualhuas” plants displayed complete resistance against P. variabilis, with no visible symptoms (incompatible reaction), while those of “Real” plants revealed high susceptibility (compatible reaction), with typical downy mildew lesions on their leaf surfaces. Disease incidence reached about 66% in the inoculated “Real” plants, with most inoculated leaves having lesions of grades 4 and 5 covering up to 90% of their leaf surfaces. Susceptibility indices reached up to 66% in the inoculated “Real” plants. Resistance to P. variabilis observed in the “Hualhuas” plants may have been largely attributed to elevated endogenous H2O2 levels, increased peroxidase (POX) activity and abundance, enhanced phenylalanine ammonia-lyase (PAL) activity and expression, as well as the upregulation of the pathogen-related protein 10 gene (PR-10). The results of this study indicate that the quinoa cultivar “Hualhuas” not only is a promising candidate for sustainable control of quinoa downy mildew but also, through a deep understanding of its molecular resistance mechanisms, would provide a possible route to enhance downy mildew resistance in other genotypes.
Micro-Injection Molding of Carbon-Fiber-Reinforced Plastic (CFRP)/Polymethyl Methacrylate (PMMA) Composite Components
Authors: Xiao Yingying , Xu Bin , Zhao Hang , Zhu Likuan , Lei Jianguo
Published: November 2024

CFRP exhibits a low specific gravity, good rigidity, and high strength and is widely used in the automobile, aerospace, and biomedical fields. Against this background, the demand for composite components prepared using CFRP and polymers has increased. The service life of composite components is closely related to the bonding strength between the CFRP and the polymer. Here, using CFRP and polymethyl methacrylate (PMMA) as raw materials, composite components were prepared via injection molding. First, micro-grooves were produced on the CFRP surface using the hot-pressing technique. Subsequently, the melted PMMA was filled in these micro-grooves using injection molding, thereby forming the bonding interface of the composite components. These micro-grooves can increase the contact area between CFRP and PMMA, thereby enhancing the bonding strength of the CRFP and PMMA interface. In this study, a single-factor experiment was used to explore the influence of each process parameter on the tensile strength of the composite components. Finally, after optimizing process parameters, the composite components with tensile strength of 10.72 MPa were obtained.
Structural Optimization of a High-Performance Green Sandwich Made of Sisal Reinforced Epoxy Facings and Balsa Core
Authors: Zuccarello Bernardo , Bongiorno Francesco , Militello Carmelo
Published: November 2024

Within the range of composite laminates for structural applications, sandwich laminates are a special category intended for applications characterized by high flexural stresses. As it is well known from the technical literature, structural sandwich laminates have a simple configuration consisting of two skins of very strong material, to which the flexural strength is delegated, between which an inner layer (core) of light material with sufficient shear strength is interposed. As an example, a sandwich configuration widely used in civil, naval, and mechanical engineering is that obtained with fiberglass skins and a core of various materials, such as polyurethane foam or another lightweight material, depending on the application. Increasingly stringent regulations aimed at protecting the environment by reducing harmful emissions of carbon dioxide and carbon monoxide have directed recent research towards the development of new composites and new sandwiches characterized by low environmental impact. Among the various green composite solutions proposed in the literature, a very promising category is that of high-performance biocomposites, which use bio-based matrices reinforced by fiber reinforcements. This approach can also be used to develop green sandwiches for structural applications, consisting of biocomposite skins and cores made by low-environmental impact or renewable materials. In order to make a contribution to this field, a structural sandwich consisting of high-performance sisal–epoxy biocomposite skins and an innovative renewable core made of balsa wood laminates with appropriate lay-ups has been developed and then properly characterized in this work. Through a systematic theoretical–experimental analysis of three distinct core configurations, the unidirectional natural core, the cross-ply type, and the angle-ply type, it has been shown how the use of natural balsa gives rise to inefficient sandwiches, whereas performance optimization is fully achieved by considering the angle-ply core type [±45/90]. Finally, the subsequent comparison with literature data of similar sandwiches has shown how the optimal configuration proposed can be advantageously used to replace synthetic glass–resin sandwiches widely used in various industrial sectors (mechanical engineering, shipbuilding, etc.) and in civil engineering.
Recent Advances in Fire Safety of Carbon Fiber-Reinforced Epoxy Composites for High-Pressure Hydrogen Storage Tanks
Authors: Dagdag Omar , Kim Hansang
Published: November 2024

The increasing use of hydrogen as a clean energy carrier has underscored the necessity for advanced materials that can provide safe storage under extreme conditions. Carbon fiber-reinforced epoxy (CFRP) composites are increasingly utilized in various high-performance applications, including automotive, aerospace, and particularly hydrogen storage tanks, due to their exceptional strength-to-weight ratio, durability, excellent corrosion resistance, and low thermal conductivity. However, the inherent flammability of epoxy matrices poses significant safety concerns, particularly in hydrogen storage, where safety is paramount. This review paper provides a comprehensive overview of the recent progress in enhancing the fire safety of CFRP. The focus is on innovative strategies such as developing novel flame-retardant (FR) additives, intumescent coatings, and nanomaterial reinforcements. It analyzes the effectiveness of these strategies in improving the fire performance of CFRP composites, including their flame retardancy, smoke suppression, and heat release rate reduction. The review paper also explores the application of fire modeling tools to predict the fire behavior of CFRP composite hydrogen storage tanks under various fire scenarios. Additionally, the review discusses the implications of these advancements on the performance and safety of hydrogen storage tanks, highlighting both the progress made and the challenges that remain.
A Machine Vision-Based Method of Impurity Detection for Rapeseed Harvesters
Authors: Chen Xu , Guan Zhuohuai , Li Haitong , Zhang Min
Published: November 2024

The impurity rate is one of the core indicators for evaluating the quality of rapeseed combine harvesters. It directly affects the economic value of rapeseed. At present, the impurity rate of rapeseed combine harvesters mainly relies on manual detection during shutdown, which cannot be monitored in real time. Due to the lack of accurate real-time impurity rate data, the operation parameters of rapeseed harvesters mainly depend on the driver’s experience, which results in large fluctuations in field harvest quality. In this research, a machine vision-based method of impurity detection for rapeseed harvesters, including an image acquisition device and impurity detection algorithm, was developed. The image acquisition device is equipped with a direct-current light source, a conveyor belt, and an industrial camera for taking real-time images of rapeseed samples. Based on the color and shape characteristics of impurity and rapeseed, the detection of rapeseed and impurity was achieved. A quantitative model for the rapeseed impurity rate was constructed to calculate the real-time impurity rate of machine-harvested rapeseed accurately. The field experiment showed that the average accuracy of the detection system for the impurity rate in rapeseed was 86.36% compared with the manual detection data. The impurity detection system proposed in this paper can swiftly and effectively identify rapeseed and impurity and accurately calculate the impurity rate, which can be applied to rapeseed harvesters to provide data support for the adjustment of operating parameters.
Experimental Research on Behavior of Spontaneous Imbibition and Displacement After Fracturing in Terrestrial Shale Oil Based on Nuclear Magnetic Resonance Measurements
Authors: Zhu Jian , Wang Fei , Zhang Shicheng , Ma Xinfang
Published: November 2024

Spontaneous imbibition (SI) effectively enhances oil recovery in shale reservoirs, significantly changing well shut-in and flowback design. This study conducted SI and displacement experiments to simulate the well shut-in and flowback stages so that the mechanism of imbibition and displacement between crude oil and fracture fluid can be discussed. In addition, the relative contribution to oil recovery of different types of pores in various stages and the effect of wettability were determined with low-field nuclear magnetic resonance (LF-NMR) via each sample’s T2 transverse relaxation time at each time. The experimental results show that shale has multiscale pore structure characteristics combined with micropores, small mesopores, and mesopores. During the SI process, crude oil is displaced from micropores by fracture fluid at first, and then a large amount of oil production comes from small mesopores. Oil recovery of water-wet core samples is approximately 40.7%. Oil recovery of oil-wet core samples is about 26%. The wettability significantly affects the imbibition and displacement oil recovery of samples. For the process of SI, oil recovered from small mesopores takes the lead in the complete sample recovery. For the displacement process, oil recovered from small mesopores and mesopores take the lead in the complete sample recovery. After displacement, only 12% of fracture fluid flooded from the samples. This research, demonstrating the imbibition and displacement characteristics of terrestrial shale and several relevant affecting factors, contributes to understanding the fracturing fluid retention mechanism in shale reservoirs and provides crucial theoretical foundations for the development of shale oil reservoirs.
Human and World: Fichte and Wang Yangming on the Highest Good
Authors: Wang Jun
Published: November 2024

In comparative philosophy, the study of Fichte and Wang Yangming has been largely overlooked. This article compares their views on the highest good and their approaches to the human–world relationship. Fichte sees the highest good as the realization of practical reason, achievable through religious faith and love for God, enabling individuals to transcend sensual desires and unify with God. Religious faith connects individuals to a higher existence, fostering moral actions. Wang Yangming, however, defines the highest good as the manifestation of conscience within, emphasizing internal cultivation and moral practice to achieve the unity of knowledge and action. Fichte’s approach relies on religious faith, while Wang Yangming’s focuses on self-cultivation. This study reveals the complementarity between Chinese and Western perspectives on the highest good, offering a new lens for cross-cultural dialogue and insights into the complex relationship between humans and the world.
Kenotic Solidarity in Discernment
Authors: Ryliškytė Ligita
Published: November 2024

This article employs a Christological lens, deeply informed by Bernard J. F. Lonergan’s theology of history and soteriology, to discern the conditions necessary for all-inclusive solidarity. It highlights the twofold kenotic aspect of solidarity, addressing a gap in mainstream theological discourses that often emphasize ‘particular’ solidarity with those in need while neglecting its ‘universal’ (all-inclusive) dimension. Affirming ‘universal’ solidarity necessitates guarding against a misleading notion of neutrality and against totalization, as well as resisting a truncated understanding of intersubjectivity that is prone to group bias. After laying the foundations for understanding solidarity in light of its secular origins and Christian theological context, the article concludes that redemptive solidarity cannot exist without a christomorphic kenosis, which encompasses two incommensurable dimensions: solidarity with victims and solidarity with victimizers as potential converts (that is, as capable of metanoia). In the pursuit of transforming evil into good in history, kenotic solidarity requires prioritizing the common good over personal advantage, even to the extent of refusing to exploit what is (or seems to be) rightfully one’s own—whether privilege, possessions, or the right to strict retribution— just as Christ did when he did not exploit his equality with God to his own advantage (Phil 2:6).
A Preliminary Assessment of Land Restoration Progress in the Great Green Wall Initiative Region Using Satellite Remote Sensing Measurements
Authors: Deng Andy , Hao Xianjun , Qu John J.
Published: November 2024

The Great Green Wall (GGW) initiative, which started in 2007 and is still in development as of 2024, aims to combat desertification and enhance sustainability over 8000 km across Africa’s Sahel-Sahara region, encompassing 11 key countries and 7 countries associated with the initiative. Because of limited ground measurements for the GGW project, the progress and impacts of the GGW initiative have been a challenging problem to monitor and assess. This study aims to utilize satellite remote sensing data to analyze changes in the key factors related to the sustainability of the GGW region, including land cover type, vegetation index, precipitation rate, land surface temperature (LST), surface soil moisture, etc. Results from temporal analysis of these factors indicate that the deserts along the GGW are retreating and the regional mean of the Normalized Difference Vegetation Index (NDVI) has an increasing trend, although the precipitation has a slightly decreasing trend, over the past two decades. Further analysis shows spatial heterogeneity of vegetation, precipitation, and soil moisture changes. Desertification is still a challenging issue in some GGW countries. These results are helpful in understanding climate change in the GGW regions and the impacts of the Great Green Wall initiative.
Attention Swin Transformer UNet for Landslide Segmentation in Remotely Sensed Images
Authors: Liu Bingxue , Wang Wei , Wu Yuming , Gao Xing
Published: November 2024

The development of artificial intelligence makes it possible to rapidly segment landslides. However, there are still some challenges in landslide segmentation based on remote sensing images, such as low segmentation accuracy, caused by similar features, inhomogeneous features, and blurred boundaries. To address these issues, we propose a novel deep learning model called AST-UNet in this paper. This model is based on structure of SwinUNet, attaching a channel Attention and spatial intersection (CASI) module as a parallel branch of the encoder, and a spatial detail enhancement (SDE) module in the skip connection. Specifically, (1) the spatial intersection module expands the spatial attention range, alleviating noise in the image and enhances the continuity of landslides in segmentation results; (2) the channel attention module refines the spatial attention weights by feature modeling in the channel dimension, improving the model’s ability to differentiate targets that closely resemble landslides; and (3) the spatial detail enhancement module increases the accuracy for landslide boundaries by strengthening the attention of the decoder to detailed features. We use the landslide data from the area of Luding, Sichuan to conduct experiments. The comparative analyses with state-of-the-art (SOTA) models, including FCN, UNet, DeepLab V3+, TransFuse, TranUNet, and SwinUNet, prove the superiority of our AST-UNet for landslide segmentation. The generalization of our model is also verified in the experiments. The proposed AST-UNet obtains an F1-score of 90.14%, mIoU of 83.45%, foreground IoU of 70.81%, and Hausdorff distance of 3.73, respectively, on the experimental datasets.
Intelligent QLFEKF Integrated Navigation for the SSBE Cruise Phase Based on X-Ray Pulsar/Solar and Target Planetary Doppler Information Fusion
Authors: Tao Wenjian , Zhang Jinxiu , Song Jianing , Lin Qin , Chen Zebin , Wang Hui , Yang Jikun , Wang Jihe
Published: November 2024

The Solar System Boundary Exploration (SSBE) mission is the focal point for future far-reaching space exploration. Due to the SSBE having many scientific difficulties that need to be studied, such as a super long space exploratory distance, a super long flight time in orbit, and a significant communication data delay between the ground and the probe, the probe must have sufficient intelligence to realize intelligent autonomous navigation. Traditional navigation schemes have been unable to provide high-accuracy autonomous intelligent navigation for the probe independent of the ground. Therefore, high-accuracy intelligent astronomical integrated navigation would provide new methods and technologies for the navigation of the SSBE probe. The probe of the SSBE is disturbed by multiple sources of solar light pressure and a complex, unknown environment during its long cruise operation while in orbit. In order to ensure the high-accuracy position state and velocity state error estimation for the probe in the cruise phase, an autonomous intelligent integrated navigation scheme based on the X-ray pulsar/solar and target planetary Doppler velocity measurements is proposed. The reinforcement Q-learning method is introduced, and the reward mechanism is designed for trial-and-error tuning of state and observation noise error covariance parameters. The federated extended Kalman filter (FEKF) based on the Q-learning (QLFEKF) navigation algorithm is proposed to achieve high-accuracy state estimations of the autonomous intelligence navigation system for the SSBE probe cruise phase. The main advantage of the QLFEKF is that Q-learning combined with the conventional federated filtering method could optimize the state parameters in real-time and obtain high position and velocity state estimation (PVSE) accuracy. Compared with the conventional FEKF integrated navigation algorithm, the PVSE navigation accuracy of the federated filter integrated based the Q-learning navigation algorithm is improved by 55.84% and 37.04%, respectively, demonstrating the higher accuracy and greater capability of the raised autonomous intelligent integrated navigation algorithm. The simulation results show that the intelligent integrated navigation algorithm based on QLFEKF has higher navigation accuracy and is able to satisfy the demands of autonomous high accuracy for the SSBE cruise phase.
Residual-Based Implicit Neural Representation for Synthetic Aperture Radar Images
Authors: Han Dongshen , Zhang Chaoning
Published: November 2024

Implicit neural representations (INRs) are a new way to represent all kinds of signals ranging from 1D audio to 3D shape signals, among which 2D images are the most widely explored due to their ubiquitous presence. Image INRs utilize a neural network to learn a continuous function that takes pixel coordinates as input and outputs the corresponding pixel values. The continuous representation of synthetic aperture radar (SAR) images using INRs has not yet been explored. Existing INR frameworks developed on natural images show reasonable performance, but this performance suffers when capturing fine details. This can be attributed to INR’s prioritization of learning inter-pixel relationships, which harms intra-pixel mapping in those regions that require fine detail. To address this, we decompose the target image into an artificial uniform noise component (intra-pixel mapping) and a residual image (inter-pixel relationships). Rather than directly learning the INRs for the target image, we propose a noise-first residual learning (NRL) method. The NRL first learns the uniform noise component, then gradually incorporates the residual into the optimization target using a sine-adjusted incrementation scheme as training progresses. Given that some SAR images inherently contain significant noise, which can facilitate learning the intra-pixel independent mapping, we propose a gradient-based dataset separation method. This method distinguishes between clean and noisy images, allowing the model to learn directly from the noisy images. Extensive experimental results show that our method achieves competitive performance, indicating that learning the intra-pixel independent mapping first, followed by the inter-pixel relationship, can enhance model performance in learning INR for SAR images.
Self-Activated Implicit Neural Representation for Synthetic Aperture Radar Images
Authors: Han Dongshen , Zhang Chaoning
Published: November 2024

Image Implicit Neural Representations (INRs) adopt a neural network to learn a continuous function for mapping the pixel coordinates to their corresponding values. This task has gained significant attention for representing images in a continuous manner. Despite substantial progress regarding natural images, there is little investigation of INRs for Synthetic Aperture Radar (SAR) images. This work takes a pioneering effort to study INRs for SAR images and finds that fine details are hard to represent. It has been shown in prior works that fine details can be easier to learn when the model weights are better initialized, which motivated us to investigate the benefits of activating the model weight before target training. The challenge of this task lies in the fact that SAR images cannot be used during the model activation stage. To this end, we propose exploiting a cross-pixel relationship of the model output, which relies on no target images. Specifically, we design a novel self-activation method by alternatively using two loss functions: a loss used to smooth out the model output, and another used for the opposite purpose. Extensive results on SAR images empirically show that our proposed method helps improve the model performance by a non-trivial margin.
Evaluating PlanetScope and UAV Multispectral Data for Monitoring Winter Wheat and Sustainable Fertilization Practices in Mediterranean Agroecosystems
Authors: Moletto-Lobos Italo , Cyran Katarzyna , Orden Luciano , Sánchez-Méndez Silvia , Franch Belen , Kalecinski Natacha , Andreu-Rodríguez Francisco J. , Mira-Urios Miguel Á. , Saéz-Tovar José A. , Guillevic Pierre C. , Moral Raul
Published: November 2024

Cereal crops play a critical role in global food security, but their productivity is increasingly threatened by climate change. This study evaluates the feasibility of using PlanetScope satellite imagery and a UAV equipped with the MicaSense RedEdge multispectral imaging sensor in monitoring winter wheat under various fertilizer treatments in a Mediterranean climate. Eleven fertilizer treatments, including organic-mineral fertilizer (OMF) pellets, were tested. The results show that conventional inorganic fertilization provided the highest yield (8618 kg ha⁻1), while yields from OMF showed a comparable performance to traditional fertilizers, indicating their potential for sustainable agriculture. PlanetScope data demonstrated moderate accuracy in predicting canopy cover (R2 = 0.68), crop yield (R2 = 0.54), and grain quality parameters such as protein content (R2 = 0.49), starch (R2 = 0.56), and hectoliter weight (R2 = 0.51). However, its coarser resolution limited its ability to capture finer treatment-induced variability. MicaSense, despite its higher spatial resolution, performed poorly in predicting crop components, with R2 values below 0.35 for yield and protein content. This study highlights the complementary use of remote sensing technologies to optimize wheat management and support climate-resilient agriculture through the integration of sustainable fertilization strategies.
Enhancing Landslide Susceptibility Mapping by Integrating Neighboring Information in Slope Units: A Spatial Logistic Regression
Authors: Li Leilei , Jia Mingzhen , Xu Chong , Tian Yingying , Ma Siyuan , Yang Jintao
Published: November 2024

Landslide susceptibility mapping (LSM) is a vital tool for proactive disaster mitigation. Although numerous studies utilize slope units (SUs) for LSM, the limited integration of adjacency information, including spatial autocorrelation, often reduces predictive accuracy. In this study, GRASS GIS was utilized to generate slope units, and a spatial logistic regression (SLR) model was developed to incorporate the adjacency information of the slope units to predict the landslide susceptibility. Then, the spatial stratification heterogeneity patterns of landslide susceptibility were analyzed using GeoDetector. The results showed that the SLR model achieved an area under the curve (AUC) of 0.89, a notable improvement of 0.26 compared to the traditional logistic regression (LR) model that does not incorporate adjacency information. This indicates that incorporating adjacency information effectively enhances LSM accuracy by mitigating spatial autocorrelation. Furthermore, lithology, PGV, and distance to the epicenter were identified as the primary factors contributing to the formation of the spatial stratification heterogeneity of landslide susceptibility. Among these, the interaction between lithology and PGV exhibits the strongest nonlinear enhancement. By integrating both mapping units and their adjacency information, this study provides a novel approach to improving the predictive accuracy of LSM. Moreover, by analyzing the driving factors of spatial stratification heterogeneity in landslide susceptibility maps, the study advances the practical utility of LSM for disaster management and mitigation.
Enhanced Polarimetric Radar Vegetation Index and Integration with Optical Index for Biomass Estimation in Grazing Lands Across the Contiguous United States
Authors: Chang Jisung Geba , Kraatz Simon , Anderson Martha , Gao Feng
Published: November 2024

Grazing lands are crucial for agricultural productivity, ecological stability, and carbon sequestration, underscoring the importance of monitoring vegetation biomass for the effective management of these ecosystems. Remote sensing data, including optical vegetation indices (VIs) like the Normalized Difference Vegetation Index (NDVI), are widely used to monitor vegetation dynamics due to their simplicity and high sensitivity. In contrast, radar-based VIs, such as the Polarimetric Radar Vegetation Index (PRVI), offer additional advantages, including all-weather imaging capabilities, a wider saturation range, and sensitivity to the vegetation structure information. This study introduces an enhanced form of the PRVI, termed the Normalized PRVI (NPRVI), which is calibrated to a 0 to 1 range, constraining the minimum value to reduce the background effects. The calibration and range factor were derived from statistical analysis of PRVI components across vegetated regions in the Contiguous United States (CONUS), using dual-polarization C-band Sentinel-1 and L-band ALOS-PALSAR data on the Google Earth Engine (GEE) platform. Machine learning models using NPRVI and NDVI demonstrated their complementarity with annual herbaceous biomass data from the Rangeland Analysis Platform. The results showed that the Random Forest Model outperformed the other machine learning models tested, achieving R2 ≈ 0.51 and MAE ≈ 498 kg/ha (relative MAE ≈ 32.1%). Integrating NPRVI with NDVI improved biomass estimation accuracy by approximately 10% compared to using NDVI alone, highlighting the added value of incorporating radar-based vegetation indices. NPRVI may enhance the monitoring of grazing lands with relatively low biomass compared to other vegetation types, while also demonstrating applicability across a broad range of biomass levels and in diverse vegetation covers.
Sub-Terahertz Imaging-Based Real-Time Non-Destructive Inspection System for Estimating Water Activity and Foreign Matter Depth in Seaweed
Authors: Kwak Dong-Hoon , Yun Ho-Won , Lee Jong-Hun , Kim Young-Duk , Choi Doo-Hyun
Published: November 2024

As the importance of hygiene and safety management in food manufacturing has been increasingly emphasized, research on non-destructive and non-contact inspection technologies has become more active. This study proposes a real-time and non-destructive food inspection system with sub-terahertz waves which penetrates non-conducting materials by using a frequency of 0.1 THz. The proposed system detects not only the presence of foreign matter, but also the degree of depth to which it is mixed in foods. In addition, the system estimates water activity levels, which serves as the basis for assessing the freshness of seaweed by analyzing the transmittance of signals within the sub-terahertz image. The system employs YOLOv8n, which is one of the newest lightweight object detection models. This lightweight model utilizes the feature pyramid network (FPN) to effectively detect objects of various sizes while maintaining a fast processing speed and high performance. In particular, to validate the performance in real manufacturing facilities, we implemented a hardware platform, which accurately inspects seaweed products while cooperating with a conveyor device moving at a speed of 45 cm/s. For the validation of the estimation performance against various water activities and the degree of depth of foreign matter, we gathered and annotated a total of 9659 sub-terahertz images and optimized the learning model. The final results show that the precision rate is 0.91, recall rate is 0.95, F1-score is 0.93, and mAP is 0.97, respectively. Overall, the proposed system demonstrates an excellent performance in the detection of foreign matter and in freshness estimation, and can be applied in several applications regarding food safety.
Operational Modal Analysis of Civil Engineering Structures with Closely Spaced Modes Based on Improved Hilbert–Huang Transform
Authors: Shang Xu-Qiang , Huang Tian-Li , He Yi-Bin , Chen Hua-Peng
Published: November 2024

In long-span bridges and high-rise buildings, closely spaced modes are commonly observed, which greatly increases the challenge of identifying modal parameters. Hilbert–Huang transform (HHT), a widely used method for modal parameter identification, first applies empirical mode decomposition (EMD) to decompose the acquired response and then uses the Hilbert transform (HT) to identify the modal parameters. However, the problem is that the deficiency of mode separation of EMD in HHT limits its application for structures with closely spaced modes. In this study, an improved HHT based on analytical mode decomposition (AMD) is proposed and is used to identify the modal parameters of structures with closely spaced modes. In the improved HHT, AMD is first employed to replace EMD for decomposing the measured response into several mono-component modes. Then, the random decrement technique is applied to the decomposed mono-component modes to obtain the free decay responses. Furthermore, the resulting free decay responses are analyzed by HT to estimate the modal parameters of structures with closely spaced modes. Examples of a simple three-degree-of-freedom system with closely spaced modes, a high-rise building under ambient excitation, and the Ting Kau bridge under typhoon excitations are adopted to validate the accuracy, effectiveness, and applicability of the proposed method. The results demonstrate that the proposed method can efficiently and accurately identify the natural frequencies and damping ratios of structures with closely spaced modes. Moreover, its identification results are more precise compared to those obtained using existing methods.
Accurate Identification Partial Discharge of Cable Termination for High-Speed Trains Based on S-Transform and Two-Dimensional Convolutional Network Algorithm
Authors: Xie Yunlong , You Peng , Wu Guangning , Zhang Tingyu , Luo Yang , Zhou Shuyuan , Liu Kai , Chen Kui , Xin Dongli , Gao Guoqiang
Published: November 2024

Cable termination is an important part of energy transmission in high-speed trains, and it is also a weak link in the insulation. It is important to determine the insulation status of cable terminals by the detection results of partial discharge signals, but the partial discharge signals in the field test circuit are mixed with a large amount of external corona interference, which affects the detection accuracy. This paper proposes a signal recognition model that incorporates Stockwell transform (ST) and 2DCNN, which in combination with wavelet noise reduction can achieve a high-precision classification effect for partial discharge and corona interference with an accuracy rate of up to 98.75%. By selecting the maximum energy moment in the ST matrix to correct the position of the time window during the recognition of long time series signals, the problem of corona interference being truncated by the time window and being misidentified as partial discharge is overcome, and the generalization ability of the model is enhanced. Experimental results show that the method has an excellent performance in separating partial discharge and corona interference in long time series signals.
Automatic Roll-Profile Positioning Detection System Based on Contact Sensor
Authors: Zheng Jiali , Huang Huagui , Hu Qiwei
Published: November 2024

Based on the traditional saddle instrument, a portable roll-profile measuring device based on a contact sensor is designed and optimized. The positioning module is added via the machine vision method, which enables the automatic reading of measurement points. The measurement accuracy of the device is 1 μm. Possible errors caused by the process of installing the roll-profile meter are analyzed, and the installation requirements of the measuring device are provided. On this basis, the compensation method for roll measurement with the roll profile is also improved. The accuracy of the measuring device is verified by measuring the roll on the spot, and the measurement error of the flat roll is 0.02 mm. When measuring the roll crown, the measurement accuracy is improved by 69% after the compensation algorithm is applied. The measurement results meet industrial measurement requirements, which is highly significant to the intelligent operation and maintenance of the roll.
Integrating Connected Vehicles into IoT Ecosystems: A Comparative Study of Low-Power, Long-Range Communication Technologies
Authors: Iordache Valentin , Minea Marius , Gheorghiu Răzvan Andrei , Bădău Florin , Cormoș Angel Ciprian , Stan Valentin Alexandru , Stăncel Ion Nicolae , Stoica Victor
Published: November 2024

Integrating road vehicles into broader Internet of Things (IoT) ecosystems is an important step in the development of fully connected and smart transportation systems. This research explores the potential of using communication technologies that achieve a balance between low-power and long-range (LPLR) capabilities while remaining cost-effective, specifically Bluetooth Classic BR-EDR, Bluetooth LE, ZigBee, nRF24, and LoRa—for Vehicle-to-Infrastructure (V2I) and Vehicle-to-IoT (V2IoT) ecosystem interactions. During this research, several field tests were conducted employing different types of communication modules, across three distinct environments: an open-field inter-urban road, a forest inter-urban road, and an urban road. The modules were evaluated based on the communication range, messaging rate, error rate, and geographical data from GNSS (Global Navigation Satellite System) coordinates, using point-to-point communication between a roadside unit (RSU) and a moving vehicle equipped with an onboard unit (OBU). The results demonstrate the usability of these technologies for integrating vehicles into both public infrastructure (for V2I services) and private IoT systems, highlighting their potential for scalable, cost-effective deployment in smart transportation systems.
Training in Roller Speed Skating: Proposal of Surface Electromyography and Kinematics Data for Educational Purposes in Junior and Senior Athletes
Authors: Bongiorno Giulia , Sisti Giulio , Biancuzzi Helena , Dal Mas Francesca , Minisini Francesco Giuseppe , Miceli Luca
Published: November 2024

Introduction: Roller skating shares biomechanical similarities with other sports, but specific studies on speed skaters are limited. Injuries, particularly to the groin, are frequent and related to acute and chronic muscle stress. Technology, particularly surface electromyography, can now be used to monitor performance and prevent injuries, especially those caused by muscular asymmetries. Such studies can be used to enhance training and for educational purposes. Materials and Methods: This pilot study was conducted on three subjects: two cadet-athletes and a novice, compared with the performance model of an elite athlete. Surface electromyography and kinematic analysis monitored the lower limb muscles during the propulsion and recovery phases of skating. Electrodes were placed on specific muscles, and triaxial accelerometers were used to detect kinematic differences and asymmetries. The results: Cadet 1 was closest to the elite athlete’s performance model compared to Cadet 2, especially in kinematics and muscle efficiency. However, both cadets showed electromyographic differences compared to the elite athlete, with uneven muscle co-activations. The novice exhibited more oscillations and earlier propulsion compared to the elite athlete. Discussion: Using electromyography and kinematic analysis made it possible to identify differences between elite athletes, cadets, and novices. These observations provide useful data for developing personalized training and educational plans and preventing injuries related to muscle overload.
Multi-Sensor Fusion for Wheel-Inertial-Visual Systems Using a Fuzzification-Assisted Iterated Error State Kalman Filter
Authors: Huang Guohao , Huang Haibin , Zhai Yaning , Tang Guohao , Zhang Ling , Gao Xingyu , Huang Yang , Ge Guoping
Published: November 2024

This paper investigates the odometry drift problem in differential-drive indoor mobile robots and proposes a multi-sensor fusion approach utilizing a Fuzzy Inference System (FIS) within a Wheel-Inertial-Visual Odometry (WIVO) framework to optimize the 6-DoF localization of the robot in unstructured scenes. The structure and principles of the multi-sensor fusion system are developed, incorporating an Iterated Error State Kalman Filter (IESKF) for enhanced accuracy. An FIS is integrated with the IESKF to address the limitations of traditional fixed covariance matrices in process and observation noise, which fail to adapt effectively to complex kinematic characteristics and visual observation challenges such as varying lighting conditions and unstructured scenes in dynamic environments. The fusion filter gains in FIS-IESKF are adaptively adjusted for noise predictions, optimizing the rule parameters of the fuzzy inference process. Experimental results demonstrate that the proposed method effectively enhances the localization accuracy and system robustness of differential-drive indoor mobile robots in dynamically changing movements and environments.
Avelumab in First Line Maintenance in Advanced Urothelial Carcinoma (aUC) in Elderly Patients: Efficacy, Tolerability, and Quality of Life in Real Life Setting
Authors: De Luca Rossella , Vitale Pasquale , Pepe Alessio , Spedaliere Roberta , Simonato Alchiede , Addeo Raffaele , Cicero Giuseppe
Published: November 2024

(1) Background Immune checkpoint inhibitors (ICIs) have recently become an important therapeutic option for patients with advanced urothelial carcinoma (aUC). Avelumab is an anti-PD-L1 (programmed cell death ligand 1) antibody that restores antitumor T-cell immune function by blocking the binding of PD-1 to its ligand PD-L1. (2) Methods: Our study enrolled 60 elderly patients (≥70 years) diagnosed with aUC. The primary endpoints of this study were overall survival (OS), progression free survival (PFS), and objective response rate (ORR); the secondary endpoints were tolerability, pre- and post- treatment reduction in serum Ca 19.9, and quality of life (QoL). (3) Results: Our results showed no statistically significant or clinically relevant differences between the PD-L1-positive and negative groups. Avelumab was well tolerated and resulted in good disease control, with a moderate toxicity profile and significant clinical benefit. The median PFS was 3.6 months (95% CI: 2.3–6.8), and the median OS was 18.6 months (95% CI: 6.3–20.7), with an ORR of 20%. A significant correlation was observed between serum Ca 19.9 reduction and PFS of 0.59 (95% CI: 0.12–0.57), p = 0.007. (4) Conclusions: Avelumab is an immunotherapy treatment that has been shown to be an effective and well tolerated treatment option in elderly patients with aUC.
Study on Separation Kinetics of Non-Spherical Single Feeding Particle in the Gas–Solid Separation Fluidized Bed
Authors: Fan Xuchen , Fu Yuping , He Yongliang , Sun Liying
Published: November 2024

Gas–solid separation fluidized bed is an efficient and clean coal separation technology with a good separation effect for coal particles. However, there is a lack of systematic research on the complex motion behavior of the feeding particles in gas–solid separation fluidized beds. In this study, the separation kinetics of non-spherical single feeding particles in the fluidized bed are examined. The particle sphericity coefficient Φ is introduced to characterize the morphology of irregular coal particles, and the drag coefficient for the feeding particles is modified to verify the suitability of the non-spherical particle drag model for gas–solid fluidized bed separation. After optimization and correction, a ρS.sus (the bed density when single feeding particles are suspended in the gas–solid separation fluidized bed) prediction model is obtained. When the prediction accuracy of the ρS.sus prediction model is 90%, the confidence degree is 85.72%. This ρS.sus of the single non-spherical feeding particle prediction model highlights a direction for improving the separation effect, provides a theoretical basis for the industrialization of gas–solid fluidized bed, and promotes the process of dry fluidized separation.
Effectiveness of a Cloud Learning Management System in Developing the Digital Transformation Skills of Blind Graduate Students
Authors: El Koshiry Amr , Eliwa Entesar , Abd El-Hafeez Tarek , Tony Mohamed Abd Allah
Published: November 2024

Digital transformation has become a critical aspect of modern education, necessitating the development of digital skills among all students, including those with disabilities. Among these, blind students face unique challenges in acquiring the digital competencies needed for academic success and professional integration. This study aimed to enhance the digital transformation skills of blind postgraduate students by evaluating the effectiveness of a cloud-based learning management system, Moodle Cloud. Using a mixed methods approach, we combined descriptive and quasi-experimental designs to assess the impact of the intervention. The sample included 20 blind graduate students from Beni Suef University, equally divided into experimental and control groups. Pre- and post-assessments measured participants’ digital transformation skills through achievement tests and performance evaluations. The findings indicated significant improvements in the experimental group, with higher scores in both the achievement tests and performance assessments compared to the control group. The results suggest that the cloud-based learning management system played a vital role in enhancing digital skills, and no significant differences were found between remote and in-person applications of the intervention. The study emphasizes the importance of incorporating modern digital technologies into the education of blind students, aligning with Egypt’s Vision 2030 plan and ongoing educational reforms.
Gendered Differences in Household Engagement in Non-Farm Business Operations and Implications on Household Welfare: A Case of Rural and Urban Malawi
Authors: Mgomezulu Wisdom Richard , Dar Javaid Ahmad , Maonga Beston B.
Published: November 2024

Mainstreaming gender issues in a demographically divided setting remains a critical component in policy frameworks and project designs. The focus of this study revolves around male- and female-headed households’ engagement in business operations, with an extended effect on household welfare. This study uses Malawi’s Integrated Household Survey 5, and answers two research questions: Are there any determinants of household engagement in non-farm businesses in rural and urban areas of Malawi? And is there any impact of gender differentials in household engagements in non-farm business operations on household welfare in rural and urban areas of Malawi? This study notes that male-managed non-farm SMEs had better profits compared with their female counterparts, as described by the Probit and the Oaxaca two-fold decomposition models. Mediation analysis was later used to establish the impact of the gender differentials using profits as the mediating variable. In general, females were found to be better off, but the benefits were insignificant in reducing the general rural–urban gap. This study recommends the provision of support towards credit structures for rural households and women-headed households; improved financial and business literacy for increased engagement in business operations and reduced poverty; and support towards advocacy of gender inclusion in economic empowerment approaches.