bluuebunny/crossref_metadata_2025 huggingface dataset:
Polymer Bionanocomposites Based on a P3BH/Polyurethane Matrix with Organomodified Montmorillonite—Mechanical and Thermal Properties, Biodegradability, and Cytotoxicity
Authors: Krzykowska Beata , Uram Łukasz , Frącz Wiesław , Kovářová Miroslava , Sedlařík Vladimir , Hanusova Dominika , Kisiel Maciej , Paciorek-Sadowska Joanna , Borowicz Marcin , Zarzyka Iwona
Published: September 2024

In the present work, hybrid nanobiocomposites based on poly(3-hydroxybutyrate), P3HB, with the use of aromatic linear polyurethane as modifier and organic nanoclay, Cloisite 30B, as a nanofiller were produced. The aromatic linear polyurethane (PU) was synthesized in a reaction of diphenylmethane 4,4′-diisocyanate and polyethylene glycol with a molecular mass of 1000 g/mole. The obtained nanobiocomposites were characterized by the small-angle X-ray scattering technique, scanning electron microscopy, Fourier infrared spectroscopy, thermogravimetry, and differential scanning calorimetry, and moreover, their selected mechanical properties, biodegradability, and cytotoxicity were tested. The effect of the organomodified montmorillonite presence in the biocomposites on their properties was investigated and compared to those of the native P3HB and the P3HB-PU composition. The obtained hybrid nanobiocomposites have an exfoliated structure. The presence and content of Cloisite 30B influence the P3HB-PU composition’s properties, and 2 wt.% Cloisite 30B leads to the best improvement in the aforementioned properties. The obtained results indicate that the thermal stability and mechanical properties of P3HB were improved, particularly in terms of increasing the degradation temperature, reducing hardness, and increasing impact strength, which were also confirmed by the morphological analysis of these bionanocomposites. However, the presence of organomodified montmorillonite in the obtained polymer biocomposites decreased their biodegradability slightly. The produced hybrid polymer nanobiocomposites have tailored mechanical and thermal properties and processing conditions for their expected application in the production of biodegradable, short-lived products for agriculture. Moreover, in vitro studies on human skin fibroblasts and keratinocytes showed their satisfactory biocompatibility and low cytotoxicity, which make them safe when in contact with the human body, for instance, in biomedical applications.
Thermomechanical Assessment of Recovered PA12 Powders with Basalt Filler for Automotive Components
Authors: Napolitano Francesco , Papa Ilaria , Cimino Francesca , Lopresto Valentina , Russo Pietro
Published: September 2024

Additive manufacturing processes allow for precise and efficient production, but it is estimated that one-third of the materials used results in waste. Further improvement in a sustainable perspective could come from the ability to manage these scraps and from the exploration of different routes for recovery and reuse. The Selective Laser Sintering process is particularly sensitive to this issue due to the waste ratio which can reach a very high quantity of not-sintered virgin powders. In this research study, recovered PA12 powders, preliminarily characterized through thermal and mechanical analysis, were mixed with 15% basalt powder to improve their aspect and thermomechanical resistance. The influence of basalt powder (BP) on mechanical properties as well as on the thermal stability of polyamide12 (PA12) powder composites was investigated. A study conducted on mechanical properties showed that polymeric composites’ stiffness and hardness were influenced by adding filler, thus improving mechanical parameters. On the other hand, the application of thermogravimetric analysis allowed us to determine the composite’s thermal stability. The objective is to obtain a recovered fully biobased material that could be used to substitute the petroleum-derived polymeric ones currently employed in the production of interiors and shells in the automotive sector.
The Effects of Internal Electron Donors on MgCl2-Supported Ziegler–Natta Catalysts for Isotactic PP
Authors: Li Bin , Li Huashu , Hu Hongfan , Zhou Yi , Mao Guoliang , Xin Shixuan
Published: September 2024

The electron donors (ED) in Ziegler–Natta (Z-N) catalysis are classified as internal electron donors (IED) and external electron donors (EED), and both IED and EED are indispensable components for enhancing the catalytic reactivity and regulating the stereoregularity of polyolefinic materials in a typical industrial Z-N catalytic process. With the intensive research on ED, the Z-N catalyst performances have experienced successive progress in the last few decades. Polypropylenes (PP) as a commodity polyolefin material, especially the isotactic PP (iPP), are produced in multi-billion pounds per annum by utilization of the various IED- and EED-assisted Z-N catalysts systems. In the course of developing Z-N catalysts, the ED constitutes a key component of the content and represents a significant area of future research. In this review, we introduced a concise overview of the functions of IEDs in the generations of Z-N catalyst systems and the widely used IED types (A total of 11 different types of IEDs are encompassed within this study) that have been developed so far. In addition, we focused on the coordination modes of different IEDs in the MgCl2-supported Z-N catalyst system and analyzed the effects of different types of IEDs on the PP isotacticity, regioselectivity, hydrogen sensitivity, and briefly introduced the application of environmentally friendly rosinate and salicylate IEDs.
A Multi-Scale Numerical Simulation Method Considering Anisotropic Relative Permeability
Authors: Wu Li , Wang Junqiang , Jia Deli , Zhang Ruichao , Zhang Jiqun , Yan Yiqun , Wang Shuoliang
Published: September 2024

Most of the oil reservoirs in China are fluvial deposits with firm reservoir heterogeneity, where differences in fluid flow capacity in individual directions should not be ignored; however, the available commercial reservoir simulation software cannot consider the anisotropy of the relative permeability. To handle this challenge, this paper takes full advantage of the parallelism of the multi-scale finite volume (MsFV) method and establishes a multi-scale numerical simulation approach that incorporates the effects of reservoir anisotropy. The methodology is initiated by constructing an oil–water black-oil model considering the anisotropic relative permeability. Subsequently, the base model undergoes decoupling through a sequential solution, formulating the pressure and transport equations. Following this, a multi-scale grid system is configured, within which the pressure and transport equations are progressively developed in the fine-scale grid domain. Ultimately, the improved multi-scale finite volume (IMsFV) method is applied to mitigate low-frequency error in the coarse-scale grid, thereby enhancing computational efficiency. This paper introduces two primary innovations. The first is the development of a multi-scale solution method for the pressure equation incorporating anisotropic relative permeability. Validated using the Egg model, a comparative analysis with traditional numerical simulations demonstrates a significant improvement in computational speed without sacrificing accuracy. The second innovation involves applying the multi-scale framework to investigate the impact of anisotropy relative permeability on waterflooding performance, uncovering distinct mechanisms by which absolute and relative permeability anisotropy influence waterflooding outcomes. Therefore, the IMsFV method can be used as an effective tool for high-resolution simulation and precise residual oil prediction in anisotropic reservoirs.
Prediction Method for Excess Surface Temperature Peak Time Inclusion Defect Depth Based on Conjugate Gradient Algorithm
Authors: Yue Yajing , Du Weixiang , Yu Jianping , Ren Baocun
Published: September 2024

This study addresses the challenge of accurately calculating the depth of inclusion defects in Glass Fiber-Reinforced Plastic (GFRP), which is commonly used in onshore wind turbine blades. To overcome this issue, we proposed a novel Excess Surface Temperature Peak Time (ESPT) estimation method that combines a conjugate gradient algorithm with a conventional analytical approach. This research employed the Inverse Heat Transfer Problem (IHTP) solution method to estimate the boundary conditions of an experimental sample subjected to pulse excitation. By drawing analogies with traditional depth detection methods, we analyzed specific physical models and determined the calculated thickness of the sample. The Excess Surface Temperature Peak Time characteristics were then used to estimate the defect depth, and the resulting estimates and relative errors were evaluated. Our results demonstrated that the proposed method achieved a relative error of less than 15% when calculating defect depth, confirming its effectiveness. This approach provides new insights and possibilities for improving defect depth estimation in GFRP materials, offering valuable contributions to the assessment and maintenance of wind turbine blade safety.
Effects of the Characterization Methods of Heptane Plus Components (C7+) on the Phase Behavior of Volatile Fluids
Authors: Wang Jia , Liu Hailong , Zhao Haining , Wang Bo , Wu Haonan , Nan Rongli , An Chen
Published: September 2024

For reservoir fluids with strong volatile tendencies, such as gas condensate and volatile oil, the determination of the characteristic parameters of the equation of state has a great influence on the fluid phase behavior and subsequent reservoir engineering, production engineering, and surface facilities design calculations. However, the process of characterizing reservoir fluid lacks a unified and standardized procedure, and the methods are largely empirical and rather complicated. In this paper, three main stages are defined for the characterization of the equation of state parameters for these volatile reservoir fluids, including (1) determining the C7+ distribution, (2) calculating the equation of state parameters for the C7+ single-carbon component, and (3) lump components. The typical calculation methods in each step and their effects on the fluid phase behavior calculations are quantitatively analyzed and compared. Finally, the workflow and the suitable characterization methods for gas condensate and volatile oil phase behavior calculations are selected. The results show that C7+ distribution and C7+ single carbon component properties have a greater influence on the phase behavior prediction of condensate gas and volatile oil, while the lumping of C7+ single carbon component has relatively little influence.
Functional Polyvinyl Alcohol/Chitosan/C-AgNP/Starch Composite Gel with Excellent Swelling and Water Retention Properties for Effective Antibacterial Action
Authors: Fan Bo , Yang Dan , Feng Qian , He Yucai , He Wei , Huang Meizi
Published: September 2024

Microbial contamination remains a global issue threatening human health. In this research, silver nanoparticles (AgNPs) were fabricated using Osmanthus fragrans flower extract as a reducing agent, and biochar derived from carbonizing waste barley distillers’ grain shells was used as a support to fabricate silver-loaded carbon (C-AgNP, C-Ag). PVA-CS-C-Ag-St gel was acquired by cross-linking polyvinyl alcohol (PVA), chitosan (CS), and starch (St) with glutaraldehyde (GA). Results from SEM, FTIR, and XRD demonstrated that PVA, CS, St, and C-Ag were successfully incorporated into the gel. The PVA-CS-C-Ag-S gel showcased excellent swelling and water retention properties, which had substantial antibacterial activity against Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus, with inhibition zones of 25.0 mm, 22.5 mm, and 18.0 mm, respectively. Finally, the antimicrobial analysis revealed that PVA-CS-C-Ag-St gel exhibited excellent antimicrobial properties against typical Gram-positive bacteria (Staphylococcus aureus) and Gram-negative bacteria (Escherichia coli and Pseudomonas aeruginosa). Overall, the PVA-CS-C-Ag-St gel holds great promise for food preservation and environmental pollution control.
A Review on the Latest Early Pleistocene Carnivoran Guild from the Vallparadís Section (NE Iberia)
Authors: Madurell-Malapeira Joan , Prat-Vericat Maria , Bartolini-Lucenti Saverio , Faggi Andrea , Fidalgo Darío , Marciszak Adrian , Rook Lorenzo
Published: September 2024

The Vallparadís Section encompasses various geological layers that span a significant chronological range, extending from the latest Early Pleistocene to the early Middle Pleistocene, covering a timeframe from approximately 1.2 to 0.6 Ma. This period holds particular importance, as it coincides with a significant climatic transition known as the Early–Middle Pleistocene Transition, a pivotal phase in Quaternary climatic history. This transition, marked by the shift from a 41,000-year obliquity-driven climatic cycle to a 100,000-year precession-forced cyclicity, had profound effects on the Calabrian carnivorous mammal communities. Notably, the once diverse carnivore guild began to decline across Europe during this period, with their last documented occurrences coinciding with those found within the Vallparadís Section (e.g., Megantereon or Xenocyon). Concurrently, this period witnessed the initial dispersals of African carnivorans into the European landscape (e.g., steppe lions), marking a significant shift in the composition and dynamics of the region’s carnivorous fauna.
Shamans and “Dark Agencies”: War, Magical Parasitism, and Re-Enchanted Spirits in Siberia
Authors: Zorbas Konstantinos
Published: September 2024

Alleged practices of magical assault and vampirism are a recurrent feature of popular explanations of misfortune in Tuva, South Siberia. Based on a field study of healing practices in an “Association of Shamans”, this article analyses rituals of redressing curse afflictions in the context of Russian political domination. A central purpose of this discussion is to foreground the centrality of kinds of parasitical worship and occult threat to structures of political power in—and beyond—the territory of Tuva. Focusing on a “cursescape”, which develops from the combative practices of shamans, occult specialists, and office-holders, the article probes a repertoire of shamanic healing symbols. It is argued that healing efficacy is constructed in the process of engaging with hunting symbols and animal spirits, which appear in Indigenous Siberian cosmologies. The analysis shows that ideas of ritual risk underpin the process of symbolic resolution. Whereas shamanic practices provide refuge to spirits evicted from their natural landscapes, Tibetan Buddhism—the unifying religion of Tuva—offers an alternative path of healing the effects of the shamans’ propagation of spirits. The article highlights indigenous perceptions of a “cursed” landscape as a space where the agencies of “darkness” and their political sponsors are confronted with an emancipating religious modality emerging from local Buddhist rituals. The analysis displays the unsolved drama of itinerant spirits and shamanic ancestral souls, whose agency is revealed through successive—yet inauspicious—forms of reincarnation.
Observation of Post-Sunset Equatorial Plasma Bubbles with BDS Geostationary Satellites over South China
Authors: Ma Guanyi , Li Jinghua , Fan Jiangtao , Wan Qingtao , Maruyama Takashi , Dong Liang , Gao Yang , Zhang Le , Wang Dong
Published: September 2024

An equatorial plasma bubble (EPB) is characterized by ionospheric irregularities which disturb radio waves by causing phase and amplitude scintillations or even signal loss. It is becoming increasingly important in space weather to assure the reliability of radio systems in both space and on the ground. This paper presents a newly established GNSS ionospheric observation network (GION) around the north equatorial ionization anomaly (EIA) crest in south China, which has a longitudinal coverage of ∼30° from 94°E to 124°E. The measurement with signals from geostationary earth orbit (GEO) satellites of the BeiDou navigation satellite system (BDS) is capable of separating the temporal and spatial variations of the ionosphere. A temporal fluctuation of TEC (TFT) parameter is proposed to characterize EPBs. The longitude of the EPBs’ generation can be located with TFT variations in the time–longitude dimension. It is found that the post-sunset EPBs have a high degree of longitudinal variability. They generally show a quasiperiodic feature, indicating their association with atmospheric gravity wave activities. Wave-like structures with different scale sizes can co-exist in the same night.
Exploration of Deep-Learning-Based Error-Correction Methods for Meteorological Remote-Sensing Data: A Case Study of Atmospheric Motion Vectors
Authors: Cao Hang , Leng Hongze , Zhao Jun , Xu Xiaodong , Yang Jinhui , Li Baoxu , Zhou Yong , Huang Lilan
Published: September 2024

Meteorological satellite remote sensing is important for numerical weather forecasts, but its accuracy is affected by many things during observation and retrieval, showing that it can be improved. As a standard way to measure wind from space, atmospheric motion vectors (AMVs) are used. They are separate pieces of information spread out in the troposphere, which gives them more depth than regular surface or sea surface wind measurements. This makes rectifying problems more difficult. For error correction, this research builds a deep-learning model that is specific to AMVs. The outcomes show that AMV observational errors are greatly reduced after correction. The root mean square error (RMSE) drops by almost 40% compared to ERA5 true values. Among these, the optimization of solar observation errors exceeds 40%; the discrepancies at varying atmospheric pressure altitudes are notably improved; the degree of optimization for data with low QI coefficients is substantial; and there remains potential for enhancement in data with high QI coefficients. Furthermore, there has been a significant enhancement in the consistency coefficient of the wind’s physical properties. In the assimilation forecasting experiments, the corrected AMV data demonstrated superior forecasting performance. With more training, the model can fix things better, and the changes it makes last for a long time. The results show that it is possible and useful to use deep learning to fix errors in meteorological remote-sensing data.
Classification and Mapping of Fuels in Mediterranean Forest Landscapes Using a UAV-LiDAR System and Integration Possibilities with Handheld Mobile Laser Scanner Systems
Authors: Hoffrén Raúl , Lamelas María Teresa , de la Riva Juan
Published: September 2024

In this study, we evaluated the capability of an unmanned aerial vehicle with a LiDAR sensor (UAV-LiDAR) to classify and map fuel types based on the Prometheus classification in Mediterranean environments. UAV data were collected across 73 forest plots located in NE of Spain. Furthermore, data collected from a handheld mobile laser scanner system (HMLS) in 43 out of the 73 plots were used to assess the extent of improvement in fuel identification resulting from the fusion of UAV and HMLS data. UAV three-dimensional point clouds (average density: 452 points/m2) allowed the generation of LiDAR metrics and indices related to vegetation structure. Additionally, voxels of 5 cm3 derived from HMLS three-dimensional point clouds (average density: 63,148 points/m2) facilitated the calculation of fuel volume at each Prometheus fuel type height stratum (0.60, 2, and 4 m). Two different models based on three machine learning techniques (Random Forest, Linear Support Vector Machine, and Radial Support Vector Machine) were employed to classify the fuel types: one including only UAV variables and the other incorporating HMLS volume data. The most relevant UAV variables introduced into the classification models, according to Dunn’s test, were the 99th and 10th percentile of the vegetation heights, the standard deviation of the heights, the total returns above 4 m, and the LiDAR Height Diversity Index (LHDI). The best classification using only UAV data was achieved with Random Forest (overall accuracy = 81.28%), with confusion mainly found between similar shrub and tree fuel types. The integration of fuel volume from HMLS data yielded a substantial improvement, especially in Random Forest (overall accuracy = 95.05%). The mapping of the UAV model correctly estimated the fuel types in the total area of 55 plots and at least part of the area of 59 plots. These results confirm that UAV-LiDAR systems are valid and operational tools for forest fuel classification and mapping and show how fusion with HMLS data refines the identification of fuel types, contributing to more effective management of forest ecosystems.
Design and Analysis of a Moon-Based Earth-Radiation Measurement System
Authors: Li Shuqi , Luo Zhitao , Liu Yanfeng , Fang Wei , Wang Yuwei , Jia Ruidong , Wu Duo , Song Baoqi , Yi Xiaolong , Ye Xin
Published: September 2024

This research project envisions using a lunar observation platform to measure the full-wave (0.2~100 μm) and shortwave (0.2~4.3 μm) radiation of the Earth, achieving an accurate estimation of the overall radiation budget of the Earth. Based on the lunar platform, the system analyzes Earth’s radiation characteristics and geometric attributes, as well as the sampling properties of observation times. Informed by these analyses, an Earth-facing optical radiation measurement system tailored to these specifications is designed. The optical system adopts an off-axis three-mirror configuration with a secondary image plane, incorporating a field stop at the primary image plane to effectively suppress solar stray light, scattered lunar surface light, and background radiation from the instrument itself, ensuring the satisfactory signal-to-noise ratio, detection sensitivity, and observation duration of the instrument. At the same time, stringent requirements are imposed for the surface treatments of instrument components and temperature control accuracy to further ensure accuracy. Simulation analyses confirm that the design satisfies requirements, achieving a measurement accuracy of better than 1% across the entire optical system. This Moon-based Earth-radiation measurement system, with capabilities for Earth-pointing tracking, radiation energy detection, and stray-light suppression, furnishes a more comprehensive dataset, helping to advance our understanding of the mechanisms driving global climate change
Central Difference Variational Filtering Based on Conjugate Gradient Method for Distributed Imaging Application
Authors: Ye Wen , Zhang Fubo , Chen Hongmei
Published: September 2024

The airborne distributed position and orientation system (ADPOS), which integrates multi-inertia measurement units (IMUs), a data-processing computer, and a Global Navigation Satellite System (GNSS), serves as a key sensor in new higher-resolution airborne remote sensing applications, such as array SAR and multi-node imaging loads. ADPOS can provide reliable, high-precision and high-frequency spatio-temporal reference information to realize multinode motion compensation with the various nonlinear filter estimation methods such as Central Difference Kalman Filtering (CDKF), and modified CDKF. Although these known nonlinear models demonstrate good performance, their noise estimation performance with its linear minimum variance estimation criterion is limited for ADPOS. For this reason, in this paper, Central Difference Variational Filtering (CDVF) based on the variational optimization process is presented. This method adopts the conjugate gradient algorithm to enhance the estimation performance for mean correction in the filtering update stage. On one hand, the proposed method achieves adaptability by estimating noise covariance through the variational optimization method. On the other hand, robustness is implemented under the minimum variance estimation criterion based on the conjugate gradient algorithm to suppress measurement noise. We conducted a real ADPOS flight test, and the experimental results show that the accuracy of the slave motion parameters has significantly improved compared to the current CDKF. Moreover, the compensation performance shows a clear enhancement.
A HRWS SAR Motion Compensation Method with Multichannel Phase Correction
Authors: Zhou Liming , Deng Minglong , He Jing , Wang Bing , Zhang Shengmiao , Liu Xuanyu , Wei Shunjun
Published: September 2024

The multichannel synthetic aperture radar (SAR) possesses the capability to acquire high-resolution, wide-swath SAR imagery, which has great potential for application. However, similar to traditional single-channel SAR systems, it suffers from imaging quality degradation due to motion errors. Many motion compensation algorithms have been used to improve the quality of single-channel SAR images, while fewer studies have been conducted on multichannel SAR motion compensation methods. The sub-image motion compensation method utilizes the single channel motion errors to perform multichannel motion errors compensation, considering that multiple channels have the same phase errors. To improve the quality of multichannel SAR imaging when multiple channel motion errors are inconsistent, this paper proposes a motion compensation method with multichannel phase correction for HRWS SAR. First, the method derives the phase errors estimation model via maximum sharpness to simultaneously estimate multichannel phase. Then, it compensates for the motion errors of all channels during backprojection imaging. The inconsistent motion errors of multiple channels can be compensated by estimating the phase errors of all channels, improving the image quality. The channel phase errors can be corrected while compensating for the motion errors. Moreover, the experimental results of point targets and complex scenes validate the effectiveness of the proposed method.
Measuring Biophysical Parameters of Wheat Canopy with MHz- and GHz-Frequency Range Impulses Employing Contactless GPR
Authors: Muzalevskiy Konstantin , Fomin Sergey , Karavayskiy Andrey , Leskova Julia , Lipshin Alexey , Romanov Vasily
Published: September 2024

In this paper, the advantages of the joint use of MHz- and GHz-frequency band impulses when employing contactless ground penetration radar (GPR) for the remote sensing of biomass, the height of the wheat canopy, and underlying soil moisture were experimentally investigated. A MHz-frequency band nanosecond impulse with a duration of 1.2 ns (average frequency of 750 MHz and spectrum bandwidth of 580 MHz, at a level of –6 dB) was emitted and received by a GPR OKO-3 equipped with an AB-900 M3 antenna unit. A GHz-frequency band sub-nanosecond impulse with a duration of 0.5 ns (average frequency of 3.2 GHz and spectral bandwidth of 1.36 GHz, at a level of −6 dB) was generated using a horn antenna and a Keysight FieldFox N9917B 18 GHz vector network analyzer. It has been shown that changes in the relative amplitudes and time delays of nanosecond impulses, reflected from a soil surface covered with wheat at a height from 0 to 87 cm and fresh above-ground biomass (AGB) from 0 to 1.5 kg/m2, do not exceed 6% and 0.09 ns, respectively. GPR nanosecond impulses reflected/scattered by the wheat canopy have not been detected. In this research, sub-nanosecond impulses reflected/scattered by the wheat canopy have been confidently identified and make it possible to measure the wheat height (fresh AGB up to 2.3 kg/m2 and height up to 104 cm) with a determination coefficient (R2) of ~0.99 and a bias of ~−7 cm, as well as fresh AGB where R2 = 0.97, with a bias = −0.09 kg/m2, and a root-mean-square error of 0.1 kg/m2. The joint use of impulses in two different MHz- and GHz-frequency bands will, in the future, make it possible to create UAV-based reflectometers for simultaneously mapping the soil moisture, height, and biomass of vegetation for precision farming systems.
Monitoring Temporal Sandbar and Shoreline Changes at Saint Louis, Senegal: Using Sentinel-2 Imagery (2015–2022)
Authors: Taveneau Adélaïde , Almar Rafael , Bergsma Erwin W. J. , Cissé Cheikh Omar Tidjani , Sy Boubou Aldiouma , Ndour Abdoulaye
Published: September 2024

Understanding beach dynamics, both in time and in space, is paramount to better understand how and when to intervene to improve coastal management strategies. Beach morphodynamics is expressed in a variety of ways. As indicators of beach change, we can measure the shoreline, the beach topography, and the bathymetry; e.g., in situ measurements rarely cover large extents, are often collected on a local scale (beach), and rarely cover a sufficient time span with a sufficient surveying frequency or a simultaneous measurement of the beach and bar system. Regular-revisit satellites, such as the ESA’s Sentinel-2 mission, provide the opportunity to regularly monitor both shoreline and sandbar dynamics, and the time span is increasing and likely to continue for the decades to come. Using the satellite-derived shoreline and bar position, here, we show that the shoreline and bar are intrinsically coupled. Using Sentinel-2 satellite imagery, we show that the actual erosion/accretion status of the beach at Saint Louis (Senegal) is strongly influenced by the sandbar dynamics. There is a coupled behavior in their seasonal evolution and trend. Our results show that a very large accretive wave of about 50 m observed on the beach is driven by a local welding of the inner sandbar to the beach. Finally, we conclude that this type of event could be anticipated by an analysis of the sandbar.
Multi-Domain Joint Synthetic Aperture Radar Ship Detection Method Integrating Complex Information with Deep Learning
Authors: Tian Chaoyang , Lv Zongsen , Xue Fengli , Wu Xiayi , Liu Dacheng
Published: September 2024

With the flourishing development of deep learning, synthetic aperture radar (SAR) ship detection based on this method has been widely applied across various domains. However, most deep-learning-based detection methods currently only use the amplitude information from SAR images. In fact, phase information and time-frequency features can also play a role in ship detection. Additionally, the background noise and the small size of ships also pose challenges to detection. Finally, satellite-based detection requires the model to be lightweight and capable of real-time processing. To address these difficulties, we propose a multi-domain joint SAR ship detection method that integrates complex information with deep learning. Based on the imaging mechanism of line-by-line scanning, we can first confirm the presence of ships within echo returns in the eigen-subspace domain, which can reduce detection time. Benefiting from the complex information of single-look complex (SLC) SAR images, we transform the echo returns containing ships into the time-frequency domain. In the time-frequency domain, ships exhibit distinctive features that are different from noise, without the limitation of size, which is highly advantageous for detection. Therefore, we constructed a time-frequency SAR image dataset (TFSID) using the images in the time-frequency domain, and utilizing the advantages of this dataset, we combined space-to-depth convolution (SPDConv) and Inception depthwise convolution (InceptionDWConv) to propose Efficient SPD-InceptionDWConv (ESIDConv). Using this module as the core, we proposed a lightweight SAR ship detector (LSDet) based on YOLOv5n. The detector achieves a detection accuracy of 99.5 with only 0.3 M parameters and 1.2 G operations on the dataset. Extensive experiments on different datasets demonstrated the superiority and effectiveness of our proposed method.
Triple-Camera Rectification for Depth Estimation Sensor
Authors: Jeon Minkyung , Park Jinhong , Kim Jin-Woo , Woo Sungmin
Published: September 2024

In this study, we propose a novel rectification method for three cameras using a single image for depth estimation. Stereo rectification serves as a fundamental preprocessing step for disparity estimation in stereoscopic cameras. However, off-the-shelf depth cameras often include an additional RGB camera for creating 3D point clouds. Existing rectification methods only align two cameras, necessitating an additional rectification and remapping process to align the third camera. Moreover, these methods require multiple reference checkerboard images for calibration and aim to minimize alignment errors, but often result in rotated images when there is significant misalignment between two cameras. In contrast, the proposed method simultaneously rectifies three cameras in a single shot without unnecessary rotation. To achieve this, we designed a lab environment with checkerboard settings and obtained multiple sample images from the cameras. The optimization function, designed specifically for rectification in stereo matching, enables the simultaneous alignment of all three cameras while ensuring performance comparable to traditional methods. Experimental results with real camera samples demonstrate the benefits of the proposed method and provide a detailed analysis of unnecessary rotations in the rectified images.
Thyroid Screening Techniques via Bioelectromagnetic Sensing: Imaging Models and Analytical and Computational Methods
Authors: Varvari Anna A. , Pitilakis Alexandros , Karatzidis Dimitrios I. , Kantartzis Nikolaos V.
Published: September 2024

The thyroid gland, which is sensitive to electromagnetic radiation, plays a crucial role in the regulation of the hormonal levels of the human body. Biosensors, on the other hand, are essential to access information and derive metrics about the condition of the thyroid by means of of non-invasive techniques. This paper provides a systematic overview of the recent literature on bioelectromagnetic models and methods designed specifically for the study of the thyroid. The survey, which was conducted within the scope of the radiation transmitter–thyroid model–sensor system, is centered around the following three primary axes: the bands of the frequency spectrum taken into account, the design of the model, and the methodology and/or algorithm. Our review highlights the areas of specialization and underscores the limitations of each model, including its time, memory, and resource requirements, as well as its performance. In this manner, this specific work may offer guidance throughout the selection process of a bioelectromagnetic model of the thyroid, as well as a technique for its analysis based on the available resources and the specific parameters of the electromagnetic problem under consideration.
Few-Shot Image Classification of Crop Diseases Based on Vision–Language Models
Authors: Zhou Yueyue , Yan Hongping , Ding Kun , Cai Tingting , Zhang Yan
Published: September 2024

Accurate crop disease classification is crucial for ensuring food security and enhancing agricultural productivity. However, the existing crop disease classification algorithms primarily focus on a single image modality and typically require a large number of samples. Our research counters these issues by using pre-trained Vision–Language Models (VLMs), which enhance the multimodal synergy for better crop disease classification than the traditional unimodal approaches. Firstly, we apply the multimodal model Qwen-VL to generate meticulous textual descriptions for representative disease images selected through clustering from the training set, which will serve as prompt text for generating classifier weights. Compared to solely using the language model for prompt text generation, this approach better captures and conveys fine-grained and image-specific information, thereby enhancing the prompt quality. Secondly, we integrate cross-attention and SE (Squeeze-and-Excitation) Attention into the training-free mode VLCD(Vision-Language model for Crop Disease classification) and the training-required mode VLCD-T (VLCD-Training), respectively, for prompt text processing, enhancing the classifier weights by emphasizing the key text features. The experimental outcomes conclusively prove our method’s heightened classification effectiveness in few-shot crop disease scenarios, tackling the data limitations and intricate disease recognition issues. It offers a pragmatic tool for agricultural pathology and reinforces the smart farming surveillance infrastructure.
Automatic Measurement of Seed Geometric Parameters Using a Handheld Scanner
Authors: Huang Xia , Zhu Fengbo , Wang Xiqi , Zhang Bo
Published: September 2024

Seed geometric parameters are important in yielding trait scorers, quantitative trait loci, and species recognition and classification. A novel method for automatic measurement of three-dimensional seed phenotypes is proposed. First, a handheld three-dimensional (3D) laser scanner is employed to obtain the seed point cloud data in batches. Second, a novel point cloud-based phenotyping method is proposed to obtain a single-seed 3D model and extract 33 phenotypes. It is connected by an automatic pipeline, including single-seed segmentation, pose normalization, point cloud completion by an ellipse fitting method, Poisson surface reconstruction, and automatic trait estimation. Finally, two statistical models (one using 11 size-related phenotypes and the other using 22 shape-related phenotypes) based on the principal component analysis method are built. A total of 3400 samples of eight kinds of seeds with different geometrical shapes are tested. Experiments show: (1) a single-seed 3D model can be automatically obtained with 0.017 mm point cloud completion error; (2) 33 phenotypes can be automatically extracted with high correlation compared with manual measurements (correlation coefficient (R2) above 0.9981 for size-related phenotypes and R2 above 0.8421 for shape-related phenotypes); and (3) two statistical models are successfully built to achieve seed shape description and quantification.
Data-Driven Power Prediction for Proton Exchange Membrane Fuel Cell Reactor Systems
Authors: He Shuai , Wu Xuejing , Bai Zexu , Zhang Jiyao , Lou Shinee , Mu Guoqing
Published: September 2024

Enhancing high-performance proton exchange membrane fuel cell (PEMFC) technology is crucial for the widespread adoption of hydrogen energy, a leading renewable resource. In this research, we introduce an innovative and cost-effective data-driven approach using the BP-AdaBoost algorithm to accurately predict the power output of hydrogen fuel cell stacks. The algorithm’s effectiveness was validated with experimental data obtained from an advanced fuel cell testing platform, where the predicted power outputs closely matched the actual results. Our findings demonstrate that the BP-AdaBoost algorithm achieved lower RMSE and MAE, along with higher R2, compared to other models, such as Partial Least Squares Regression (PLS), Support Vector Machine (SVM), and back propagation (BP) neural networks, when predicting power output for electric stacks of the same type. However, the algorithm’s performance decreased when applied to electric stacks with varying material compositions, highlighting the need for more sophisticated models to handle such diversity. These results underscore the potential of the BP-AdaBoost algorithm to improve PEMFC efficiency while also emphasizing the necessity for further research to develop models capable of accurately predicting power output across different types of PEMFC stacks.
Investigation of the Robust Integration of Distributed Fibre Optic Sensors in Structural Concrete Components
Authors: Wimmer Johannes , Braml Thomas
Published: September 2024

In recent times, the value of data has grown. This tendency is also observeable in the construction industry, where research and digitalisation are increasingly oriented towards the collection, processing and analysis of different types of data. In addition to planning data, measurement data is a main focus. fibre optic measurements offer a highly precise and comprehensive approach to data collection. It is, however, important to note that this technology is still in research regarding concrete structures. This paper presents two methods of integrating filigree sensors into concrete structures. The first approach entails wrapping a fibre around a tendon duct and analysing the installation and associated measurements. The second method involves bonding polyimide and acrylate-coated fibres with 2K epoxy and cyanoacrylate in the grooves of rebars, exposing them to chemical environments. The resulting measurement data is evaluated qualitatively and quantitatively to ascertain its resilience to environmental factors. These developed criteria are consolidated in a decision matrix. Fibre-adhesive combinations necessitate protection from chemical and mechanical influences. The limitations of the solutions are pointed out, and alternative options are proposed.
Gesture Recognition Framework for Teleoperation of Infrared (IR) Consumer Devices Using a Novel pFMG Soft Armband
Authors: Young Sam , Zhou Hao , Alici Gursel
Published: September 2024

Wearable technologies represent a significant advancement in facilitating communication between humans and machines. Powered by artificial intelligence (AI), human gestures detected by wearable sensors can provide people with seamless interaction with physical, digital, and mixed environments. In this paper, the foundations of a gesture-recognition framework for the teleoperation of infrared consumer electronics are established. This framework is based on force myography data of the upper forearm, acquired from a prototype novel soft pressure-based force myography (pFMG) armband. Here, the sub-processes of the framework are detailed, including the acquisition of infrared and force myography data; pre-processing; feature construction/selection; classifier selection; post-processing; and interfacing/actuation. The gesture recognition system is evaluated using 12 subjects’ force myography data obtained whilst performing five classes of gestures. Our results demonstrate an inter-session and inter-trial gesture average recognition accuracy of approximately 92.2% and 88.9%, respectively. The gesture recognition framework was successfully able to teleoperate several infrared consumer electronics as a wearable, safe and affordable human–machine interface system. The contribution of this study centres around proposing and demonstrating a user-centred design methodology to allow direct human–machine interaction and interface for applications where humans and devices are in the same loop or coexist, as typified between users and infrared-communicating devices in this study.
Advances in Group-10 Transition Metal Dichalcogenide PdSe2-Based Photodetectors: Outlook and Perspectives
Authors: Alam Tawsif Ibne , Liu Kunxuan , Hani Sumaiya Umme , Ahmed Safayet , Tsang Yuen Hong
Published: September 2024

The recent advancements in low-dimensional material-based photodetectors have provided valuable insights into the fundamental properties of these materials, the design of their device architectures, and the strategic engineering approaches that have facilitated their remarkable progress. This review work consolidates and provides a comprehensive review of the recent progress in group-10 two-dimensional (2D) palladium diselenide (PdSe2)-based photodetectors. This work first offers a general overview of the various types of PdSe2 photodetectors, including their operating mechanisms and key performance metrics. A detailed examination is then conducted on the physical properties of 2D PdSe2 material and how these metrics, such as structural characteristics, optical anisotropy, carrier mobility, and bandgap, influence photodetector device performance and potential avenues for enhancement. Furthermore, the study delves into the current methods for synthesizing PdSe2 material and constructing the corresponding photodetector devices. The documented device performances and application prospects are thoroughly discussed. Finally, this review speculates on the existing trends and future research opportunities in the field of 2D PdSe2 photodetectors. Potential directions for continued advancement of these optoelectronic devices are proposed and forecasted.
Monitoring Eastern White Pine Health by Using Field-Measured Foliar Traits and Hyperspectral Data
Authors: Timalsina Sudan , Rahimzadeh-Bajgiran Parinaz , Das Pulakesh , Meireles José Eduardo , Bhattarai Rajeev
Published: September 2024

Canopy foliar traits serve as crucial indicators of plant health and productivity, forming a vital link between plant conditions and ecosystem dynamics. In this study, the use of hyperspectral data and foliar traits for white pine needle damage (WPND) detection was investigated for the first time. Eastern White Pine (Pinus strobus L., EWP), a species of ecological and economic significance in the Northeastern USA, faces a growing threat from WPND. We used field-measured leaf traits and hyperspectral remote sensing data using parametric and non-parametric methods for WPND detection in the green stage. Results indicated that the random forest (RF) model based solely on remotely sensed spectral vegetation indices (SVIs) demonstrated the highest accuracy of nearly 87% and Kappa coefficient (K) of 0.68 for disease classification into asymptomatic and symptomatic classes. The combination of field-measured traits and remote sensing data indicated an overall accuracy of 77% with a Kappa coefficient (K) of 0.46. These findings contribute valuable insights and highlight the potential of both field-derived foliar and remote sensing data for WPND detection in EWP. With an exponential rise in forest pests and pathogens in recent years, remote sensing techniques can prove beneficial for the timely and accurate detection of disease and improved forest management practices.
eHealth Assistant AI Chatbot Using a Large Language Model to Provide Personalized Answers through Secure Decentralized Communication
Authors: Pap Iuliu Alexandru , Oniga Stefan
Published: September 2024

In this paper, we present the implementation of an artificial intelligence health assistant designed to complement a previously built eHealth data acquisition system for helping both patients and medical staff. The assistant allows users to query medical information in a smarter, more natural way, respecting patient privacy and using secure communications through a chat style interface based on the Matrix decentralized open protocol. Assistant responses are constructed locally by an interchangeable large language model (LLM) that can form rich and complete answers like most human medical staff would. Restricted access to patient information and other related resources is provided to the LLM through various methods for it to be able to respond correctly based on specific patient data. The Matrix protocol allows deployments to be run in an open federation; hence, the system can be easily scaled.
an-QNA: An Adaptive Nesterov Quasi-Newton Acceleration-Optimized CMOS LNA for 65 nm Automotive Radar Applications
Authors: Aras Unal , Woo Lee Sun , Delwar Tahesin Samira , Siddique Abrar , Jana Anindya , Lee Yangwon , Ryu Jee-Youl
Published: September 2024

An adaptive Nesterov quasi-Newton acceleration (an-QNA)-optimized low-noise amplifier (LNA) is proposed in this paper. An optimized single-ended-to-differential two-stage LNA circuit is presented. It includes an improved post-linearization (IPL) technique to enhance the linearity. Traditional methods like conventional quasi-Newton (c-QN) often suffer from slow convergence and the tendency to get trapped in local minima. However, the proposed an-QNA method significantly accelerates the convergence speed. Furthermore, in this paper, modifications have been made to the an-QNA algorithm using a quadratic estimation to guarantee global convergence. The optimized an-QNA-based LNA, using standard 65 nm CMOS technology, achieves a simulated gain of 17.5 dB, a noise figure (NF) of 3.7 dB, and a 1 dB input compression point (IP1dB) of −13.1 dBm. It is also noted that the optimized LNA achieves a measured gain of 12.9 dB and an NF of 4.98 dB, and the IP1dB is −17.8 dB. The optimized LNA has a chip area of 0.67 mm2.