bluuebunny/crossref_metadata_2025 huggingface dataset:
Urea Coated with Polyaspartic Acid-Chitosan Increases Foxtail Millet (Setaria italica L. Beauv.) Grain Yield by Improving Nitrogen Metabolism
Authors: Lu Lin , Wang Qi , Zhang Wei , Gao Ming , Xv Yanli , Li Shujie , Dong Haosheng , Chen Disu , Yan Peng , Dong Zhiqiang
Published: January 2024

Innovative measures of nitrogen (N) fertilization to increase season-long N availability is essential for gaining the optimal foxtail millet (Setaria italica L. Beauv.) productivity and N use efficiency. A split plot field experiment was conducted using the foxtail millet variety Huayougu 9 in 2020 and 2021 in Northeast China to clarify the physiological mechanism of a novel polyaspartic acid–chitosan (PAC)-coated urea on N assimilation and utilization from foxtail millet. Conventional N fertilizer (CN) and the urea-coated -PAC treatments were tested under six nitrogen fertilizer application levels of 0, 75, 112.5, 150, 225, and 337.5 kg N ha−1. The results showed that compared to CN, PN increased the foxtail millet yield by 5.53–15.75% and 10.43–16.17% in 2020 and 2021, respectively. PN increased the leaf area index and dry matter accumulation by 7.81–18.15% and 12.91–41.92%, respectively. PN also enhanced the activities of nitrate reductase, glutamine synthetase, glutamic oxaloacetic transaminase, and glutamic–pyruvic transaminase, thereby increasing the soluble protein in the leaf, plant, and grain N content at harvest compared to CN. Consequently, partial factor productivity from applied N, the agronomic efficiency of applied N, recovery efficiency of applied N, and physiological efficiency of applied N of foxtail millet under PN treatments compared to CN were increased. The improvement effect of the items above was more noticeable under the low–middle N application levels (75, 112.5, and 150 kg N ha−1). In conclusion, the PAC could achieve the goal of high yield and high N use efficiency in foxtail millet under the background of a one-time basic fertilizer application.
Volatile Organic Compounds Emitted by Flowers: Ecological Roles, Production by Plants, Extraction, and Identification
Authors: Lo Mame-Marietou , Benfodda Zohra , Molinié Roland , Meffre Patrick
Published: January 2024

Volatile organic compounds (VOCs) with a large chemical diversity are emitted by plant flowers. These compounds play an important role in the ecology of plants. This review presents the different ecological roles of VOCs present in the odor plumes of plant flowers, such as pollination, defense, adaptation to their environment, and communication with other organisms. The production and accumulation sites of VOCs in plants with their spatial and temporal variations, including environmental issues, are also summarized. To evaluate the qualitative and quantitative chemical composition of VOCs, several methods of extraction and analysis were used. Headspace (HS) sampling coupled with solid phase microextraction (SPME) is now well-developed for the extraction process. Parameters are known, and several fibers are now available to optimize this extraction. Most of the time, SPME is coupled with gas chromatography-mass spectrometry (GC-MS) to determine the structural identification of the VOCs, paying attention to the use of several complementary methods for identification like the use of databases, retention indices, and, when available, comparison with authentic standards analyses. The development of the knowledge on VOCs emitted by flowers is of great importance for plant ecology in the context of environmental and climate changes.
Genome-Wide Identification of the Ferric Chelate Reductase (FRO) Gene Family in Peanut and Its Diploid Progenitors: Structure, Evolution, and Expression Profiles
Authors: Guan Junhua , Zhang Zheng , Shi Gangrong
Published: January 2024

The ferric chelate reductase (FRO) family plays a vital role in metal ion homeostasis in a variety of locations in the plants. However, little is known about this family in peanut (Arachis hypogaea). This study aimed to identify FRO genes from the genomes of peanut and the two diploid progenitors (A. duranensis and A. ipaensis) and to analyze their gene/protein structures and evolution. In addition, transcriptional responses of AhFRO genes to Fe deficiency and/or Cu exposure were investigated in two peanut cultivars with different Fe deficiency tolerance (Silihong and Fenghua 1). A total of nine, four, and three FRO genes were identified in peanut, A. duranensis, and A. ipaensis, respectively, which were divided into three groups. Most AhFRO genes underwent WGD/segmental duplication, leading to the expansion of the AhFRO gene family. In general, clustered members share similar gene/protein structures. However, significant divergences occurred in AhFRO2 genes. Three out of five AhFRO2 genes were lowly expressed in all tissues under normal conditions, which may be beneficial for avoiding gene loss. Transcription analysis revealed that AhFRO2 and AhFRO7 genes might be involved in the reduction of Fe/Cu in plasma membranes and plastids, respectively. AhFRO8 genes appear to confer Fe reduction in the mitochondria. Moreover, Fe deficiency induced an increase of Cu accumulation in peanut plants in which AhFRO2.2/2.4/2.5 and FRO7.1/7.2 might be involved. Our findings provided new clues for further understanding the roles of AhFRO genes in the Fe/Cu interaction in peanut.
RNA Sequencing Analysis and Verification of Paeonia ostii ‘Fengdan’ CuZn Superoxide Dismutase (PoSOD) Genes in Root Development
Authors: Wang Jiange , Song Yinglong , Wang Zheng , Shi Liyun , Yu Shuiyan , Xu Yufeng , Wang Guiqing , He Dan , Jiang Liwei , Shang Wenqian , He Songlin
Published: January 2024

Tree peony (Paeonia suffruticosa) is a significant medicinal plant. However, the low rooting number is a bottleneck problem in the micropropagation protocols of P. ostii ‘Fengdan’. The activity of superoxide dismutase (SOD) is closely related to root development. But research on the SOD gene’s impact on rooting is still lacking. In this study, RNA sequencing (RNA-seq) was used to analyze the four crucial stages of root development in P. ostii ‘Fengdan’ seedlings, including the early root primordium formation stage (Gmfq), root primordium formation stage (Gmf), root protrusion stage (Gtq), and root outgrowth stage (Gzc). A total of 141.77 GB of data were obtained; 71,718, 29,804, and 24,712 differentially expressed genes (DEGs) were identified in the comparison groups of Gmfq vs. Gmf, Gmf vs. Gtq, and Gtq vs. Gzc, respectively. Among the 20 most highly expressed DEGs in the three comparison groups, only the CuZnSOD gene (SUB13202229, PoSOD) was found to be significantly expressed in Gtq vs. Gzc. The overexpression of PoSOD increased the number of adventitious roots and promoted the activities of peroxidase (POD) and SOD in P. ostii ‘Fengdan’. The gene ADVENTITIOUS ROOTING RELATED OXYGENASE1 (PoARRO-1), which is closely associated with the development of adventitious roots, was also significantly upregulated in overexpressing PoSOD plants. Furthermore, PoSOD interacted with PoARRO-1 in yeast two-hybrid (Y2H) and biomolecular luminescence complementation (BiFC) assays. In conclusion, PoSOD could interact with PoARRO-1 and enhance the root development of tube plantlets in P. ostii ‘Fengdan’. This study will help us to preliminarily understand the molecular mechanism of adventitious root formation and improve the root quality of tree peony and other medicinal plants.
Recyclable Multifunctional Nanocomposites Based on Carbon Nanotube Reinforced Vitrimers with Shape Memory and Joule Heating Capabilities
Authors: Cortés Alejandro , Sánchez-Romate Xoan F. , Martinez-Diaz David , Prolongo Silvia G. , Jiménez-Suárez Alberto
Published: January 2024

The present study focuses on the multifunctional capabilities of carbon nanotube (CNT)-reinforced vitrimers. More specifically, the thermomechanical properties, the Joule effect heating capabilities, the electrical conductivity, the shape memory, and the chemical recycling capacity are explored as a function of the CNT content and the NH2/epoxy ratio. It is observed that the electrical conductivity increases with the CNT content due to a higher number of electrical pathways, while the effect of the NH2/epoxy ratio is not as prevalent. Moreover, the Tg of the material decreases when increasing the NH2/epoxy ratio due to the lower cross-link density, whereas the effect of the CNTs is more complex, in some cases promoting a steric hindrance. The results of Joule heating tests prove the suitability of the proposed materials for resistive heating, reaching average temperatures above 200 °C when applying 100 V for the most electrically conductive samples. Shape memory behavior shows an outstanding shape fixity ratio in every case (around 100%) and a higher shape recovery ratio (95% for the best-tested condition) when decreasing the NH2/epoxy ratio and increasing the CNT content, as both hinder the rearrangement of the dynamic bonds. Finally, the results of the recyclability tests show the ability to regain the nanoreinforcement for their further use. Therefore, from a multifunctional analysis, it can be stated that the proposed materials present promising properties for a wide range of applications, such as Anti-icing and De-icing Systems (ADIS), Joule heating devices for comfort or thermotherapy, or self-deployable structures, among others.
Preparation of Polyimide/Ionic Liquid Hybrid Membrane for CO2/CH4 Separation
Authors: Du Xiaoyu , Zhao Shijun , Qu Yanqing , Jia Hongge , Xu Shuangping , Zhang Mingyu , Geng Guoliang
Published: January 2024

Imidazole ionic liquids (ILs) have good affinity and good solubility for carbon dioxide (CO2). Such ionic liquids, combined with polyimide membrane materials, can solve the problem that, today, CO2 is difficult to separate and recover. In this study, the ionic liquid (IL) of 1-ethyl-3-methylimidazolium tetrafluoroborate (IL1), 1-pentyl-3-methylimidazolium tetrafluoroborate (IL2), 1-octyl-3-methylimidazolium tetrafluoroborate (IL3), and 1-dodecylimidazolium tetrafluoroborate (IL4) with different contents were added to a polyimide matrix, and a series of polyimide membranes blended with ionic liquid were prepared using a high-speed mixer. The mechanical properties and gas separation permeability of the membranes were investigated. Among them, the selectivity of the PI/IL3 membrane for CO2/CH4 was 180.55, which was 2.5 times higher than the PI membrane, and its CO2 permeability was 16.25 Barrer, which exceeded the Robeson curve in 2008; the separation performance of the membrane was the best in this work.
Effects of Ferric Ions on Cellulose Nanocrystalline-Based Chiral Nematic Film and Its Applications
Authors: Wang Shuaiqi , Lin Bingqun , Zeng Yihan , Pan Mingzhu
Published: January 2024

Chiral nematic materials have been attracting attention in fields of advanced functional applications due to their unique iridescent colors and tunable helical structure. A precisely decreased pitch is of importance for construction and applications of chiral nematic materials; however, it remains a huge challenge. Herein, cellulose nanocrystal (CNC) is selected as a constructed matrix for chiral nematic films, and ferric chloride (FeCl3) is used as a modification agent. We investigate the effects of the ferric ion loads on the helical structure and optical characteristics of iridescent film. Subsequently, the influence of ferric ions on the assembly process of CNC liquid crystal and the regulation of the structure color of self-assembled monolayers are discussed. Therefore, the CNC/FeCl3 chiral nematic films showed a blueshifted structural color from orange to blue, which highlights a simple route to achieve the regulation of decreased pitch. Further, we have applied this CNC/FeCl3 chiral nematic film for benzene gas detection. The sensing performance shows that the CNC/FeCl3 chiral nematic film reacts to benzene gas, which can be merged into the nematic layer of the CNC and trigger the iron ions chelated on the CNC, consequently arousing the redshift of the reflected wavelength and the effective colorimetric transition. This CNC/FeCl3 chiral nematic film is anticipated to boost a new gas sensing mechanism for faster and more effective in-situ qualitative investigations.
Recent Progress in Electrochemical CO2 Reduction at Different Electrocatalyst Materials
Authors: Barcelos Marcela Miranda , Vasconcellos Maria de Lourdes Soprani , Ribeiro Josimar
Published: January 2024

Given the environmental problems caused by burning fossil fuels, it is believed that converting carbon dioxide (CO2) into chemical inputs is a great ally to generating clean energy. In this way, investigative studies related to electrochemical CO2 reduction (CO2RE) concerning the behavior of metal catalysts have received attention about the processes involved. CO2RE can be an important tool to mitigate the presence of this gas in the Earth’s atmosphere. Given these considerations, in this review, we report the main catalysts used to act as CO2RE. Among them, we emphasize catalysts based on Ni, Zn, and Cu, which encompass the main properties related to the electrochemical conversion of CO2. Regarding the Cu-based catalyst, it presents high conversion efficiency but low selectivity. Furthermore, we also describe the main mechanisms related to the electrochemical conversion of CO2.
Digital Analysis of Occlusion in Fixed Partial Implant Prostheses: How to Overcome Age-Related Changes in the Stomatognathic System
Authors: Dib Zakkour Juan , Dib Zakkour Sara , Montero Javier , García-Cenador Begoña , Flores-Fraile Javier , Dib Zaitun Abraham
Published: January 2024

Due to their lack of periodontal ligaments (PLs) and the differences between dental implants and natural teeth, it is necessary to improve and generate a new occlusal scheme to prolong the life of implants and prostheses. The age and the sex of patients must be considered because of their effects on the stomatognathic system’s physiology. Operators must manage all the changes to obtain good sensations during mastication and a better occlusal scheme for implanting fixed partial prostheses. Dentists should try to protect this type of prosthesis using adjacent teeth and the PL. This is why new digital systems were created. The combination of T-Scan® (digital software for occlusal analysis) and electromyography (EMG) could allow doctors to find areas where it is necessary to act and to find suitable solutions for the problems generated by using conventional methods of occlusal analysis (such as articulating paper). In this study, a new method for establishing occlusion on fixed partial implant prostheses has been created, combining digital systems with conventional articulating paper. This method consists of asking the patient to bite down with different forces and situations in an attempt to achieve Implant-Protected Occlusion (IPO). The use of digital systems has been shown to be more effective than using only conventional systems. This new method allows a safer mode of occlusion which protects implants and prostheses, saving all the differences between them and natural teeth, and increasing the satisfaction of patients. This method also helps to overcome the changes in the stomatognathic system as age increases, adjusting the occlusion to changes in PLs with age.
Spirituality as a Predictor of Well-Being, Mental Distress or Both: A Four-Week Follow-Up Study in a Sample of Dutch and Belgian Adults
Authors: Huijs Thijs , Braam Arjan , Kruizinga Renske , Jacobs Nele , Reijnders Jennifer , Simons Marianne
Published: January 2024

In general, studies of spirituality show positive associations with measures of well-being, but less is known about the possible role of mental distress in this association. Following the two-continua model of mental health, the current quantitative four-week follow-up study examines how spirituality is associated with well-being and mental distress. Spirituality is measured using the Spirituality Attitude and Interest List questionnaire (SAIL), well-being by the Dutch Mental Health Continuum-Short Form (MHCSF-SF), and mental distress by the Symptom Questionnaire (SQ-48). At baseline, 874 adults from the Netherlands and Belgium completed the online questionnaire; four weeks later, 560 participants completed the follow-up questionnaire. Multiple regression analyses showed that spirituality at baseline, and in particular the subscale on ‘meaning in life,’ predicted higher well-being scores at follow-up after adjustment for baseline well-being scores. Spirituality also predicted changes in mental distress scores, in particular on the subscales of trust and transcendent experience. However, these associations were in opposite directions. Trust was associated with a small decrease in mental distress over time and transcendent experience was associated with a small increase in mental distress over time. The results confirm the importance of meaning in life, trust, and transcendent experience as elements of mental health.
A Two Stage Nonlinear I/O Decoupling and Partially Wireless Controller for Differential Drive Mobile Robots
Authors: Kouvakas Nikolaos D. , Koumboulis Fotis N. , Sigalas John
Published: January 2024

Differential drive mobile robots, being widely used in several industrial and domestic applications, are increasingly demanding when concerning precision and satisfactory maneuverability. In the present paper, the problem of independently controlling the velocity and orientation angle of a differential drive mobile robot is investigated by developing an appropriate two stage nonlinear controller embedded on board and also by using the measurements of the speed and accelerator of the two wheels, as well as taking remote measurements of the orientation angle and its rate. The model of the system is presented in a nonlinear state space form that includes unknown additive terms arising from external disturbances and actuator faults. Based on the nonlinear model of the system, the respective I/O relation is derived, and a two-stage nonlinear measurable output feedback controller, analyzed into an internal and an external controller, is designed. The internal controller aims to produce a decoupled inner closed-loop system of linear form, regulating the linear velocity and angular velocity of the mobile robot independently. The internal controller is of the nonlinear PD type and uses real time measurements of the angular velocities of the active wheels of the vehicle, as well as the respective accelerations. The external controller aims toward the regulation of the orientation angle of the vehicle. It is of a linear, delayed PD feedback form, offering feedback from the remote measurements of the orientation angle and angular velocity of the vehicle, which are transmitted to the controller through a wireless network. Analytic formulae are derived for the parameters of the external controller to ensure the stability of the closed-loop system, even in the presence of the wireless transmission delays, as well as asymptotic command following for the orientation angle. To compensate for measurement noise, external disturbances, and actuator faults, a metaheuristic algorithm is proposed to evaluate the remaining free controller parameters. The performance of the proposed control scheme is evaluated through a series of computational experiments, demonstrating satisfactory behavior.
Unified Interpretable Deep Network for Joint Super-Resolution and Pansharpening
Authors: Yu Dian , Zhang Wei , Xu Mingzhu , Tian Xin , Jiang Hao
Published: January 2024

Joint super-resolution and pansharpening (JSP) brings new insight into the spatial improvement of multispectral images. How to efficiently balance the spatial and spectral qualities in JSP is important for deep learning-based approaches. To address this problem, we propose a unified interpretable deep network for JSP, named UIJSP-Net. First, we formulate the JSP problem as an optimization problem in a specially designed physical model based on the relationship among the JSP result, the multispectral image, and the panchromatic image. In particular, two deep priors are utilized to describe latent distributions of different variables, which can improve the accuracy of the physical model. Furthermore, we adopt the alternating direction method of multipliers to solve the above optimization problem, where a series of iterative steps are generated. Finally, we design UIJSP-Net by unfolding these iterative steps into multiple corresponding stages in a unified network. Because UIJSP-Net has clear physical meanings, the spatial resolution of multispectral images can be efficiently improved while the spectral information can be kept as well. Extensive experimental results are carried out on both simulated and real datasets to demonstrate the superiority of UIJSP-Net over other state-of-the-art methods from qualitative and quantitative aspects.
Calculation of CO2 Emissions from China at Regional Scales Using Remote Sensing Data
Authors: Li Yaqian , Chen Yile , Cai Qi , Zhu Liujun
Published: January 2024

Since industrialization, global carbon dioxide (CO2) emissions have been rising substantially, playing an increasingly important role in global warming and climate change. As the largest CO2 emitter, China has proposed an ambitious reduction plan of peaking before 2030 and achieving carbon neutrality by 2060. Calculation of CO2 emissions inventories at regional scales (e.g., city and county) has great significance in terms of China’s regional carbon policies as well as in achieving the national targets. However, most of the existing emissions data were calculated based on fossil fuel consumptions and were thus limited to the provinces in China, making it challenging to compare and analyze the CO2 emissions of different cities and counties within a province. Machine learning methods provided a promising alternative but were still suffering from the lack of availability of training samples at city or county scales. Accordingly, this study proposed to use the energy consumption per unit GDP (ECpGDP) and GDP to calculate the effective CO2 emissions, which are the CO2 emissions if all consumed energy was generated by standard coal. Random forest models were then trained to establish relationships between the remote sensing night-light data and effective CO2 emissions. A total of eight predictor variables were used, including the night-light data, the urbanization ratio, the population density, the type of sensors and administrative divisions, latitude, longitude, and the area of each city or county. Meanwhile, the mean value of the five-fold cross-validation model was used as the estimated effective CO2 emissions in order to avoid overfitting. The evaluation showed a root mean square error (RMSE) of 10.972 million tons and an overall Pearson’s correlation coefficient (R) of 0.952, with satisfactory spatial and temporal consistency. The effective CO2 emissions of 349 cities and 2843 counties in China during 1992–2021 were obtained, providing a promising dataset for CO2-emission-related applications.
Modeling the Land Cover Change in Chesapeake Bay Area for Precision Conservation and Green Infrastructure Planning
Authors: Zhang Xinge , Li Kenan , Dai Yuewen , Yi Shujing
Published: January 2024

This study developed a precise land cover model to predict the shifts from pervious to impervious surfaces in the Chesapeake watershed. Utilizing 1 m resolution longitudinal land cover data from the Conservation Innovation Center (CIC), our model achieved impressive balanced accuracies: 98.96% for Portsmouth, 99.88% for Isle of Wight, and 95.76% for James City. Based on the analysis of feature importance, our model also assessed the influence of local socioeconomic and environmental factors, along with their spatial lags as represented by natural splines. These outcomes and findings are crucial for land use and environmental planners, providing them with tools to identify areas of urban expansion and to devise appropriate green infrastructure strategies, while also prioritizing land conservation. Additionally, our model offers insights into the socioeconomic and environmental drivers behind land cover changes. Its adaptability at the county level and reliance on widely available data make it a viable option for other municipalities within the Chesapeake basin to conduct similar analyses. As a proof-of-concept, this project underscores the potential of precision conservation in facilitating both land preservation and the advancement of green infrastructure planning, thus serving as a valuable resource for policymakers and planners in the region.
Lake Ice Thickness Retrieval Method with ICESat-2-Assisted CyroSat-2 Echo Peak Selection
Authors: Ye Hao , Jin Guowang , Zhang Hongmin , Xiong Xin , Li Jiahao , Wang Jiajun
Published: January 2024

Lake ice thickness (LIT) is one of the key climate variables in the lake ice domain, but there are currently large uncertainties in the retrieval of LIT. We present and validate a new LIT retrieval method that utilizes ICESat-2 data to assist CryoSat-2 echo peak selection, aiming to improve the accuracy of LIT retrieval and enable data acquisition without on-site measurements. The method involves screening out similar ICESat-2 and CryoSat-2 tracks based on time and space constraints. It also involves dynamically adjusting the range constraint window of CryoSat-2 waveforms based on the high-precision lake ice surface ellipsoid height obtained from ICESat-2/ATL06 data. Within this range constraint window, the peak selection strategy is used to determine the scattering interfaces between snow-ice and ice-water. By utilizing the distance between the scattering horizons, the thickness of the lake ice can be determined. We performed the ice thickness retrieval experiment for Baker Lake in winter and verified it against the on-site measurement data. The results showed that the accuracy was about 0.143 m. At the same time, we performed the ice thickness retrieval experiment for Great Bear Lake (GBL), which does not have on-site measurement data, and compared it with the climate change trend of GBL. The results showed that the retrieval results were consistent with the climate change trend of GBL, confirming the validity of the proposed method.
Detection of Coastal Erosion and Progradation in the Colombian ‘Atrato River’ Delta by Using Sentinel-1 Synthetic Aperture Radar Data
Authors: Vásquez-Salazar Rubén Darío , Cardona-Mesa Ahmed Alejandro , Valdés-Quintero Juan , Olmos-Severiche César , Gómez Luis , Travieso-González Carlos M. , Díaz-Paz Jean Pierre , Espinosa-Ovideo Jorge Ernesto , Diez-Rendón Lorena , Garavito-González Andrés F. , Vásquez-Cano Esteban
Published: January 2024

This paper presents a methodology to detect the coastal erosion and progradation effects in the ‘Atrato River’ delta, located in the Gulf of Urabá in Colombia, using SAR (Synthetic Aperture Radar) images. Erosion is the physical–mechanical loss of the soil that affects its functions and ecosystem services while producing a reduction in its productive capacity. Progradation is the deposition of layers in the basinward direction while moving coastward. Other studies have investigated these two phenomena using optical images, encountering difficulties due to the persistent presence of clouds in this region. In order to avoid the cloud effects, in this study, we used 16 Sentinel 1 SAR images with two different polarizations between 2016 and 2023. First, each image was rescaled from 0 to 255, then the image was despeckled by a deep learning (DL) model. Afterwards, a single RGB image was composed with the filtered polarizations. Next, a classifier with 99% accuracy based on Otsu’s method was used to determine whether each pixel was water or not. Then, the classified image was registered to a reference one using Oriented FAST and Rotated BRIEF (ORB) descriptor. Finally, a multitemporal analysis was performed by comparing every image to the previous one to identify the studied phenomena, calculating areas. Also, all images were integrated to obtain a heatmap that showed the overall changes across eight years (2016–2023) in a single image. The multitemporal analysis performed found that the newly created mouth is the most active area for these processes, coinciding with other studies. In addition, a comparison of these findings with the Oceanic Niño Index (ONI) showed a relative delayed coupling to the erosion process and a coupling of progradation with dry and wet seasons.
LDnADMM-Net: A Denoising Unfolded Deep Neural Network for Direction-of-Arrival Estimations in A Low Signal-to-Noise Ratio
Authors: Liang Can , Liu Mingxuan , Li Yang , Wang Yanhua , Hu Xueyao
Published: January 2024

In this paper, we explore the problem of direction-of-arrival (DOA) estimation for a non-uniform linear array (NULA) under strong noise. The compressed sensing (CS)-based methods are widely used in NULA DOA estimations. However, these methods commonly rely on the tuning of parameters, which are hard to fine-tune. Additionally, these methods lack robustness under strong noise. To address these issues, this paper proposes a novel DOA estimation approach using a deep neural network (DNN) for a NULA in a low SNR. The proposed network is designed based on the denoising convolutional neural network (DnCNN) and the alternating direction method of multipliers (ADMM), which is dubbed as LDnADMM-Net. First, we construct an unfolded DNN architecture that mimics the behavior of the iterative processing of an ADMM. In this way, the parameters of an ADMM can be transformed into the network weights, and thus we can adaptively optimize these parameters through network training. Then, we employ the DnCNN to develop a denoising module (DnM) and integrate it into the unfolded DNN. Using this DnM, we can enhance the anti-noise ability of the proposed network and obtain a robust DOA estimation in a low SNR. The simulation and experimental results show that the proposed LDnADMM-Net can obtain high-accuracy and super-resolution DOA estimations for a NULA with strong robustness in a low signal-to-noise ratio (SNR).
Application of WRF-LES on the Simulation of Seasonal Characteristics of Atmospheric Boundary Layer Structure in Taklamakan Desert
Authors: Xu Xiaoyi , Li Xin , Zhang Yuanjie , Gao Zhiqiu , Sun Jingxi
Published: January 2024

The lack of observational data in Taklamakan Desert makes it very difficult to study its unique boundary layer structure. As a common means of supplementing observational data, the mesoscale boundary layer parameterization scheme in the numerical model method is difficult to capture small-scale turbulent processes, which may lead to large deviations in simulation. In order to obtain more accurate simulation data of desert atmospheric boundary layer, nested LES into WRF (WRF-LES) was configured to simulate the seasonal variations in Taklamakan Desert. By comparing LES with the conventional boundary layer parameterization scheme, the error characteristics between the two schemes are analyzed. The results show that LES exhibits superior performance in solving key atmospheric features such as small-scale processes and low-level jet streams. The simulation results in winter and summer have great uncertainty due to the boundary condition errors, respectively. LES also shows the maximum and minimum optimization degree in summer and winter, respectively, while the simulation results in spring and autumn are relatively stable. In the analysis of turbulence parameters, there are clear seasonal differences in turbulence characteristics, and the intensity of turbulence in summer is significantly higher than that in other seasons. When turbulent activity is strong, the difference in potential temperature and horizontal wind speed simulated between the two schemes is closely related to intense turbulent kinetic energy in LES. More accurate turbulence reproduced in LES leads to the better potential temperature and horizontal wind speed simulations in summer. In addition, large-scale cloud systems can lead to considerable simulation bias. Neither scheme can accurately simulate the cloud emergence process, and large differences between the two schemes occur at this point.
YOLO-IHD: Improved Real-Time Human Detection System for Indoor Drones
Authors: Kucukayan Gokhan , Karacan Hacer
Published: January 2024

In the field of unmanned systems, the combination of artificial intelligence with self-operating functionalities is becoming increasingly important. This study introduces a new method for autonomously detecting humans in indoor environments using unmanned aerial vehicles, utilizing the advanced techniques of a deep learning framework commonly known as “You Only Look Once” (YOLO). The key contribution of this research is the development of a new model (YOLO-IHD), specifically designed for human detection in indoor using drones. This model is created using a unique dataset gathered from aerial vehicle footage in various indoor environments. It significantly improves the accuracy of detecting people in these complex environments. The model achieves a notable advancement in autonomous monitoring and search-and-rescue operations, highlighting its importance for tasks that require precise human detection. The improved performance of the new model is due to its optimized convolutional layers and an attention mechanism that process complex visual data from indoor environments. This results in more dependable operation in critical situations like disaster response and indoor rescue missions. Moreover, when combined with an accelerating processing library, the model shows enhanced real-time detection capabilities and operates effectively in a real-world environment with a custom designed indoor drone. This research lays the groundwork for future enhancements designed to significantly increase the model’s accuracy and the reliability of indoor human detection in real-time drone applications.
An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation
Authors: Jian Zini , Song Tianxiang , Zhang Zhihui , Ai Zhao , Zhao Heng , Tang Man , Liu Kan
Published: January 2024

Fluorescence in situ hybridization (FISH) is a powerful cytogenetic method used to precisely detect and localize nucleic acid sequences. This technique is proving to be an invaluable tool in medical diagnostics and has made significant contributions to biology and the life sciences. However, the number of cells is large and the nucleic acid sequences are disorganized in the FISH images taken using the microscope. Processing and analyzing images is a time-consuming and laborious task for researchers, as it can easily tire the human eyes and lead to errors in judgment. In recent years, deep learning has made significant progress in the field of medical imaging, especially the successful application of introducing the attention mechanism. The attention mechanism, as a key component of deep learning, improves the understanding and interpretation of medical images by giving different weights to different regions of the image, enabling the model to focus more on important features. To address the challenges in FISH image analysis, we combined medical imaging with deep learning to develop the SEAM-Unet++ automated cell contour segmentation algorithm with integrated attention mechanism. The significant advantage of this algorithm is that it improves the accuracy of cell contours in FISH images. Experiments have demonstrated that by introducing the attention mechanism, our method is able to segment cells that are adherent to each other more efficiently.
PUFchain 3.0: Hardware-Assisted Distributed Ledger for Robust Authentication in Healthcare Cyber–Physical Systems
Authors: Bathalapalli Venkata K. V. V. , Mohanty Saraju P. , Kougianos Elias , Iyer Vasanth , Rout Bibhudutta
Published: January 2024

This article presents a novel hardware-assisted distributed ledger-based solution for simultaneous device and data security in smart healthcare. This article presents a novel architecture that integrates PUF, blockchain, and Tangle for Security-by-Design (SbD) of healthcare cyber–physical systems (H-CPSs). Healthcare systems around the world have undergone massive technological transformation and have seen growing adoption with the advancement of Internet-of-Medical Things (IoMT). The technological transformation of healthcare systems to telemedicine, e-health, connected health, and remote health is being made possible with the sophisticated integration of IoMT with machine learning, big data, artificial intelligence (AI), and other technologies. As healthcare systems are becoming more accessible and advanced, security and privacy have become pivotal for the smooth integration and functioning of various systems in H-CPSs. In this work, we present a novel approach that integrates PUF with IOTA Tangle and blockchain and works by storing the PUF keys of a patient’s Body Area Network (BAN) inside blockchain to access, store, and share globally. Each patient has a network of smart wearables and a gateway to obtain the physiological sensor data securely. To facilitate communication among various stakeholders in healthcare systems, IOTA Tangle’s Masked Authentication Messaging (MAM) communication protocol has been used, which securely enables patients to communicate, share, and store data on Tangle. The MAM channel works in the restricted mode in the proposed architecture, which can be accessed using the patient’s gateway PUF key. Furthermore, the successful verification of PUF enables patients to securely send and share physiological sensor data from various wearable and implantable medical devices embedded with PUF. Finally, healthcare system entities like physicians, hospital admin networks, and remote monitoring systems can securely establish communication with patients using MAM and retrieve the patient’s BAN PUF keys from the blockchain securely. Our experimental analysis shows that the proposed approach successfully integrates three security primitives, PUF, blockchain, and Tangle, providing decentralized access control and security in H-CPS with minimal energy requirements, data storage, and response time.
Compound Fault Diagnosis of Planetary Gearbox Based on Improved LTSS-BoW Model and Capsule Network
Authors: Li Guoyan , He Liyu , Ren Yulin , Li Xiong , Zhang Jingbin , Liu Runjun
Published: January 2024

The identification of compound fault components of a planetary gearbox is especially important for keeping the mechanical equipment working safely. However, the recognition performance of existing deep learning-based methods is limited by insufficient compound fault samples and single label classification principles. To solve the issue, a capsule neural network with an improved feature extractor, named LTSS-BoW-CapsNet, is proposed for the intelligent recognition of compound fault components. Firstly, a feature extractor is constructed to extract fault feature vectors from raw signals, which is based on local temporal self-similarity coupled with bag-of-words models (LTSS-BoW). Then, a multi-label classifier based on a capsule network (CapsNet) is designed, in which the dynamic routing algorithm and average threshold are adopted. The effectiveness of the proposed LTSS-BoW-CapsNet method is validated by processing three compound fault diagnosis tasks. The experimental results demonstrate that our method can via decoupling effectively identify the multi-fault components of different compound fault patterns. The testing accuracy is more than 97%, which is better than the other four traditional classification models.
Gas Turbine Anomaly Detection under Time-Varying Operation Conditions Based on Spectra Alignment and Self-Adaptive Normalization
Authors: Miao Dongyan , Feng Kun , Xiao Yuan , Li Zhouzheng , Gao Jinji
Published: January 2024

Gas turbine vibration data may exhibit considerable differences under time-varying conditions, which poses challenges for neural network anomaly detection. We first propose a framework for a gas turbine vibration frequency spectra process under time-varying operation conditions, assisting neural networks’ ability to capture weak information. The framework involves scaling spectra for aligning all frequency components related to rotational speed and normalizing frequency amplitude in a self-adaptive way. Degressive beta variational autoencoder is employed for learning spectra characteristics and anomaly detection, while a multi-category anomaly index is proposed to accommodate various operating conditions. Finally, a dataset of blade Foreign Object Damage (FOD) fault occurring under time-varying operating conditions was used to validate the framework and anomaly detection. The results demonstrate that the proposed method can effectively reduce the spectra differences under time-varying conditions, and also detect FOD fault during operation, which are challenging to identify using conventional methods.
The Convergence of Intelligent Tutoring, Robotics, and IoT in Smart Education for the Transition from Industry 4.0 to 5.0
Authors: Adel Amr
Published: January 2024

This review paper provides a comprehensive analysis of the automation of smart education in the context of Industry 5.0 from 78 papers, focusing on the integration of advanced technologies and the development of innovative, effective, and ethical educational solutions for the future workforce. As the world transitions into an era characterized by human–machine collaboration and rapidly evolving technologies, there is an urgent need to recognize the pivotal role of smart education in preparing individuals for the opportunities and challenges presented by the new industrial landscape. The paper examines key components of smart education, including intelligent tutoring systems, adaptive learning environments, learning analytics, and the application of the Internet of Things (IoT) in education. It also discusses the role of advanced technologies such as artificial intelligence (AI), machine learning (ML), robotics, and augmented and virtual reality (AR/VR) in shaping personalized and immersive learning experiences. The review highlights the importance of smart education in addressing the growing demand for upskilling and reskilling, fostering a culture of lifelong learning, and promoting adaptability, resilience, and self-improvement among learners. Furthermore, the paper delves into the challenges and ethical considerations associated with the implementation of smart education, addressing issues such as data privacy, the digital divide, teacher and student readiness, and the potential biases in AI-driven systems. Through a presentation of case studies and examples of successful smart education initiatives, the review aims to inspire educators, policymakers, and industry stakeholders to collaborate and innovate in the design and implementation of effective smart education solutions. Conclusively, the paper outlines emerging trends, future directions, and potential research opportunities in the field of smart education, emphasizing the importance of continuous improvement and the integration of new technologies to ensure that education remains relevant and effective in the context of Industry 5.0. By providing a holistic understanding of the key components, challenges, and potential solutions associated with smart education, this review paper seeks to contribute to the ongoing discourse surrounding the automation of smart education and its role in preparing the workforce for the future of work.
Exploring the Influence of Ecological Niches and Hologenome Dynamics on the Growth of Encephalartos villosus in Scarp Forests
Authors: Motsomane Nqobile , Suinyuy Terence N. , Pérez-Fernández María A. , Magadlela Anathi
Published: January 2024

Information on how bacteria in plants and soil, along with extracellular enzymes, affect nutrient cycling in Encephalartos villosus growing in phosphorus deficient and acidic scarp forests is lacking. Bacteria in coralloid roots, rhizosphere, and non-rhizosphere soils were isolated to determine the potential role of soil bacterial communities and their associated enzyme activities in nutrient contributions in rhizosphere and non-rhizosphere soils. The role of soil characteristics and associated bacteria on E. villosus nutrition and nitrogen source reliance was investigated. Encephalartos villosus leaves, coralloid roots, rhizosphere, and non-rhizosphere soils were collected at two scarp forests. Leaf nutrition, nitrogen source reliance, soil nutrition, and extracellular enzyme activities were assayed. A phylogenetic approach was used to determine the evolutionary relationship between identified bacterial nucleotide sequences. The clustering pattern of isolated bacterial strains was primarily dictated by the ecological niches from which they originated (rhizosphere soil, non-rhizosphere soil, and coralloid roots), thus indicating that host-microbe interactions may be a key driver of this pattern, in line with the hologenome theory. There were insignificant differences in the phosphorus and nitrogen cycling enzyme activities in E. villosus rhizosphere and non-rhizosphere soils in both localities. Significantly positive correlations were recorded between nitrogen and phosphorus cycling enzymes and phosphorus and nitrogen concentrations in rhizosphere and non-rhizosphere soils. Additionally, more than 70% of the leaf nitrogen was derived from the atmosphere. This study challenged the conventional expectation that environmental filters alone dictate microbial community composition in similar habitats and revealed that host-microbe interactions, as proposed by the hologenome theory, are significant drivers of microbial community structuring. The isolated bacteria and their plant growth promoting traits play a role in E. villosus nutrition and nitrogen source reliance and secrete nutrient cycling enzymes that promote nutrient availability in rhizosphere and non-rhizosphere soils.
Sustainability Evaluation of Hybrid Agriculture-Tractor Powertrains
Authors: Pascuzzi Simone , Łyp-Wrońska Katarzyna , Gdowska Katarzyna , Paciolla Francesco
Published: January 2024

Agricultural tractors are highly fuel-consuming and soil/air polluting machines; thus, the introduction of new sustainable technologies, such as hybridization, can be very impactful for the development of electric hybrid agricultural tractors. These vehicles combine the classic internal combustion engine with an electric machine. This paper reports the modeling and simulation, conducted using a simulation software typically used for on-road vehicles, of a two-wheel-drive agricultural tractor in three different configurations: the conventional one, and the series and parallel electric-hybrid powertrains. The simulated task is the trailing of a “big square baler” during the process of straw wrapping and baling. The evaluation and the comparison of the fuel consumption, CO2 emissions and the depth of discharge of the different configurations have been carried out to determine if it is possible to downsize the ICE while maintaining the same performance levels. This study highlights the fact that both the fuel consumption and the CO2 emissions of series and parallel electric-hybrid agricultural tractors are ten times lower and five times lower than those of a traditional tractor, respectively. Furthermore, the presence of an electric machine allows a more precise speed profile tracking. This study points out that the hybridization of agricultural tractor powertrains is one of the most promising approaches for reducing pollutant emissions and fuel consumption.