bluuebunny/crossref_metadata_2025 huggingface dataset:
The Application of Nano Drug Delivery Systems in Female Upper Genital Tract Disorders
Authors: van Staden Daniélle , Gerber Minja , Lemmer Hendrik J. R.
Published: November 2024

The prevalence of female reproductive system disorders is increasing, especially among women of reproductive age, significantly impacting their quality of life and overall health. Managing these diseases effectively is challenging due to the complex nature of the female reproductive system, characterized by dynamic physiological environments and intricate anatomical structures. Innovative drug delivery approaches are necessary to facilitate the precise regulation and manipulation of biological tissues. Nanotechnology is increasingly considered to manage reproductive system disorders, for example, nanomaterial imaging allows for early detection and enhances diagnostic precision to determine disease severity and progression. Additionally, nano drug delivery systems are gaining attention for their ability to target the reproductive system successfully, thereby increasing therapeutic efficacy and decreasing side effects. This comprehensive review outlines the anatomy of the female upper genital tract by highlighting the complex mucosal barriers and their impact on systemic and local drug delivery. Advances in nano drug delivery are described for their sustainable therapeutic action and increased biocompatibility to highlight the potential of nano drug delivery strategies in managing female upper genital tract disorders.
Silibinin-Loaded Liposomes: The Influence of Modifications on Physicochemical Characteristics, Stability, and Bioactivity Associated with Dermal Application
Authors: Karkad Amjed Abdullah , Pirković Andrea , Milošević Milena , Stojadinović Bojan , Šavikin Katarina , Marinković Aleksandar , Jovanović Aleksandra A.
Published: November 2024

Background/Objectives: The aims of the presented study were the development of four types of silibinin-loaded liposomes (multilamellar liposomes—MLVs, sonicated small unilamellar liposomes—SUVs, UV-irradiated liposomes, and lyophilized liposomes) and their physicochemical characterization and biological potential related to skin health benefits. Methods: The characterization was performed via the determination of the encapsulation efficiency (EE), particle size, polydispersity index, zeta potential, conductivity, mobility, storage stability, density, surface tension, viscosity, FT-IR, and Raman spectra. In addition, cytotoxicity on the keratinocytes and antioxidant and anti-inflammatory potential were also determined. Results: UV irradiation significantly changed the rheological and chemical properties of the liposomes and increased their cytotoxic effect. The lyophilization of the liposomes caused significant changes in their EE and physical characteristics, decreased their ABTS and DPPH radical scavenging potential, and increased their potential to reduce the expression of interleukin 1 beta (IL-1β) in cells treated with bacterial lipopolysaccharide. Sonication significantly changed the EE and physical and rheological properties of the liposomes, and slightly increased their cytotoxicity and reduction effect on IL-1β, while the anti-ABTS and anti-DPPH capacity of the liposomes significantly increased. All developed liposomes showed an increasing trend in particle size and a decreasing trend in zeta potential (absolute values) during storage. Conclusions: Silibinin-loaded liposomes (MLVs and lyophilized) showed promising antioxidant activity (toward reactive oxygen species generated in cells) and anti-inflammatory effects (reducing macrophage inhibitory factor expression) on keratinocytes and did not lead to a change in their viability. Future perspectives will focus on wound healing, anti-aging, and other potential of developed liposomes with silibinin in sophisticated cell-based models of skin diseases, wounds, and aging.
Quantitative Investigation of Layer-by-Layer Deposition and Dissolution Kinetics by New Label-Free Analytics Based on Low-Q-Whispering Gallery Modes
Authors: Olszyna Mateusz , Domac Algi , Zimmer Jasmine , Dähne Lars
Published: November 2024

A new instrument for label-free measurements based on optical Low-Q Whispering Gallery Modes (WGMs) for various applications is used for a detailed study of the deposition and release of Layer-by-Layer polymer coatings. The two selected coating pairs interact either via hydrogen bonding or electrostatic interactions. Their assembly was followed by common Quartz Crystal Microbalance (QCM) technology and the Low-Q WGMs. In contrast to planar QCM sensor chips of 1 cm, the WGM sensors are fluorescent spherical beads with diameters of 10.2 µm, enabling the detection of analyte quantities in the femtogram range in tiny volumes. The beads, with a very smooth surface and high refractive index, act as resonators for circular light waves that can revolve up to 10,000 times within the bead. The WGM frequencies are highly sensitive to changes in particle diameter and the refractive index of the surrounding medium. Hence, the adsorption of molecules shifts the resonance frequency, which is detected by a robust instrument with a high-resolution spectrometer. The results demonstrate the high potential of the new photonic measurement and its advantages over QCM technology, such as cheap sensors (billions in one Eppendorf tube), simple pre-functionalization, much higher statistic safety by hundreds of sensors for one measurement, 5–10 times faster analysis, and that approx. 25, 000 fewer analyte molecules are needed for one sensor. In addition, the deposited molecule amount is not superposed by hydrated water as for QCM. A connection between sensors and instruments does not exist, enabling application in any transparent environment, like microfluidics, drop-on slides, Petri dishes, well plates, cell culture vasculature, etc.
Induction and Transcriptome Analysis of Callus Tissue from Endosperm of Makapuno Coconut
Authors: Huang Jing , Liu Zijia , Guo Qinghui , Zou Jixin , Zheng Yusheng , Li Dongdong
Published: November 2024

The makapuno coconut endosperm is distinguished by its soft and irregular texture, in contrast to the solid endosperm of regular coconuts. To establish a scientific foundation for studying makapuno coconuts, callus was induced from makapuno endosperm using a combination of plant growth regulators. The induction was successful, and the resulting callus was subsequently subcultured for further study. Transcriptome sequencing of the makapuno callus identified 429 differentially expressed genes (DEGs), with 273 upregulated and 156 downregulated, compared to callus derived from regular coconut endosperm. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis indicated that these DEGs were involved in key metabolic pathways, including fructose and mannose metabolism, carbon fixation in photosynthetic organisms, galactose metabolism, and amino sugar and nucleotide sugar metabolism. Furthermore, lipid content analysis of the makapuno callus revealed a significantly higher total lipid level compared to regular callus, with notable differences in the levels of specific fatty acids, such as myristic acid, palmitic acid, and linoleic acid. This study establishes a novel platform for molecular biological research on makapuno coconuts and provides valuable insights into the molecular mechanisms underlying the formation of makapuno callus tissue. The findings also lay the groundwork for future research aimed at elucidating the unique properties of makapuno endosperm and exploring its potential applications.
Extraction of Lignocellulose from Rice Straw and Its Carboxymethylation When Activated by Microwave Radiation
Authors: Nakyp Abdirakym , Cherezova Elena , Karaseva Yuliya , Shalmagambetov Kaiyrzhan , Aleksandrov Aleksandr , Zhapparbergenov Rakhmetulla , Akylbekov Nurgali , Turmanov Rakhymzhan
Published: November 2024

The paper presents the process of cellulose extraction from rice straw using water–alkaline solution treatment and the subsequent process of carboxymethylation of the obtained product when activated by microwave radiation. After mercerization of rice straw, the obtained product contained 89.2% cellulose and 6.7% lignin. The X-ray diffraction pattern of the obtained lignocellulose shows three diffraction peaks in the region typical for the polymorphic modification of cellulose Iβ (2θ = 15.50(78), 21.70(145), 34.70(52)). The degree of crystallinity was 65%. The product was heat-stable up to 247 °C. The synthesis of carboxymethylcellulose (CMC) based on the obtained product included successive processes of thermostating in alcohol–alkali solution and cellulose esterification reaction using monochloroacetic acid. To activate the carboxymethylation process, microwave radiation was used (350 W for 90 s), which made it possible to reduce the reaction time by more than 100 times. Functional group analysis of the carboxylated lignocellulose from rice straw was carried out using an FTIR spectrometer. In the IR spectra, a band with a maximum of 1742 cm−1 was recorded, corresponding to stretching vibrations of >C(O)OH groups. The degree of polymerization was recorded by mass spectrometry.
Development of Sustainable Cement Asphalt Mortar Using Agricultural Waste-Derived Bio-Oil and Latex–Acrylic Polymers for Enhanced Durability
Authors: Kim Yeong-Min , Kim Kyungnam , Le Tri Ho Minh
Published: November 2024

Cement Asphalt Mortar (CAM) is widely applied in infrastructure, particularly in railways, bridge expansion joints, and pavements, due to its combination of cement’s load-bearing capacity and asphalt’s flexibility. Conventional CAM formulations, however, often encounter challenges such as extended setting times, high shrinkage, and limited durability under extreme environmental conditions. This study addresses these limitations by integrating bio-oil and polymer additives to enhance both the sustainability and performance of CAM mixtures. CAM mixtures were evaluated with cement-to-asphalt emulsion (C/AE) mass ratios of 75:25 and 50:50, incorporating bio-oil contents of 2% and 4% by mass and latex–acrylic polymer proportions ranging from 1% to 2% by mass. The optimized mix design, with a 75:25 cement-to-asphalt emulsion (C/AE) mass ratio, 2% bio-oil, and 1.5% polymer, improved flowability by 25%. This formulation achieved a flow diameter of approximately 205 mm and reduced the flow time to 72 s. Compressive strength tests indicated that this formulation reached an early-stage strength of 10.45 MPa (a 20.8% improvement over the control) and a 28-day strength of 24.18 MPa. Thermal stability tests at 45 °C demonstrated that the optimized CAM retained 86.6% of its compressive strength, compared to a 25% reduction in unmodified mixtures. Chemical resistance assessments in 5% sulfuric acid and 5% sodium hydroxide solutions showed strength retention of 95.03% and 91.98%, respectively, outperforming control mixtures by 17% and 13%. SEM examination revealed a dense, cohesive microstructure, reducing shrinkage to 0.01% from 0.15% in the control. These findings underscore the potential of bio-oil and latex–acrylic polymers to improve the performance and sustainability of CAM mixtures, making them well suited for resilient, rapid-setting infrastructure applications.
Thermo-Mechanically Coupled Settlement and Temperature Response of a Composite Foundation in Complex Geological Conditions for Molten Salt Tank in Tower Solar Plants
Authors: Zhu Shezhou , Li Xu , Li Yi , Sun Hemei , Kang Xin
Published: November 2024

The degradation of complex geological structures due to thermo-mechanical cycling results in a reduction in bearing capacity, which can readily induce engineering issues such as uneven settlement, cracking, and even the destabilization of the foundations of molten salt storage tanks. This study establishes a foundational model for a molten salt storage tank through the use of COMSOL Multiphysics and conducts a numerical simulation analysis to evaluate the settlement deformation and temperature distribution of the foundation under the influence of thermo-mechanical coupling. Concurrently, the research proposes two distinct design approaches for the tank’s foundational structure. A comparative analysis of the results indicates that the use of a pile raft foundation in conjunction with a traditional foundation mode results in a reduction of settlement at the center of the foundation’s top surface by 380.1 mm, while also decreasing the maximum effective stress in the steel ring wall by 240.7 MPa. The thermal effects impact a depth of 10 m in the foundation soil and an influence radius of 20 m. Additionally, the foundation soil exhibits optimal thermal insulation properties, resulting in minimal energy loss. These findings indicate that the pile raft foundation in conjunction with a traditional foundation mode displays remarkable adaptability to complex geological conditions, with both settlement and temperature distribution of the foundation maintained within acceptable limits.
Design and Experimental Evaluation of Multiple 3D-Printed Reduction Gearboxes for Wearable Exoskeletons
Authors: Bezzini Riccardo , Bassani Giulia , Avizzano Carlo Alberto , Filippeschi Alessandro
Published: November 2024

The recent advancements in wearable exoskeletons have highlighted their effectiveness in assisting humans for both rehabilitation and augmentation purposes. These devices interact with the user; therefore, their actuators and power transmission mechanisms are crucial for enhancing physical human–robot interaction (pHRI). The advanced progression of 3D printing technology as a valuable method for creating lightweight and efficient gearboxes enables the exploration of multiple reducer designs. However, to the authors’ knowledge, only sporadic implementations with relatively low reduction ratios have been reported, and the respective experimental validations usually vary, preventing a comprehensive evaluation of different design and implementation choices. In this paper, we design, develop, and examine experimentally multiple 3D-printed gearboxes conceived for wearable assistive devices. Two relevant transmission ratios (1:30 and 1:80) and multiple designs, which include single- and double-stage compact cam cycloidal drives, compound planetary gearboxes, and cycloidal and planetary architectures, are compared to assess the worth of 3D-printed reducers in human–robot interaction applications. The resulting prototypes were examined by evaluating their weight, cost, backdrivability, friction, regularity of the reduction ratio, gear play, and stiffness. The results show that the developed gearboxes represent valuable alternatives for actuating wearable exoskeletons in multiple applications.
High-Resolution Remotely Sensed Evidence Shows Solar Thermal Power Plant Increases Grassland Growth on the Tibetan Plateau
Authors: Liu Naijing , Peng Huaiwu , Zhang Zhenshi , Li Yujin , Zhang Kai , Guo Yuehan , Cui Yuzheng , Jiang Yingsha , Gao Wenxiang , Wu Donghai
Published: November 2024

Solar energy plays a crucial role in mitigating greenhouse gas emissions in the context of global climate change. However, its deployment for green electricity generation can significantly influence regional climate and vegetation dynamics. While prior studies have examined the impacts of solar power plants on vegetation, the accuracy of these assessments has often been constrained by the availability of publicly accessible multispectral, high-resolution remotely sensed imagery. Given the abundant solar energy resources and the ecological significance of the Tibetan Plateau, a thorough evaluation of the vegetation effects associated with solar power installations is warranted. In this study, we utilize sub-meter resolution imagery from the GF-2 satellite to reconstruct the fractional vegetation cover (FVC) at the Gonghe solar thermal power plant through image classification, in situ sampling, and sliding window techniques. We then quantify the plant’s impact on FVC by comparing data from the pre-installation and post-installation periods. Our findings indicate that the Gonghe solar thermal power plant is associated with a 0.02 increase in FVC compared to a surrounding control region (p < 0.05), representing a 12.5% increase relative to the pre-installation period. Notably, the enhancement in FVC is more pronounced in the outer ring areas than near the central tower. The observed enhancement in vegetation growth at the Gonghe plant suggests potential ecological and carbon storage benefits resulting from solar power plant establishment on the Tibetan Plateau. These findings underscore the necessity of evaluating the climate and ecological impacts of renewable energy facilities during the planning and design phases to ensure a harmonious balance between clean energy development and local ecological integrity.
MAPM:PolSAR Image Classification with Masked Autoencoder Based on Position Prediction and Memory Tokens
Authors: Wang Jianlong , Li Yingying , Quan Dou , Hou Beibei , Wang Zhensong , Sima Haifeng , Sun Junding
Published: November 2024

Deep learning methods have shown significant advantages in polarimetric synthetic aperture radar (PolSAR) image classification. However, their performances rely on a large number of labeled data. To alleviate this problem, this paper proposes a PolSAR image classification method with a Masked Autoencoder based on Position prediction and Memory tokens (MAPM). First, MAPM designs a Masked Autoencoder (MAE) based on the transformer for pre-training, which can boost feature learning and improve classification results based on the number of labeled samples. Secondly, since the transformer is relatively insensitive to the order of the input tokens, a position prediction strategy is introduced in the encoder part of the MAE. It can effectively capture subtle differences and discriminate complex, blurry boundaries in PolSAR images. In the fine-tuning stage, the addition of learnable memory tokens can improve classification performance. In addition, L1 loss is used for MAE optimization to enhance the robustness of the model to outliers in PolSAR data. Experimental results show the effectiveness and advantages of the proposed MAPM in PolSAR image classification. Specifically, MAPM achieves performance gains of about 1% in classification accuracy compared with existing methods.
A GIS Plugin for the Assessment of Deformations in Existing Bridge Portfolios via MTInSAR Data
Authors: Calò Mirko , Ruggieri Sergio , Nettis Andrea , Uva Giuseppina
Published: November 2024

The paper presents a GIS plugin, named Bridge Assessment System via MTInSAR (BAS-MTInSAR), aimed at assessing deformations in existing simply supported concrete girder bridges through Multi-Temporal Interferometry Synthetic Aperture Radar (MTInSAR). Existing bridges require continuous maintenance to ensure functionality toward external effects undermining the safety of these structures, such as aging, material degradation, and environmental factors. Although effective and standardized methodologies exist (e.g., structural monitoring, periodic onsite inspections), new emerging technologies could be employed to provide time- and cost-effective information on the current state of structures and to drive prompt interventions to mitigate risk. One example is represented by MTInSAR data, which can provide near-continuous information about structural displacements over time. To easily manage these data, the paper presents BAS-MTInSAR. The tool allows users to insert information of the focused bridge (displacement time series, structural information, temperature data) and, through a user-friendly GUI, observe the occurrence of abnormal deformations. In addition, the tool implements a procedure of multisource data management and defines proper thresholds to assess bridge behavior against current code prescriptions. BAS-MTInSAR is fully described throughout the text and was tested on a real case study, showing the main potentialities of the tool in managing bridge portfolios.
Root-Zone Salinity in Irrigated Arid Farmland: Revealing Driving Mechanisms of Dynamic Changes in China’s Manas River Basin over 20 Years
Authors: Yang Guang , Qiao Xuejin , Zuo Qiang , Shi Jianchu , Wu Xun , Ben-Gal Alon
Published: November 2024

The risk of soil salinization is prevalent in arid and semi-arid regions, posing a critical challenge to sustainable agriculture. This study addresses the need for accurate assessment of regional root-zone soil salt content (SSC) and understanding of underlying driving mechanisms, which are essential for developing effective salinization mitigation and water management strategies. A remote sensing inversion technique, initially proposed to estimate root-zone SSC in cotton fields, was adapted and validated more widely to non-cotton farmlands. Validation results (with a coefficient of determination R2 > 0.53) were obtained using data from a three-year (2020–2022) regional survey conducted in the arid Manas River Basin (MRB), Xinjiang, China. Based on this adapted technique, we analyzed the spatiotemporal distributions of root-zone SSC across all farmlands in MRB from 2001 to 2022. Findings showed that root-zone SSC decreased significantly from 5.47 to 3.77 g kg−1 over the past 20 years but experienced a slight increase of 0.15 g kg−1 in recent five years (2017–2022), attributed to cultivated area expansion and reduced irrigation quotas due to local water shortages. The driving mechanisms behind root-zone SSC distributions were analyzed using an approach combined with two machine learning algorithms, eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanation (SHAP), to identify influential factors and quantify their impacts. The approach demonstrated high predictive accuracy (R2 = 0.96 ± 0.01, root mean squared error RMSE = 0.19 ± 0.03 g kg−1, maximum absolute error MAE = 0.14 ± 0.02 g kg−1) in evaluating SSC drivers. Factors such as initial SSC, crop type distribution, duration of film mulched drip irrigation implementation, normalized difference vegetation index (NDVI), irrigation amount, and actual evapotranspiration (ETa), with mean (SHAP value) ≥ 0.02 g kg−1, were found to be more closely correlated with root-zone SSC variations than other factors. Decreased irrigation amount appeared as the primary driver for recent increased root-zone SSC, especially in the mid- and down-stream sections of MRB. Recommendations for secondary soil salinization risk reduction include regulation of the planting structure (crop choice and extent of planting area) and maintenance of a sufficient irrigation amount.
An Image Fusion Algorithm for Sustainable Development Goals Satellite-1 Night-Time Light Images Based on Optimized Image Stretching and Dual-Domain Fusion
Authors: Li Kedong , Cheng Bo , Li Xiaoming , Zhang Xiaoping , Wang Guizhou , Gao Jie , He Qinxue , Gan Yaocan
Published: November 2024

The Glimmer Imager of Urbanization (GIU) on SDGSAT-1 provides high-resolution and global-coverage images of night-time lights (NLs) with 10 m panchromatic (PAN) and 40 m multispectral (MS) imagery. High-resolution 10 m MS NL images after ideal fusion can be used to better study subtle manifestations of human activities. Most existing remote sensing image-fusion methods are based on the fusion of daytime optical remote sensing images, which do not apply to lossless compressed images of the GIU. To address this limitation, we propose a novel approach for 10 m NL data fusion, namely, a GIU NL image fusion model based on PAN-optimized OIS (OIS) and DDF (DDF) fusion for SDGSAT-1 high-resolution products. The OIS of PAN refers to the optimized stretching method that integrates linear and gamma stretching while DDF indicates a fusion process that separately merges the dark and light regions of NL images using different fusion methods, then stitches them together. In this study, fusion experiments were conducted in four study areas—Beijing, Shanghai, Moscow, and New York—and the proposed method was compared to traditional methods using visual evaluation and five quantitative evaluation metrics. The results demonstrate that the proposed method achieves superior visual quality and outperforms conventional methods across all quantitative metrics. Additionally, the ablation study confirmed the necessity of the methodological steps employed in this study.
Lightweight Neural Network for Centroid Detection of Weak, Small Infrared Targets via Background Matching in Complex Scenes
Authors: Xu Xiangdong , Wang Jiarong , Sha Zhichao , Nie Haitao , Zhu Ming , Nie Yu
Published: November 2024

In applications such as aerial object interception and ballistic estimation, it is crucial to precisely detect the centroid position of the target rather than to merely identify the position of the target bounding box or segment all pixels belonging to the target. Due to the typically long distances between targets and imaging devices in such scenarios, targets often exhibit a low contrast and appear as dim, obscure shapes in infrared images, which represents a challenge for human observation. To rapidly and accurately detect small targets, this paper proposes a lightweight, end-to-end detection network for small infrared targets. Unlike existing methods, the input of this network is five consecutive images after background matching. This design significantly improves the network’s ability to extract target motion features and effectively reduces the interference of static backgrounds. The network mainly consists of a local feature aggregation module (LFAM), which uses multiple-sized convolution kernels to capture multi-scale features in parallel and integrates multiple spatial attention mechanisms to achieve accurate feature fusion and effective background suppression, thereby enhancing the ability to detect small targets. To improve the accuracy of predicted target centroids, a centroid correction algorithm is designed. In summary, this paper presents a lightweight centroid detection network based on background matching for weak, small infrared targets. The experimental results show that, compared to directly inputting a sequence of images into the neural network, inputting a sequence of images processed by background matching can increase the detection rate by 9.88%. Using the centroid correction algorithm proposed in this paper can therefore improve the centroid localization accuracy by 0.0134.
Enhanced Fishing Monitoring in the Central-Eastern North Pacific Using Deep Learning with Nightly Remote Sensing
Authors: Li Jiajun , Li Jinyou , Zhang Kui , Li Xi , Chen Zuozhi
Published: November 2024

The timely and accurate monitoring of high-seas fisheries is essential for effective management. However, efforts to monitor industry fishing vessels in the central-eastern North Pacific have been hampered by frequent cloud cover and solar illumination interference. In this study, enhanced fishing extraction algorithms based on computer vision were developed and tested. The results showed that YOLO-based computer vision models effectively detected dense small fishing targets, with original YOLOv8 achieving a precision (P) of 89% and a recall (R) of 79%, while refined versions improved these metrics to 93% and 99%, respectively. Compared with traditional threshold methods, the YOLO-based enhanced models showed significantly higher accuracy. While the threshold method could identify similar trend changes, it lacked precision in detecting individual targets, especially in blurry scenarios. Using our trained computer vision model, we established a dataset of dynamic changes in fishing vessels over the past decade. This research provides an accurate and reproducible process for precise monitoring of lit fisheries in the North Pacific, leveraging the operational and near-real-time capabilities of Google Earth Engine and computer vision. The approach can also be applied to dynamic monitoring of industrial lit fishing vessels in other regions.
Joint Classification of Hyperspectral and LiDAR Data via Multiprobability Decision Fusion Method
Authors: Chen Tao , Chen Sizuo , Chen Luying , Chen Huayue , Zheng Bochuan , Deng Wu
Published: November 2024

With the development of sensor technology, the sources of remotely sensed image data for the same region are becoming increasingly diverse. Unlike single-source remote sensing image data, multisource remote sensing image data can provide complementary information for the same feature, promoting its recognition. The effective utilization of remote sensing image data from various sources can enhance the extraction of image features and improve the accuracy of feature recognition. Hyperspectral remote sensing (HSI) data and light detection and ranging (LiDAR) data can provide complementary information from different perspectives and are frequently combined in feature identification tasks. However, the process of joint use suffers from data redundancy, low classification accuracy and high time complexity. To address the aforementioned issues and improve feature recognition in classification tasks, this paper introduces a multiprobability decision fusion (PRDRMF) method for the combined classification of HSI and LiDAR data. First, the original HSI data and LiDAR data are downscaled via the principal component–relative total variation (PRTV) method to remove redundant information. In the multifeature extraction module, the local texture features and spatial features of the image are extracted to consider the local texture and spatial structure of the image data. This is achieved by utilizing the local binary pattern (LBP) and extended multiattribute profile (EMAP) for the two types of data after dimensionality reduction. The four extracted features are subsequently input into the corresponding kernel–extreme learning machine (KELM), which has a simple structure and good classification performance, to obtain four classification probability matrices (CPMs). Finally, the four CPMs are fused via a multiprobability decision fusion method to obtain the optimal classification results. Comparison experiments on four classical HSI and LiDAR datasets demonstrate that the method proposed in this paper achieves high classification performance while reducing the overall time complexity of the method.
A New Paradigm in AC Drive Control: Data-Driven Control by Learning Through the High-Efficiency Data Set—Generalizations and Applications to a PMSM Drive Control System
Authors: Costin Madalin , Bivol Ion
Published: November 2024

This paper presents a new means to control the processes involving energy conversion. Electric machines fed by electronic converters provide a useful power defined by the inner product of two generalized energetic variables: effort and flow. The novelty in this paper is controlling the desired energetic variables by a Data-Driven Control (DDC) law, which comprises the effort and flow and the corresponding process control. The same desired useful power might be obtained with different controls at different efficiencies. Solving the regularization problem is based on building a knowledge database that contains the maximum efficiency points. Knowing a reasonable number of optimal efficiency operation points, an interpolation Radial Base Function (RBF) control was built. The RBF algorithm can be found by training and testing the optimal controls for any admissible operation points of the process. The control scheme developed for Permanent Magnet Synchronous Motor (PMSM) has an inner DDC loop that performs converter control based on measured speed and demanded torque by the outer loop, which handles the speed. A comparison of the DDC with the Model Predictive Control (MPC) of the PMSM highlights the advantages of the new control method: the method is free from the process nature and guarantees higher efficiency.
Multimodal Trajectory Prediction for Diverse Vehicle Types in Autonomous Driving with Heterogeneous Data and Physical Constraints
Authors: Ge Maoning , Ohtani Kento , Ding Ming , Niu Yingjie , Zhang Yuxiao , Takeda Kazuya
Published: November 2024

The accurate prediction of vehicle behavior is crucial for autonomous driving systems, impacting their safety and efficiency in complex urban environments. To address the challenge of multi-agent trajectory prediction, we propose a novel model integrating multiple input modalities, including historical trajectories, map data, vehicle features, and interaction information. Our approach employs a Conditional Variational Autoencoder (CVAE) framework with a decoder that predicts control actions using the Gaussian Mixture Model (GMM) and then converts these actions into dynamically feasible trajectories through a bicycle model. Evaluated on the nuScenes dataset, the model achieves great performance across key metrics, including minADE5 of 1.26 and minFDE5 of 2.85, demonstrating robust performance across various vehicle types and prediction horizons. These results indicate that integrating multiple data sources, physical models, and probabilistic methods significantly improves trajectory prediction accuracy and reliability for autonomous driving. Our approach generates diverse yet realistic predictions, capturing the multimodal nature of future outcomes while adhering to Physical Constraints and vehicle dynamics.
A Novel Approach for Kalman Filter Tuning for Direct and Indirect Inertial Navigation System/Global Navigation Satellite System Integration
Authors: Tavares Jr. Adalberto J. A. , Oliveira Neusa M. F.
Published: November 2024

This work presents an innovative approach for tuning the Kalman filter in INS/GNSS integration, combining states from the inertial navigation system (INS) and data from the Global Navigation Satellite System (GNSS) to enhance navigation accuracy. The INS uses measurements from accelerometers and gyroscopes, which are subject to uncertainties in scale factor, misalignment, non-orthogonality, and bias, as well as temporal, thermal, and vibration variations. The GNSS receiver faces challenges such as multipath, temporary signal loss, and susceptibility to high-frequency noise. The novel approach for Kalman filter tuning involves previously performing Monte Carlo simulations using ideal data from a predetermined trajectory, applying the inertial sensor error model. For the indirect filter, errors from inertial sensors are used, while, for the direct filter, navigation errors in position, velocity, and attitude are also considered to obtain the process noise covariance matrix Q. This methodology is tested and validated with real data from Castro Leite Consultoria’s commercial platforms, PINA-F and PINA-M. The results demonstrate the efficiency and consistency of the estimation technique, highlighting its applicability in real scenarios.
A Comparison Between Single-Stage and Two-Stage 3D Tracking Algorithms for Greenhouse Robotics
Authors: Rapado-Rincon David , Burusa Akshay K. , van Henten Eldert J. , Kootstra Gert
Published: November 2024

With the current demand for automation in the agro-food industry, accurately detecting and localizing relevant objects in 3D is essential for successful robotic operations. However, this is a challenge due the presence of occlusions. Multi-view perception approaches allow robots to overcome occlusions, but a tracking component is needed to associate the objects detected by the robot over multiple viewpoints. Multi-object tracking (MOT) algorithms can be categorized between two-stage and single-stage methods. Two-stage methods tend to be simpler to adapt and implement to custom applications, while single-stage methods present a more complex end-to-end tracking method that can yield better results in occluded situations at the cost of more training data. The potential advantages of single-stage methods over two-stage methods depend on the complexity of the sequence of viewpoints that a robot needs to process. In this work, we compare a 3D two-stage MOT algorithm, 3D-SORT, against a 3D single-stage MOT algorithm, MOT-DETR, in three different types of sequences with varying levels of complexity. The sequences represent simpler and more complex motions that a robot arm can perform in a tomato greenhouse. Our experiments in a tomato greenhouse show that the single-stage algorithm consistently yields better tracking accuracy, especially in the more challenging sequences where objects are fully occluded or non-visible during several viewpoints.
A Feature Integration Network for Multi-Channel Speech Enhancement
Authors: Zeng Xiao , Zhang Xue , Wang Mingjiang
Published: November 2024

Multi-channel speech enhancement has become an active area of research, demonstrating excellent performance in recovering desired speech signals from noisy environments. Recent approaches have increasingly focused on leveraging spectral information from multi-channel inputs, yielding promising results. In this study, we propose a novel feature integration network that not only captures spectral information but also refines it through shifted-window-based self-attention, enhancing the quality and precision of the feature extraction. Our network consists of blocks containing a full- and sub-band LSTM module for capturing spectral information, and a global–local attention fusion module for refining this information. The full- and sub-band LSTM module integrates both full-band and sub-band information through two LSTM layers, while the global–local attention fusion module learns global and local attention in a dual-branch architecture. To further enhance the feature integration, we fuse the outputs of these branches using a spatial attention module. The model is trained to predict the complex ratio mask (CRM), thereby improving the quality of the enhanced signal. We conducted an ablation study to assess the contribution of each module, with each showing a significant impact on performance. Additionally, our model was trained on the SPA-DNS dataset using a circular microphone array and the Libri-wham dataset with a linear microphone array, achieving competitive results compared to state-of-the-art models.
Visual Localization Based on Torus-like Surfaces
Authors: Liu Xuandong , Luo Lihong , Shen Bingren
Published: November 2024

Previous visual localization started from point correspondences (PCs) to estimate poses. This article takes the camera position as the entry point and finds that the camera position solution set rotates around an axis connected by two observed 3D points to form a surface called a torus-like surface (TLS). The relevant parameters of TLS are calculated based on PCs and camera intrinsic parameters. In order to reduce the number of solutions, this article uses four PCs to construct three TLSs. By utilizing four PCs, the pose determination problem is reformulated as the task of finding the optimal intersection of three surfaces. By using the step-size adaptive tracking method, the candidate set of intersections can be quickly and accurately found. Combining the feature information of intersections on TLS and the camera intrinsic parameters, the optimal position is obtained. Based on this position, the rotation matrix can be determined. In the synthetic data experiments and the dataset experiments based on image localization, it is shown that the visual localization based on TLS is more accurate than current state-of-the-art methods, which provides a new entry angle and effective ideas for visual localization. Its accuracy and practicality are fully demonstrated in the application test of augmented reality indoor navigation.
Research on Visual–Inertial Measurement Unit Fusion Simultaneous Localization and Mapping Algorithm for Complex Terrain in Open-Pit Mines
Authors: Xiao Yuanbin , Xu Wubin , Li Bing , Zhang Hanwen , Xu Bo , Zhou Weixin
Published: November 2024

As mining technology advances, intelligent robots in open-pit mining require precise localization and digital maps. Nonetheless, significant pitch variations, uneven highways, and rocky surfaces with minimal texture present substantial challenges to the precision of feature extraction and positioning in traditional visual SLAM systems, owing to the intricate terrain features of open-pit mines. This study proposes an improved SLAM technique that integrates visual and Inertial Measurement Unit (IMU) data to address these challenges. The method incorporates a point–line feature fusion matching strategy to enhance the quality and stability of line feature extraction. It integrates an enhanced Line Segment Detection (LSD) algorithm with short segment culling and approximate line merging techniques. The combination of IMU pre-integration and visual feature restrictions is executed inside a tightly coupled visual–inertial framework utilizing a sliding window approach for back-end optimization, enhancing system robustness and precision. Experimental results demonstrate that the suggested method improves RMSE accuracy by 36.62% and 26.88% on the MH and VR sequences of the EuRoC dataset, respectively, compared to ORB-SLAM3. The improved SLAM system significantly reduces trajectory drift in the simulated open-pit mining tests, improving localization accuracy by 40.62% and 61.32%. The results indicate that the proposed method demonstrates significance.
The Application of Augmented Reality Technology in Perioperative Visual Guidance: Technological Advances and Innovation Challenges
Authors: Shen Yichun , Wang Shuyi , Shen Yuhan , Hu Jingyi
Published: November 2024

In contemporary medical practice, perioperative visual guidance technology has become a critical element in enhancing the precision and safety of surgical procedures. This study provides a comprehensive review of the advancements in the application of Augmented Reality (AR) technology for perioperative visual guidance. This review begins with a retrospective look at the evolution of AR technology, including its initial applications in neurosurgery. It then delves into the technical challenges that AR faces in areas such as image processing, 3D reconstruction, spatial localization, and registration, underscoring the importance of improving the accuracy of AR systems and ensuring their stability and consistency in clinical use. Finally, the review looks forward to how AR technology could be further facilitated in medical applications with the integration of cutting-edge technologies like skin electronic devices and how the incorporation of machine learning could significantly enhance the accuracy of AR visual systems. As technology continues to advance, there is ample reason to believe that AR will be seamlessly integrated into medical practice, ushering the healthcare field into a new “Golden Age”.
Detection of Low-Density Foreign Objects in Infant Snacks Using a Continuous-Wave Sub-Terahertz Imaging System for Industrial Applications
Authors: Na Byeong-Hyeon , Lee Dae-Ho , Choe Jaein , Kim Young-Duk , Park Mi-Kyung
Published: November 2024

Low-density foreign objects (LDFOs) in foods pose significant safety risks to consumers. Existing detection methods, such as metal and X-ray detectors, have limitations in identifying low-density and nonmetallic contaminants. To address these challenges, our research group constructed and optimized a continuous-wave sub-terahertz (THz) imaging system for the real-time, on-site detection of LDFOs in infant snacks. The system was optimized by adjusting the attenuation value from 0 to 9 dB and image processing parameters [White (W), Black (B), and Gamma (G)] from 0 to 100. Its detectability was evaluated across eight LDFOs underneath snacks with scanning at 30 cm/s. The optimal settings for puffed snacks and freeze-dried chips were found to be 3 dB attenuation with W, B, and G values of 100, 50, and 80, respectively, while others required 0 dB attenuation with W, B, and G set to 100, 0, and 100, respectively. Additionally, the moisture content of infant snacks was measured using a modified AOAC-based drying method at 105 °C, ensuring the removal of all free moisture. Using these optimized settings, the system successfully detected a housefly and a cockroach underneath puffed snacks and freeze-dried chips. It also detected LDFOs as small as 3 mm in size in a single layer of snacks, including polyurethane, polyvinyl chloride, ethylene–propylene–diene–monomer, and silicone, while in two layers of infant snacks, they were detected up to 7.5 mm. The constructed system can rapidly and effectively detect LDFOs in foods, offering a promising approach to enhance safety in the food industry.
Addressing the Shortage of Special Education Teachers of Color: Implications for Teacher Education Programs and K-12 Systems
Authors: Myrie Doreen N. , Dowd Nicci T. , Latiker Michelle D.
Published: November 2024

A critical shortage of special education teachers persists nationwide, challenging the realization of the Individuals with Disabilities Education Act (IDEA) mandate of providing Free and Appropriate Public Education (FAPE) for students with disabilities. Exacerbated by the COVID-19 pandemic, this shortage disproportionately affects teachers of color. Moreover, it extends beyond K-12 systems, impacting special education educator preparation programs, where faculties are often drawn from practicing teachers pursuing advanced degrees. This paper delineates the multifaceted implications of the shortage of special education teachers of color in both teacher education programs and K-12 systems. Emphasizing the indispensable role and specialized skill set of special educators, we offer strategies to mitigate this crisis, including improvements in preparation, recruitment, and retention efforts. Anticipating a diverse readership encompassing teacher education programs, K-12 administrators, teachers, and education policymakers, we aim to foster awareness, prompt dialog, and inspire actionable steps to address this pressing issue.
Attitudes Towards and Communications with People with Disabilities in Saudi Arabia: Towards the Sustainability of a Healthy Citizenry
Authors: Abed Mohaned G. , Abed Lowai G. , Shackelford Todd K.
Published: November 2024

The sustainability of a healthy, well-functioning citizenry depends on the acceptance and inclusion of people with disabilities. We investigated attitudes towards and communications with people with disabilities in Saudi Arabia by securing self-reports from 236 Saudi Arabian adults in a survey developed for this research, alongside demographic variables including gender, disability status, and age. The results indicate minimal effects of attitudes towards and communications with people with disabilities on their quality of life. Additional results indicate the effects of religious factors on attitudes towards and communications with people with disabilities, and also highlight the potential negative social consequences of misconceptions about people with disabilities. Such results indicate a continuing need to educate the Saudi Arabian public about disabilities, if the country is to facilitate and sustain a healthy citizenry.
RETRACTED: He, G.; Yu, T. Evaluating the Benefits and Potential of “Plastic Reduction”: A Case Study of College Students in Western China. Sustainability 2023, 15, 16807
Authors: He Guoqiang , Yu Tao
Published: November 2024

The journal retracts the article “Evaluating the Benefits and Potential of “Plastic Reduction”: A Case Study of College Students in Western China” [...]