bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring the Relationship Between Nursing Staff and Family Members’ Appraisal of Resident Care in Nursing Homes: The Role of Facility Ownership
Authors: Millar Roberto J. , Diehl Christin , Kusmaul Nancy , Stockwell Ian
Published: February 2025

Background/Objectives: To address long-standing staffing challenges and elevating care standards in the United States, new legislation will require a minimum of 0.55 h per resident day (HPRD) of registered nurse (RN) care, 2.45 HPRD of certified nursing aide (CNA) care, and a combined total of 3.48 HPRD across any combination of nursing staff. We examine differences in family members’ views of care quality between facilities meeting the minimum staffing requirements and those that do not and whether there is any difference in those associations by facility ownership. Methods: This cross-sectional study utilized public data from 218 Medicare and Medicaid-certified nursing facilities in Maryland, collected in 2023. We used regression analyses to examine the association between staffing requirements and quality of care ratings, considering facility ownership status as a potential moderator. Results: Compared to facilities with CNA staffing levels below the cut off, facilities that met the CNA staffing requirement were rated more favorably by family members in overall quality and across the subdomains of staffing, care, activities, and security. In contrast, meeting the RN 0.55 cut off was not associated with family ratings across any quality domain. A facility for-profit status did not moderate the relationship between staffing and family ratings. Conclusions: These results suggest that CNA staff time is a significant driver of care quality and that non-profit facilities may already be closer to meeting new federal requirements. These findings highlight the need for regulations that support the minimum nursing staffing requirements to enhance care quality. Future research should identify the specific factors contributing to higher quality care in non-profit facilities and explore ways to implement these practices in for-profit settings.
Functional Foods as a New Therapeutic Strategy 2.0
Authors: Santos-Sánchez Guillermo , Cruz-Chamorro Ivan
Published: February 2025

Non-communicable diseases (NCDs), including obesity, cardiovascular conditions, cancer, and type II diabetes, are the primary causes of mortality worldwide, accounting for over 41 million deaths annually [...]
Synthesis of Bispidine-Based Prostate-Specific Membrane Antigen-Targeted Conjugate and Initial Investigations
Authors: Machulkin Aleksei E. , Petrov Stanislav A. , Kraynova Maria D. , Garanina Anastasiia S. , Egorova Bayirta V. , Timoshenko Roman V. , Vaneev Alexander N. , Erofeev Alexander S. , Priselkova Anna B. , Kalinin Mikhail A. , Medved′ko Aleksei V. , Kalmykov Stepan N. , Beloglazkina Elena K. , Vatsadze Sergey Z.
Published: February 2025

Nowadays, PSMA ligands are widely used for radiotheragnostic purposes in prostate cancer. The synthesis of a PSMA-Bisp conjugate was developed and realized with good yield (overall yield ~58% for the last two steps). All newly synthesized compounds were characterized by physicochemical methods: 1H and 13C NMR, HRMS, and LCMS (for biologically tested samples). Subsequently, Bisp1 (diacetate bispidine ligand), Bisp-alkyne (bifunctional derivative of Bisp1), and its conjugate PSMA-Bisp were labeled by 64Cu in mild conditions. In vitro studies of the labeled conjugate [64Cu]Cu-PSMA-Bisp have shown great stability in model solutions. Finally, [64Cu]Cu-PSMA-Bisp was compared to the well-known PSMA-617 conjugate labeled with 64Cu and they showed similar stability in excess bovine serum (BVS), and at the same time, labeling PSMA-Bisp with 64Cu is characterized by extremely high kinetics in mild conditions, while labeling PSMA-617 with 64Cu requires heating (90 °C). Thus, this conjugate can be incredibly promising for nuclear medicine.
The ATLAS Inner Tracker Strip Detector System for the Phase-II Large Hadron Collider Upgrade
Authors: Duden Emily Duden
Published: February 2025

ATLAS is currently preparing for the HL-LHC upgrade, with an all-silicon Inner Tracker (ITk) that will replace the current Inner Detector. The ITk will feature a pixel detector surrounded by a strip detector, with the strip system consisting of four barrel layers and 12 endcap disks. After successful completion of a large-scale prototyping program, final design reviews have been completed in key areas, such as sensors, modules, front-end electronics, and ASICs. We present an overview of the strip system and highlight the final design choices of sensors, modules, and ASICs. We summarize the results achieved during prototyping and the current status of production and pre-production on various detector components, with an emphasis on QA and QC procedures.
Staphylococcus aureus in Inflammation and Pain: Update on Pathologic Mechanisms
Authors: Rasquel-Oliveira Fernanda S. , Ribeiro Jhonatan Macedo , Martelossi-Cebinelli Geovana , Costa Fernanda Barbosa , Nakazato Gerson , Casagrande Rubia , Verri Waldiceu A.
Published: February 2025

Staphylococcus aureus (S. aureus) is a Gram-positive bacterium of significant clinical importance, known for its versatility and ability to cause a wide array of infections, such as osteoarticular, pulmonary, cardiovascular, device-related, and hospital-acquired infections. This review describes the most recent evidence of the pathogenic potential of S. aureus, which is commonly part of the human microbiota but can lead to severe infections. The prevalence of pathogenic S. aureus in hospital and community settings contributes to substantial morbidity and mortality, particularly in individuals with compromised immune systems. The immunopathogenesis of S. aureus infections involves intricate interactions with the host immune and non-immune cells, characterized by various virulence factors that facilitate adherence, invasion, and evasion of the host’s defenses. This review highlights the complexity of S. aureus infections, ranging from mild to life-threatening conditions, and underscores the growing public health concern posed by multidrug-resistant strains, including methicillin-resistant S. aureus (MRSA). This article aims to provide an updated perspective on S. aureus-related infections, highlighting the main diseases linked to this pathogen, how the different cell types, virulence factors, and signaling molecules are involved in the immunopathogenesis, and the future perspectives to overcome the current challenges to treat the affected individuals.
C24 Ceramide Lipid Nanoparticles for Skin Wound Healing
Authors: Lee Ji-Hye , Kim Jin-Hyun , Hyeon Tong-Il , Min Khee-Tae , Lee Se-Young , Ko Han-Chul , Choi Hong-Seok , Ju Kuk-Youn , Cho Young-Seok , Yoon Tae-Jong
Published: February 2025

Background/Objectives: C24 ceramide plays a crucial role in skin regeneration and wound healing; however, its hydrophobic nature limits its application in therapeutic formulations. This study aims to enhance the bioavailability and efficacy of C24 ceramide by developing ceramide-based lipid nanoparticles (C24-LNP) and evaluate their impact on skin regeneration and wound healing. Methods: C24-LNP was synthesized and characterized for aqueous stability and bioavailability. In vitro experiments were conducted to assess its effects on keratinocyte proliferation and migration. Molecular biological analysis examined key signaling pathways, including AKT and ERK1/2 phosphorylation. Additionally, an in vivo mouse wound model was utilized to evaluate wound healing efficacy, with histological analysis performed to assess epidermal and dermal regeneration. Results: C24-LNP exhibited improved aqueous stability and bioavailability compared to free C24 ceramide. In vitro studies demonstrated that C24-LNP significantly promoted keratinocyte proliferation and migration. Molecular analysis revealed activation of the AKT and ERK1/2 signaling pathways, which are critical for cell growth and skin regeneration. In vivo wound healing experiments showed that C24-LNP accelerated wound closure compared to the control group. Histological analysis confirmed enhanced epidermal and dermal regeneration, leading to improved structural and functional skin repair. Conclusion: The lipid nanoparticle formulation of C24 ceramide effectively increases its bioavailability and enhances its therapeutic efficacy in skin regeneration and wound healing. C24-LNP presents a scalable and cost-effective alternative to traditional growth factor-based therapies, offering significant potential for clinical applications in wound care and dermatological treatments.
Scintillation Properties of β-Ga2O3 Under the Excitation of Ultra-High-Charge Electron Bunches
Authors: Liang Yulan , Sun Jianhan , Zhang Chaoyi , Xu Tianqi , Chen Haoran , Huang Huaqing , Hua Chenhao , Wan Pengying , Dai Chuanwei , Wu Qingfan , Liu Juntao , Huang Lin , Lin Lin , Tang Huili , Xue Jianming , Xu Jun , Huang Senlin , Liu Bo , Ma Wenjun
Published: February 2025

The performance of ultrafast scintillators under ultrahigh dose rate is highly important for applications utilizing brilliant radiation sources. In this work, the scintillation properties of β-Ga2O3, a high-performance ultrafast wide-bandgap semiconductor scintillator, are systematically investigated under dose rates of 107 to 109 Gy/s for the first time by employing ultrashort high-charge electron bunches (bunch charge from 500 fC to 50 pC) generated from a superconducting radio-frequency accelerator. Our results show that in spite of the ultrahigh dose rate, the scintillation intensity was still linearly proportional to the electron bunch charge. Lifetime analysis reveals a fast decay component ranging from 3 to 4 ns, along with an average lifetime of 20 ns. These findings establish a solid foundation for the application of β-Ga2O3 as the scintillation material for high-charge electron sources such as laser-wakefield accelerated electrons.
Bacteria Pseudomonas sp. and Pantoea sp. Are the New Etiological Agents of Diseases on Forest Trees
Authors: Porotikova Elena , Brusnova Natalia , Sushchenko Andrei , Kolganikhina Galina , Vinogradova Svetlana
Published: February 2025

Forest trees significantly affect human life. The spread of pathogens, including bacterial ones, poses a serious threat to their health. Despite this, however, the species composition and distribution of pathogenic bacteria, as well as the etiology of common diseases affecting forest trees, remain virtually unstudied. In this study, we, for the first time, describe different species of Pseudomonas and Pantoea as new etiological agents associated with the symptoms of leaf spotting and wood darkening on Acer tataricum L., Fraxinus pennsylvanica L., Ulmus minor Mill. Ulmus laevis Pallas. and Populus tremula L. For the identification of bacteria species, we used an integrated approach based on the characterization of their morphology, biochemistry, physiology and genetics. Phylogenetic analysis was performed using multilocus typing for five genes for Pseudomonas and six genes for Pantoea. Leaf spotting on A. tataricum, F. pennsylvanica, U. minor and U. laevis was shown to be caused by Pseudomonas cerasi, Pseudomonas congelans, Pseudomonas graminis, Pseudomonas syringae and Pantoea agglomerans both in monoinfection and coinfection. Wood darkening in U. minor U. laevis and P. tremula was found to be associated with the presence of Pantoea sp. and P. agglomerans. The coinfection of forest trees with bacteria of the genera Pseudomonas and Pantoea indicates a complex mechanism of interaction between the two populations, which will be the subject of future studies.
Facile Enhancement of Mechanical Interfacial Strength of Recycled Carbon Fiber Web-Reinforced Polypropylene Composites via a Single-Step Silane Modification Process
Authors: Song Yeo-Jun , Kim Dong-Kyu , Han Woong , Choi Sun-Ho , Chung Dong-Chul , Kim Kwan-Woo , Kim Byung-Joo
Published: February 2025

In this study, a surface treatment process was introduced into the conventional dispersion process for preparing wet-laid nonwoven fabrics to improve their properties, using recycled carbon fibers (rCFs). The conventional binder solution was replaced with a solution containing different amounts of silane, and the changes in the fiber properties of the prepared nonwoven fabrics were examined after the addition of modified rCFs and polypropylene. FE-SEM analysis confirmed that a silane layer was formed on the rCF surface due to the formation of a siloxane network. FT-IR and XPS analyses further confirmed the presence of siloxane bonds and chemical modification of the rCF surface. When an optimal amount of silane content was used, the mechanical strength increased by 64% compared to untreated rCFs, owing to the improved molecular chain entanglement within the matrix. Our findings indicate that the simultaneous use of dispersion and a surface treatment can produce composites with excellent mechanical properties and improved processing and surface properties; thus, this method can be used to help upcycle rCFs, thereby expanding their applications.
Impact of Parameters and Tree Stand Features on Accuracy of Watershed-Based Individual Tree Crown Detection Method Using ALS Data in Coniferous Forests from North-Eastern Poland
Authors: Kozniewski Marcin , Kolendo Łukasz , Chmur Szymon , Ksepko Marek
Published: February 2025

The accurate detection of individual tree crowns and estimation of tree density is essential for effective forest management, biodiversity assessment, and ecological monitoring. The precision of tree crown detection algorithms plays a critical role in providing reliable data for these applications, where even slight inaccuracies can lead to significant deviations in tree population estimates and ecological indicators. Various algorithmic parameters, such as pixel size and crown segmentation thresholds, can substantially impact tree crown detection accuracy. This study aims to explore the influence of tree stand features and parameters on the effectiveness of the individual tree crown detection method based on a watershed algorithm, leading to identifying optimal configurations that enhance the reliability of forest inventories and support sustainable management practices. Our analysis of the algorithm results shows that the features of the tree stand, such as tree height variance and tree crown size variance, significantly impact the algorithm’s output in precisely estimating tree count. Consequently, adjusting the pixel size of a canopy height model in the context of tree stand features is necessary to minimize error. Additionally, our findings show that there is a need to carefully assess the criterion of membership of a detected tree crown in a circular sample plot, which we based on the point cloud.
Multi-Model Ensemble Enhances the Spatiotemporal Comprehensive Performance of Regional Climate in China
Authors: Wang Yan , Shen Yan-Jun , Wang Leibin , Guo Ying , Cheng Yuanyuan , Zhang Xiaolong
Published: February 2025

The multi-model ensemble (MME) approaches are highly regarded in climate prediction and risk assessment for their capacity to integrate multiple global climate models (GCMs) and minimize uncertainties associated with individual models. However, the quantitative impacts of spatial scale, weighted ensemble, and bias correction on the spatiotemporal comprehensive performance of MME remain unknown. In this study, we comprehensively assessed the historical simulation capabilities of 41 CMIP6 GCMs at national, basin, and grid scales. Additionally, we investigated the impact of bias correction and weighted ensemble on enhancing climate simulation performance. The results indicate that CMIP6 models exhibit notable differences in simulating regional climate characteristics of China across different scales. Weighted multi-model ensemble schemes incorporating better-performing models consistently outperform equal-weight approaches, achieving an average 20.67% reduction in the DISO (distance between indices of simulation and observation) index, with temporal performance improvements being particularly pronounced. Bias correction played a critical role in the enhancement of MMEs, reducing DISO values by 41.60% on average, particularly in the spatial dimension. Among all MMEs, the grid-scale optimized ensemble (GBQ), combining bias correction, model selection, and performance-based weighting, demonstrated superior comprehensive performance, achieving the lowest DISO values across spatial and temporal dimensions. These findings provide new insights for enhancing regional climate simulation and evaluation, and they provide more reliable scientific information for investigating climate change and formulating adaptation strategies in China.
Sparse Magnetization Vector Inversion Based on Modulus Constraints
Authors: Ou Yang , Lü Qingtian , Zhang Jie , Yang Yi , Jia Dingyu , Li Yang , Zhai Jinghong , Jiang Zhengzhong
Published: February 2025

Magnetization vector inversion (MVI) is an effective method for simultaneously determining the distribution of magnetization intensity and direction without knowing the direction of magnetization beforehand. Nevertheless, the presence of serious non-uniqueness in MVI imposes challenges in achieving accurate and reliable results. To improve the accuracy of MVI, we propose a method that incorporates a modulus constraint, informed by an analysis of the model constraints in two different frameworks. We employ a sparse operator on the magnetization magnitude and obtain an explicit expression for the magnetization components, establishing correlation constraints among them. Synthetic test results show that this method can achieve models with clear boundaries and consistent magnetization directions. Furthermore, the application of a sparse operator to the gradient’s modulus of the magnetization magnitude helps recover inclined structures. However, the dispersed magnetization directions suggest that we should also constrain the magnetization direction, simultaneously. The inversion of magnetic data measured over the Zaohuohexi iron-polymetallic deposit in Qinghai Province, northwest China, verified the proposed approach’s effectiveness.
Research on Hyperspectral Inversion of Soil Organic Carbon in Agricultural Fields of the Southern Shaanxi Mountain Area
Authors: Han Yunhao , Wang Bin , Yang Jingyi , Yin Fang , He Linsen
Published: February 2025

Rapidly obtaining information on the content and spatial distribution of soil organic carbon (SOC) in farmland is crucial for evaluating regional soil quality, land degradation, and crop yield. This study focuses on mountain soils in various crop cultivation areas in Shangzhou District, Shangluo City, Southern Shaanxi, utilizing ZY1-02D hyperspectral satellite imagery, field-measured hyperspectral data, and field sampling data to achieve precise inversion and spatial mapping of the SOC content. First, to address spectral bias caused by environmental factors, the Spectral Space Transformation (SST) algorithm was employed to establish a transfer relationship between measured and satellite image spectra, enabling systematic correction of the image spectra. Subsequently, multiple spectral transformation methods, including continuous wavelet transform (CWT), reciprocal, first-order derivative, second-order derivative, and continuum removal, were applied to the corrected spectral data to enhance their spectral response characteristics. For feature band selection, three methods were utilized: Variable Importance Projection (VIP), Competitive Adaptive Reweighted Sampling (CARS), and Stepwise Projection Algorithm (SPA). SOC content prediction was conducted using three models: partial least squares regression (PLSR), stepwise multiple linear regression (Step-MLR), and random forest (RF). Finally, leave-one-out cross-validation was employed to optimize the L4-CARS-RF model, which was selected for SOC spatial distribution mapping. The model achieved a coefficient of determination (R2) of 0.81, a root mean square error of prediction (RMSEP) of 1.54 g kg−1, and a mean absolute error (MAE) of 1.37 g kg−1. The results indicate that (1) the Spectral Space Transformation (SST) algorithm effectively eliminates environmental interference on image spectra, enhancing SOC prediction accuracy; (2) continuous wavelet transform significantly reduces data noise compared to other spectral processing methods, further improving SOC prediction accuracy; and (3) among feature band selection methods, the CARS algorithm demonstrated the best performance, achieving the highest SOC prediction accuracy when combined with the random forest model. These findings provide scientific methods and technical support for SOC monitoring and management in mountainous areas and offer valuable insights for assessing the long-term impacts of different crops on soil ecosystems.
Deep Learning-Based Seedling Row Detection and Localization Using High-Resolution UAV Imagery for Rice Transplanter Operation Quality Evaluation
Authors: Luo Yangfan , Dai Jiuxiang , Shi Shenye , Xu Yuanjun , Zou Wenqi , Zhang Haojia , Yang Xiaonan , Zhao Zuoxi , Li Yuanhong
Published: February 2025

Accurately and precisely obtaining field crop information is crucial for evaluating the effectiveness of rice transplanter operations. However, the working environment of rice transplanters in paddy fields is complex, and data obtained solely from GPS devices installed on agricultural machinery cannot directly reflect the specific information of seedlings, making it difficult to accurately evaluate the quality of rice transplanter operations. This study proposes a CAD-UNet model for detecting rice seedling rows based on low altitude orthorectified remote sensing images, and uses evaluation indicators such as straightness and parallelism of seedling rows to evaluate the operation quality of the rice transplanter. We have introduced convolutional block attention module (CBAM) and attention gate (AG) modules on the basis of the original UNet network, which can merge multiple feature maps or information flows together, helping the model better select key areas or features of seedling rows in the image, thereby improving the understanding of image content and task execution performance. In addition, in response to the characteristics of dense and diverse shapes of seedling rows, this study attempts to integrate deformable convolutional network version 2 (DCNv2) into the UNet network, replacing the original standard square convolution, making the sampling receptive field closer to the shape of the seedling rows and more suitable for capturing various shapes and scales of seedling row features, further improving the performance and generalization ability of the model. Different semantic segmentation models are trained and tested using low altitude high-resolution images of drones, and compared. The experimental results indicate that CAD-UNet provides excellent results, with precision, recall, and F1-score reaching 91.14%, 87.96%, and 89.52%, respectively, all of which are superior to other models. The evaluation results of the rice transplanter’s operation effectiveness show that the minimum and maximum straightnessof each seedling row are 4.62 and 13.66 cm, respectively, and the minimum and maximum parallelismbetween adjacent seedling rows are 5.16 and 23.34 cm, respectively. These indicators directly reflect the distribution of rice seedlings in the field, proving that the proposed method can quantitatively evaluate the field operation quality of the transplanter. The method proposed in this study can be applied to decision-making models for farmland crop management, which can help improve the efficiency and sustainability of agricultural operations.
Leveraging Ice, Cloud, and Land Elevation Satellite-2 Laser Altimetry and Surface Water Ocean Topography Radar Altimetry for Error Diagnosis in Hydraulic Models: A Case Study of the Chao Phraya River
Authors: Charoensuk Theerapol , Luchner Jakob , Bauer-Gottwein Peter
Published: February 2025

Recent advancements in satellite Earth observation (EO) technology have significantly improved the accuracy and density of data available for monitoring rivers and streams, as well as for diagnosing errors in hydraulic models. Laser and radar altimetry missions, such as ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) and SWOT (Surface Water and Ocean Topography), offer high-resolution measurements of land and water surface elevation (WSE), covering entire river reaches and providing high-resolution WSE profiles along the river chainage, which can be directly compared to hydraulic model results. In this study, we implemented a workflow to assess the accuracy of simulated WSE and evaluate the performance of hydraulic models in the Chao Phraya (CPY) River, using WSE data from ICESat-2 and SWOT. The evaluation of ICESat-2, SWOT, and simulated WSE from the model, compared to in situ data, resulted in root mean square error (RMSE) values of 0.34 m, 0.35 m, and 0.37 m, respectively. Despite this, both ICESat-2 and SWOT data proved effective for error detection and performance evaluation along the CPY river in point, profile, and spatial map comparisons, with overall RMSE values of 0.36 m and 0.33 m, respectively, when compared with simulated WSE. This paper demonstrates that ICESat-2 and SWOT are valuable tools for diagnosing errors and improving hydraulic model performance, providing critical insights for river monitoring and model validation.
DyGS-SLAM: Realistic Map Reconstruction in Dynamic Scenes Based on Double-Constrained Visual SLAM
Authors: Zhu Fan , Zhao Yifan , Chen Ziyu , Jiang Chunmao , Zhu Hui , Hu Xiaoxi
Published: February 2025

Visual SLAM is widely applied in robotics and remote sensing. The fusion of Gaussian radiance fields and Visual SLAM has demonstrated astonishing efficacy in constructing high-quality dense maps. While existing methods perform well in static scenes, they are prone to the influence of dynamic objects in real-world dynamic environments, thus making robust tracking and mapping challenging. We introduce DyGS-SLAM, a Visual SLAM system that employs dual constraints to achieve high-fidelity static map reconstruction in dynamic environments. We extract ORB features within the scene, and use open-world semantic segmentation models and multi-view geometry to construct dual constraints, forming a zero-shot dynamic information elimination module while recovering backgrounds occluded by dynamic objects. Furthermore, we select high-quality keyframes and use them for loop closure detection and global optimization, constructing a foundational Gaussian map through a set of determined point clouds and poses and integrating repaired frames for rendering new viewpoints and optimizing 3D scenes. Experimental results on the TUM RGB-D, Bonn, and Replica datasets, as well as real scenes, demonstrate that our method has excellent localization accuracy and mapping quality in dynamic scenes.
Development of a Full-Field Integrated Radiation Observation System for Lunar Hyperspectral Irradiance Measurement
Authors: Jiang Ye , Ye Xin , Wang Yuwei , Lin Yuchen , Yang Dongjun , Fang Wei
Published: February 2025

The Moon serves as an ideal reference radiation source for on-orbit calibration of starborne optical remote sensing instruments. To enhance the characterization capability for lunar spectral radiation, the full-field integrated radiation observation system (FIROS) for lunar hyperspectral irradiance measurement has been developed. FIROS accomplished lunar hyperspectral irradiance measurements in the 400–1000 nm range by integrating and spectrally analyzing the radiation across the entire lunar disc, reducing the angular sensitivity and polarization sensitivity to lunar radiation. Performance tests and preliminary lunar observational experiments conducted on FIROS indicate that the system possesses excellent response linearity and environmental adaptability, with a reduction in lunar tracking accuracy requirements by approximately an order of magnitude compared to push-broom imaging observations. The performance and lunar observation capabilities of the system have been well validated. FIROS provides a lunar observation method that simultaneously achieves full-disk light collection and hyperspectral measurement, demonstrating strong environmental adaptability and laying a solid foundation for enhancing long-term stable lunar observation data and establishing lunar radiation benchmarks.
Method for Landslide Area Detection with RVI Data Which Indicates Base Soil Areas Changed from Vegetated Areas
Authors: Arai Kohei , Nakaoka Yushin , Okumura Hiroshi
Published: February 2025

This study investigates the use of the radar vegetation index (RVI) derived from Sentinel-1 synthetic aperture radar (SAR) data for landslide detection. Traditional landslide detection methods often rely on the Normalized Difference Vegetation Index (NDVI) derived from optical imagery, which is susceptible to limitations imposed by weather conditions (clouds, rain) and nighttime. In contrast, SAR data, acquired by Sentinel-1, provides all-weather, day-and-night coverage. To leverage this advantage, we propose a novel approach utilizing RVI, a vegetation index calculated from SAR data, to identify non-vegetated areas, which often indicate potential landslide zones. To enhance the accuracy of non-vegetated area classification, we employ the high-performing EfficientNetV2 deep learning model. We evaluated the classification performance of EfficientNetV2 using RVI derived from Sentinel-1 SAR data with VV and VH polarizations. Experiments were conducted on SAR imagery of the Iburi district in Hokkaido, Japan, severely impacted by an earthquake in 2018. Our findings demonstrate that the classification performance using RVI with both VV and VH polarizations significantly surpasses that of using VV and VH polarizations alone. These results highlight the effectiveness of RVI for identifying non-vegetated areas, particularly in landslide detection scenarios. The proposed RVI-based method has broader applications beyond landslide detection, including other disaster area assessments, agricultural field monitoring, and forest inventory.
Guided Wave Propagation in a Realistic CFRP Fuselage Panel: Proof of Concept for Early-Stage Damage Detection
Authors: Sellami Fatma , Memmolo Vittorio , Hornung Mirko
Published: February 2025

This paper presents an experimental study of wave motions and a global diagnostics method in a realistic aerospace-grade composite component with a complex design. Due to the frequency dependence of the velocity, wave propagation in anisotropic materials is difficult to describe quantitatively. The analysis of experimental ultrasonic wave propagation and the interactions with discontinuities in thin-walled aircraft structures can provide a plethora of information on the wave structure, the mode shapes, and stiffness reduction. An experiment is devised with a network of various omnidirectional sensor configurations to activate and measure structural responses. The measurement process can be leveraged for flaw detection in large multilayered structures. Physically, this corresponds to analyzing the dispersive behavior and scattering properties of ultrasonic waves, the shape of the waveforms, and their corresponding velocities. Ultrasonic waves are measured in a realistic CFRP fuselage panel in a pristine state and after impacts at different energy levels. Simulations do not allow the wave motion in complex and large design structures to be entirely comprehended. The sensitivity of the guided waves as a damage detection tool is proved for the fuselage structure by an extensive measurement campaign and a probabilistic imaging approach based on health indicator fusion.
Secure and Intelligent Single-Channel Blind Source Separation via Adaptive Variational Mode Decomposition with Optimized Parameters
Authors: Yan Meishuang , Chen Lu , Hu Wei , Sun Zhihong , Zhou Xueguang
Published: February 2025

Emerging intelligent systems rely on secure and efficient signal processing to ensure reliable operation in environments where there is limited prior knowledge and significant interference. Single-channel blind source separation (SCBSS) is critical for applications such as wireless communication and sensor networks, where signals are often mixed and corrupted. Variational mode decomposition (VMD) has proven effective for SCBSS, but its performance depends heavily on selecting the optimal modal component count k and quadratic penalty parameter α. To address this challenge, we propose a secure and intelligent SCBSS algorithm leveraging adaptive VMD optimized with Improved Particle Swarm Optimization (IPSO). The IPSO dynamically determines the optimal k and α parameters, enabling VMD to filter noise and create a virtual multi-channel signal. This signal is then processed using improved Fast Independent Component Analysis (IFastICA) for high-fidelity source isolation. Experiments on the RML2016.10a dataset demonstrate a 15.7% improvement in separation efficiency over conventional methods, with robust performance for BPSK and QPSK signals, achieving correlation coefficients above 0.9 and signal-to-noise ratio (SNR) improvements of up to 24.66 dB.
Vehicle Trajectory Repair Under Full Occlusion and Limited Datapoints with Roadside LiDAR
Authors: Luo Qiyang , Xu Zhenyu , Zhang Yibin , Igene Morris , Bataineh Tamer , Soltanirad Mohammad , Jimee Keshav , Liu Hongchao
Published: February 2025

Object occlusion is a common challenge in roadside LiDAR-based vehicle tracking. This issue can cause variances in vehicle location and speed calculations. This paper proposes a vehicle tracking post-processing method designed to handle full occlusion and limited datapoint conditions. The first part of the method focuses on linking the disconnected trajectories of the same vehicle caused by full occlusion. The second part refines the vehicle representative point to enhance tracking accuracy. Performance evaluation demonstrates that the proposed method can detect and reconnect the trajectories of the same vehicle, even under prolonged full occlusion. Moreover, the refined vehicle representative point provides more stable speed estimates, even with sparse datapoints. This significantly increases the effective detection range of roadside LiDAR. This approach lays a strong foundation for the application of roadside LiDAR in emission analysis and near-crash studies.
Electromagnetic Properties of Natural Plant Leaves for Eco-Friendly and Biodegradable Substrates for Wireless IoT Devices
Authors: Atanasov Nikolay Todorov , Atanasov Blagovest Nikolaev , Atanasova Gabriela Lachezarova
Published: February 2025

Today, innovative engineering solutions, including IoT devices, enable the precise monitoring of plant health and the early detection of diseases. However, the lifespan of IoT devices used for the real-time monitoring of environmental or plant parameters in precision agriculture is typically only a few months, from planting to harvest. This short lifespan creates challenges in managing the e-waste generated by smart agriculture. One potential solution to reduce the volume and environmental impact of e-waste is to use more environmentally friendly and biodegradable materials to replace the non-degradable components (substrates) currently used in the structure of IoT devices. In this study, we estimate the electromagnetic properties at 2565 MHz of the leaves from three widely grown crops: winter wheat, corn, and sunflower. We found that winter wheat and sunflower leaves have values of the real part of relative permittivity ranging from about 33 to 69 (wheat) and 13 to 32 (sunflower), respectively, while corn exhibits a value of about 33.5. Our research indicates that the position of a leaf on the plant stem and its distance from the soil significantly affect the relative permittivity of winter wheat and sunflower. These relationships, however, are not evident in the electromagnetic properties of corn leaves.
A Signal Pattern Extraction Method Useful for Monitoring the Condition of Actuated Mechanical Systems Operating in Steady State Regimes
Authors: Munteanu Adriana , Horodinca Mihaita , Bumbu Neculai-Eduard , Dumitras Catalin Gabriel , Chitariu Dragos-Florin , Mihai Constantin-Gheorghe , Khdair Mohammed , Oancea Lucian
Published: February 2025

The aim of this paper is to present an approach to condition monitoring of an actuated mechanical system operating in a steady-state regime. The state signals generated by the sensors placed on the mechanical system (a lathe headstock gearbox) operating in a steady-state regime contain a sum of periodic components, sometimes mixed with a small amount of noise. It is assumed that the state of a rotating part placed inside a mechanical system can be characterized by the shape of a periodic component within the state signal. This paper proposes a method to find the time domain description for the significant periodic components within these state signals, as patterns, based on the arithmetic averaging of signal samples selected at constant time regular intervals. This averaging has the same effect as a numerical filter with multiple narrow pass bands. The availability of this method for condition monitoring has been fully demonstrated experimentally. It has been applied to three different state signals: the active electrical power absorbed by an asynchronous AC electric motor driving a lathe headstock gearbox, the vibration of this gearbox, and the instantaneous angular speed of the output spindle. The paper presents some relevant patterns describing the behavior of different rotating parts within this gearbox, extracted from these state signals.
Electromagnetic Imaging in Half-Space Using U-Net with the Iterative Modified Contrast Scheme
Authors: Chiu Chien-Ching , Li Ching-Lieh , Chen Po-Hsiang , Li Yen-Chun , Lim Eng-Hock
Published: February 2025

U-Net with the iterative modified contrast scheme (IMCS) is proposed to solve inverse scattering problems (ISPs) in half-space. IMCS is an innovative inversion technique that utilizes contrast functions to improve the visibility of target regions and reconstruct the internal structure of objects. In contrast to applying IMCS alone, our proposed method improves the detection of contrast boundaries, enhancing noise immunity as well as increasing the structural similarity (SSI) through deep learning with U-Net. We compare the numerical results for 200-iteration IMCS and U-Net with 3-iteration IMCS, and it is found that the accuracy of reconstructed images can be improved a lot by U-Net with the 3-iteration IMCS architecture. In addition, even in the case of large Gaussian noise, the reconstruction is still good with our proposed method.
A Method for Mounting Space Telescope Optical Systems Based on the Sensitivity Matrix of Intrinsic Coefficients
Authors: Hou Han , Ding Hongchang , Dong Keyan , Cao Guohua , Wang Boyuan
Published: February 2025

Aperture space telescopes are widely used in space debris size information detection and celestial body detection work. For the problem of limited space inside the optical system of large aperture telescopes, a space telescope mounting method based on the intrinsic coefficient sensitivity matrix is proposed by combining the wavefront detection technology. Compared with the traditional sensitivity matrix method, the method in this paper does not need to partition the detector and simplifies the construction of the wavefront reconstruction matrix. Characterisation of the system wave aberration is realised by using the eigenfactors, and the sensitivity matrix model is established according to the amount of misalignment. The experimental tests are carried out on the telescope with a diameter of 1.2 m, and the results show that the root mean square (RMS) values of the wavefront aberration in the centre field of view are less than λ/16 under the cases of eccentricity misalignment of the sub-mirror and tilting misalignment with the phase-aberration correction, which is of good value for the mounting of space telescope optical systems.
Understanding Pelvic Organ Prolapse: A Comprehensive Review of Etiology, Epidemiology, Comorbidities, and Evaluation
Authors: Zumrutbas Ali Ersin
Published: February 2025

Pelvic organ prolapse (POP) is a prevalent condition characterized by the descent of one or more pelvic organs, such as the bladder, uterus, or rectum, into the vaginal canal due to weakened pelvic floor support. This comprehensive review elucidates the multifactorial etiology of POP, which includes genetic predisposition, hormonal changes, obstetric factors, lifestyle influences, and age-related pelvic floor decline. Epidemiological data underscore its rising prevalence, particularly among older women, highlighting disparities across populations and risk factors such as parity and socioeconomic status. POP frequently coexists with significant comorbidities like urinary incontinence, sexual dysfunction, and pelvic pain, which exacerbate its impact on quality of life. Accurate evaluation through patient history, physical examinations, and imaging modalities like ultrasound and MRI is critical for diagnosis and management. The review emphasizes advancements in POP evaluation methods, including the POP-Q system, and explores the interplay of anatomical, functional, and psychosocial factors affecting patient outcomes. Key findings reveal that a tailored and holistic approach to treatment, addressing both anatomical correction and associated symptoms, significantly improves patient outcomes. Surgical interventions can alleviate related comorbidities, including urinary and bowel dysfunctions, as well as enhance body image and sexual function. However, the long-term success of such treatments necessitates continued research into preventive strategies and innovative therapeutic approaches. In conclusion, understanding the complexities of POP’s etiology, epidemiology, and management is vital for mitigating its burden and improving the health and well-being of affected individuals. This review highlights the need for integrated care strategies and further studies to advance diagnostic and therapeutic paradigms.
Utility and Practicability of Nephrometry Scoring Systems in Contemporary Clinical Practice—An International Multicentre Perspective
Authors: Yanada Brendan A. , Homewood David , Dias Brendan H. , Corcoran Niall M. , Lawrentschuk Nathan , Sabnis Ravindra , Teoh Jeremy Y. C. , Agarwal Dinesh
Published: February 2025

To conduct a multi-institutional international survey to determine the clinical utility and applicability of nephrometry scoring systems in contemporary clinical practice. Methods: A cross-sectional anonymous 15-item online survey was conducted on REDCap between January 2023 and May 2023. Survey invitations were sent via email within Australia and internationally to urologists who are either members of the Urological Society of Australia and New Zealand (USANZ) or the Urological Association of Asia (UAA) or who have direct professional relationships with their members. The survey underwent a trial run on REDCap with several urologists at our institution to test the technical functionality and comprehension prior to dissemination. Results: First, 158 responses were collected and analysed. Just over half (51%) responded that they use a nephrometry system in clinical practice, and the RENAL nephrometry scoring system is the most commonly used. Amongst respondents who use a nephrometry scoring system, 63% stated that it helps with counselling patients and 54% stated it serves as a decision-making tool on whether to perform a partial or radical nephrectomy. Furthermore, 54% use a nephrometry scoring system in surgical planning meetings, and 67% believe that it is helpful for research purposes. Common concerns included that they are too time-consuming to complete, they are unhelpful for treatment decision-making and they are only useful for research purposes. Conclusions: Nephrometry scoring systems are utilised by roughly one in two urologists in contemporary clinical practice. Further qualitative studies are required to better ascertain perspectives towards them and enhance their clinical applicability.
The Impossibility of ‘Good Mothering’ in Child Welfare Systems When Referred for Non-Traditional Harms
Authors: Rutter Nikki , Firmin Carlene , Garvey Donna , O’Brien Kate , Owens Rachael
Published: February 2025

Due to harmful narratives within child welfare and child protection services and systems, mothers in contact with these services who aim to meet the symbolic representation of the ‘ideal mother’ frequently find themselves being portrayed as the ‘bad mother’, even when their referral is ‘non-traditional’ (i.e., not specifically due to their perceived harmful actions or inactions). Through ‘ideal mother’ symbolism and narratives, there is disenfranchisement of service-engaged mothers; they are mistreated by services, which is normalised by wider discourses around motherhood. Mothers within these child welfare systems consistently experience judgement, the problematising of their parenting practices, and disempowerment despite not being the direct cause of harm to their children. This creates a sense of shame and makes the injustice of mothers’ experiences within child welfare systems invisible. This is a conceptual paper combining data generated from previously published work and a lived experience example (work with young mothers; mothers in contact with the criminal justice system; mothers with children who cause harm; and those with children experiencing extra-familial harm) using qualitative, participatory, and action-based approaches, and through emancipatory interview processes, disenfranchised mothers described their contact with child welfare and child protection systems as a source of structural, political, and/or societal injustice. Thus, such qualitative emancipatory work provides ways to acknowledge mothers in contact with child welfare services due to non-traditional harms, as they are a disadvantaged group who are too often disempowered to action change. Thus, we argue that participatory and action-based research should be a preferred method of exploring mothers’ experiences of child welfare systems, opening routes for reforming, as well as understanding systematic potential of services as oppressive and problematising rather than supportive and empowering.
The Impact of Fires on the Fractional Composition of Iron and Carbon Dynamics in the Cryogenic Soils of the Forest–Tundra of Western Siberia Under Changing Climate Conditions
Authors: Andrey Yurtaev , Moskovchenko Dmitriy , Sedov Sergey , Sharapov Denis , Shvartseva Olga
Published: February 2025

Fires significantly influence the ecosystems of Western Siberia’s forest–tundra zone. Namely, they alter soil processes, including the transformation of different forms of iron and the redistribution of carbon flows. Recent climate change, associated with increased fire frequency, has had a long-term effect on the Arctic and sub-Arctic soil systems. Iron plays a key role in stabilizing organic carbon through the sorption and coagulation processes, yet the long-term changes in iron’s fractional composition under post-fire conditions remain insufficiently studied. This research investigates the impact of natural fires on the transformation of iron forms (amorphous, crystalline, and mobile), as well as on the dynamics of organic carbon in soils within the northern boundary of the forest–tundra natural zone in Western Siberia, between the Pur and Taz rivers. In our study, we have relied on granulometric and chemical analyses, magnetic susceptibility measurements, and iron fraction extractions. Our findings reveal that in post-fire areas, the depth of the seasonally thawed layer increases, accompanied by changes in the thermal and water regimes. This leads to reduced organic carbon content, particularly in intermediate horizons (5–30 cm), and the transformation of amorphous iron into a crystalline form. Crystallization growth is confirmed by increased magnetic susceptibility. Our results highlight the dual role of iron compounds: they contribute to the long-term stabilization of organic carbon, as well as causing its accelerated mineralization by affecting redox conditions. This study is crucial for understanding the biogeochemical processes associated with climate change and increasing fire frequency.