bluuebunny/crossref_metadata_2025 huggingface dataset:
Formulation Study of a Poly(amino methacrylate) Film-Forming Solution for Transdermal Administration
Authors: Gennari Chiara G. M. , Casiraghi Antonella , Selmin Francesca , Cilurzo Francesco
Published: January 2025

Background/Objectives: The objective of this paper is to design a novel film-forming system (FFS) based on Eudragit® E PO (EuE) polymeric solutions, differing in volatile solvents (i.e., isopropanol and ethanol) and plasticizers (i.e., tributylcitrate, glycerine, triacetin and PEG 400). Methods: The physicochemical and mechanical properties of the FFS and dried films were evaluated in terms of formation time, stickiness, Tg, tensile strength, break elongation and Young’s modulus. The in vitro skin permeation studies were conducted on formulations containing caffeine and testosterone. Results: The FFS, consisting of EuE and PEG400 in isopropyl alcohol and ethanol (80:20, v/v), exhibited rapid film formation within about 5 min and the dried film allowed a high skin permeability compared to other formulations due to the ability to increase the thermodynamic activity of both drugs. When triiodothyronine (T3) was loaded as a model of a very low soluble drug, tocopherol polyethylene glycol succinate (TPGS) was added as a co-solvent and it allowed for the improvement of T3 retention in the skin. Conclusions: Among the formulative variables, the nature and the amount of plasticizer represent the most critical variables to obtain an EuE-based film with satisfying physical and biopharmaceutical properties.
Glycyrrhizinate Monoammonium Cysteine-Loaded Lipid Nanoparticles Allow for Improved Acute Liver Injury Therapy
Authors: Xu Yunjie , Li Pinghui , Sun Shiran , Chen Yulin , Feng Lixia , Jiang Dawei , Wan Chidan , Li Jianbo , Cai Xiong
Published: January 2025

Background: Acute liver injury (ALI) is a prevalent and potentially lethal condition globally, where pharmacotherapy plays a vital role. However, challenges such as rapid drug excretion and insufficient concentration at hepatic lesions often impede the treatment’s effectiveness. Methods: We successfully prepared glycyrrhizinate monoammonium cysteine (GMC)-loaded lipid nanoparticles (LNPs) using high-pressure homogenization. The characterization and safety of the LNPs were measured using electrophoretic light scattering (ELS), transmission electron microscopy (TEM), dynamic light scattering (DLS), cytotoxicity assays, and hemolysis tests. The distribution of LNPs in mice was explored using fluorescence labeling methods. The encapsulation efficiency of LNP-GMC was detected using High-Performance Liquid Chromatography (HPLC), and its slow-release effect on GMC was assessed through dialysis. The therapeutic effects of LNP-GMC and pure GMC on the ALI model were evaluated using fibroblast activation protein inhibitor (FAPI) PET imaging, blood biochemical indicators, and liver pathology slices. Results: The encapsulation of GMC in LNPs enhances drug stability and prolongs its hepatic retention, significantly improving its bioavailability and sustained release within the liver. This study also explores the expression of fibroblast activation protein (FAP) in ALI, employing 68Ga-FAPI PET/CT imaging for effective differentiation and assessment of liver injury. Conclusions: Our results suggest that LNPs offer an enhanced therapeutic approach for ALI treatment, reducing the required drug dosage, and 68Ga-FAPI PET/CT imaging provides a novel method for diagnosis and treatment assessment. This study contributes valuable insights into the utilization of LNPs in liver disease treatment, presenting a promising direction for future clinical applications.
Application of Light-Responsive Nanomaterials in Bone Tissue Engineering
Authors: Liu Aiguo , Wang Chenxu , Deng Shuang , Zhang Sitong , Zhao Ziwen , Xiao Han , Ying Ting , Yi Chengqing , Li Dejian
Published: January 2025

The application of light-responsive nanomaterials (LRNs) in bone tissue engineering shows broad prospects, especially in promoting bone healing and regeneration. With a deeper understanding of the mechanisms of bone defects and healing disorders, LRNs are receiving increasing attention due to their non-invasive, controllable, and efficient properties. These materials can regulate cellular biological reactions and promote bone cell adhesion, proliferation, and differentiation by absorbing specific wavelengths of light and converting them into physical and chemical signals. In addition, the unique surface morphology and biocompatibility of LRNs enable them to effectively load drugs in bone tissue engineering, achieve precise release, and optimize the bone regeneration process. Through photothermal and photodynamic therapy, these materials also possess antibacterial properties and can play an important role in the repair of infectious bone defects. Although LRNs have shown significant advantages in bone tissue regeneration, a series of challenges still need to be overcome to achieve their widespread and effective clinical applications. This article summarizes the basic principles, classification, and potential applications of LRNs in bone tissue regeneration, aiming to provide reference for future research and clinical applications.
Enhanced Performance of High-Power InAs/GaAs Quantum Dot Lasers Through Indium Flushing
Authors: Dai Deyan , Liu Hanqing , Shang Xiangjun , Tan Shizhuo , Zhang Qiaozhi , Yang Chengao , Jiang Dongwei , Su Xiangbin , Ni Haiqiao , Niu Zhichuan
Published: January 2025

InAs/GaAs quantum dots (QDs) appear promising for optoelectronic applications. However, the inhomogeneous broadening caused by natural strain and the non-uniform size distribution deteriorates the device performance based on multi-stacked QD layers. In this study, In-flush was incorporated during the epitaxy, and the photoluminescence (PL) linewidth was significantly narrowed to 26.1 meV for the flushed sample and maintained to 27.3 meV for the unflushed sample. The flushed sample shows better device performance in threshold current (0.229 to 0.334 A at 15 °C), power (1.142 to 1.113 W at 15 °C), and characteristic temperature (51 to 39 K in the range of 55~80 °C) compared with the unflushed sample.
Morphometric Investigation of a Species Complex in Mimosa Section Batocaulon Series Cordistipulae (Leguminosae, Caesalpinioideae)
Authors: Nascimento Janaína G. A. , Queiroz Luciano P. , Machado Marlon C. , van den Berg Cássio
Published: January 2025

Mimosa series Cordistipulae was created by Barneby in 1991, embracing species diagnosed by their small subshrubby habit and the presence of gland-tipped setae and trimerous flowers. Most species are endemic to Northeastern Brazil, and some possess characters deemed diagnostic which nonetheless overlap, making species identification difficult. Our study aimed to test species circumscriptions and sets of characters that could be applied to unequivocally distinguish the species. Twelve populations (225 individuals) were collected at nine localities, encompassing the Brazilian vegetation types Caatinga, Campos Rupestres and Restinga. Linear measurements of 38 floral and vegetative characters were measured and analyzed using Canonical Variate Analysis and cluster analysis. The first two canonical axes explained 41.4% and 18.9% of the variation and separated two populations of the group recently described as a new species. Vegetative characters are more informative for species delimitation than flower characters, and most groups are distinguished primarily by the number of pinnae pairs, number of leaflets per pinna and length of the leaf rachis. The species displaying the highest morphological similarity are M. misera, M. leptantha and M. minarum. The traditional morphometric approach was capable of objectively dealing with a type of variation that would be difficult to interpret by purely examining herbarium specimens.
Integrative Omics Analysis Reveals Mechanisms of Anthocyanin Biosynthesis in Djulis Spikes
Authors: Zheng Chunmei , Ge Wenxuan , Li Xueying , Wang Xiuzhang , Sun Yanxia , Wu Xiaoyong
Published: January 2025

Djulis (Chenopodium formosanum Koidz.), a member of the Amaranthaceae family plant, is noted for its vibrant appearance and significant ornamental value. However, the mechanisms underlying color variation in its spikes remain unexplored. This research initially detected the anthocyanin content at different developmental stages of the spike and subsequently utilized an integrative approach, combining targeted metabolomics, transcriptomics, and untargeted metabolomics analyses, to elucidate the mechanisms of anthocyanin biosynthesis in the spikes of djulis. The results of the combined multi-omics analysis showed that the metabolites associated with anthocyanin synthesis were mainly enriched in the flavonoid biosynthesis pathway (ko00941) and the anthocyanin biosynthesis pathway (ko00942). With the maturation of djulis spikes, a total of 28 differentially expressed genes and 17 differentially expressed metabolites were screened during the transition of spike color from green (G) to red (R) or orange (O). Twenty differentially expressed genes were selected for qRT-PCR validation, and the results are consistent with transcriptome sequencing. The upregulation of seven genes, including chalcone synthase (CfCHS3_1, CfCHS3_2, CfCHS3_3), flavanone 3-hydroxylase (CfF3H_3), flavonoid 3′5′-hydroxylase (CfCYP75A6_1), dihydroflavonol reductase (CfDFRA), and glucosyltransferase (Cf3GGT), promotes the formation and accumulation of delphinidin 3-sambubioside and peonidin 3-galactoside. The research results also showed that anthocyanins and betalains can coexist in the spike of djulis, and the reason for the change in spike color during development may be the result of the combined action of the two pigments. A possible regulatory pathway for anthocyanin biosynthesis during the spike maturation was constructed based on the analysis results. The results provide a reference and theoretical basis for further studying the molecular mechanism of anthocyanin regulation of color changes in Amaranthaceae plants.
Bryophytes Floristic Patterns in the Sicilian Aquatic and Humid Habitats—Important Refuges for Biodiversity Safeguarding in the Mediterranean Islands
Authors: Campisi Patrizia , Marino Mattia Letizia
Published: January 2025

In this study, a focus on the populations of bryophytes living in aquatic and humid habitats of Sicily is presented. This investigation aims to evaluate the consistency and diversity of this group of taxa. The complete list of taxa known to date in these habitats is provided, with reference to hornworts, liverworts, and mosses, and the patterns related to the biological, ecological, and chorological features of this bryophyte flora are also illustrated. Since climate change is currently the major threat of extinction for many of these species growing in aquatic and humid environments, these taxa constitute a component of the flora worthy of particular interest in studies on the quantification of the impact of global warming on biodiversity. Overall, the analysis found that, with a total of 224 taxa, this group constitutes 36% of the Sicilian bryophyte flora; it is quite diversified from a taxonomic point of view, being represented by 55 families and 106 genera. Related to ecological features and reproductive biology, some aspects of possible vulnerability are highlighted, as well as others of resilience. Moreover, the importance of future monitoring of these taxa is underlined, also in consideration of the fact that some of them, such as Sciuro-hypnum ornellanum, are species of conservation interest in Italy and in Europe, and that four other taxa are threatened in Italy.
The Genus Chiliadenus: A Comprehensive Review of Taxonomic Aspects, Traditional Uses, Phytochemistry and Pharmacological Activities
Authors: Castillo Lucinda Villaescusa , García Francisco Zaragozá , Arnáez Cristina Zaragozá
Published: January 2025

The genus Chiliadenus (Asteraceae) has been traditionally used in Mediterranean medicine for its anti-inflammatory, antioxidant, and antimicrobial properties. However, scientific research on this genus remains limited, highlighting the need for a comprehensive review of its chemical composition and pharmacological characteristics. This review compiles existing knowledge on Chiliadenus species, focusing on their secondary metabolites, such as flavonoids, terpenes, and essential oils, as well as associated biological activities. The findings show that the traditional therapeutic properties of Chiliadenus are well supported by reported pharmacological activities in previous studies, emphasizing the potential of this genus for the development of new therapeutic agents. However, the lack of comparative studies among Chiliadenus species and the scarcity of in vivo studies and clinical trials hinder the full realization of its therapeutic potential. Specifically, comparative studies could be key to identifying species with unique chemical profiles and understanding how variations in secondary metabolite composition may influence their pharmacological activities. This work highlights the urgent need to expand research in these areas to validate the pharmacological properties of Chiliadenus species for their application in modern medicine.
The Physicochemical and Rheological Properties of Green Banana Flour–Wheat Flour Bread Substitutions
Authors: Bashmil Yasmeen M. , Bekes Frank , Ruderman Michael , Suleria Hafiz A. R. , Appels Rudi , Dunshea Frank R.
Published: January 2025

Functional foods are currently receiving increasing popularity in diet modification. Green bananas contain far more dietary fiber (DF) and resistant starch (RS) than mature bananas. The potential for integrating these vital components into food, such as bread, has expanded. Thus, this study aimed to examine the physicochemical and rheological behavior of wheat flour dough after the addition of varying amounts of Australian, green banana flour (GBF) substitutions (5, 10, 15, 25, and 30%). Using MixoLab 2, we recorded the rheological parameters of the dough that had GBF substitutions. Additionally, the flour color (‘L*’, ‘a*’, and ‘b*’ value) and crumb cell structure analysis were evaluated. Although increasing the amount of GBF replacement generally improved dough quality with all banana cultivars, GBF from Cavendish and Ladyfinger showed a greater improvement than Ducasse. Improved dough mixing stability and increased viscosity, starch gelatinization, and retrogradation were all predicted to contribute to longer bread shelf life. RS content of the enriched bread increased significantly with both Ladyfinger and Ducasse (2.6%), while Ladyfinger bread had the highest DF (9.1%). With increasing GBF, L*, a*, and b* values were changed considerably with a strong linear correlation. A MATLAB analysis indicated substantial variations across samples regarding the small, medium, and total air space counts based on 10% banana flour as a standard level of addition. In conclusion, the processing properties and nutritional value of wheat flour can be enhanced by replacing specific proportions of wheat flour with green banana flour without major detrimental effects on dough processing attributes and thus highlight the possibility of utilizing GBF from different banana varieties for use in fine-tuning composite flour developments.
Dressed Up to the Nines: The Interplay of Phytohormones Signaling and Redox Metabolism During Plant Response to Drought
Authors: Jardim-Messeder Douglas , de Souza-Vieira Ygor , Sachetto-Martins Gilberto
Published: January 2025

Plants must effectively respond to various environmental stimuli to achieve optimal growth. This is especially relevant in the context of climate change, where drought emerges as a major factor globally impacting crops and limiting overall yield potential. Throughout evolution, plants have developed adaptative strategies for environmental stimuli, with plant hormones and reactive oxygen species (ROS) playing essential roles in their development. Hormonal signaling and the maintenance of ROS homeostasis are interconnected, playing indispensable roles in growth, development, and stress responses and orchestrating diverse molecular responses during environmental adversities. Nine principal classes of phytohormones have been categorized: auxins, brassinosteroids, cytokinins, and gibberellins primarily oversee developmental growth regulation, while abscisic acid, ethylene, jasmonic acid, salicylic acid, and strigolactones are the main orchestrators of environmental stress responses. Coordination between phytohormones and transcriptional regulation is crucial for effective plant responses, especially in drought stress. Understanding the interplay of ROS and phytohormones is pivotal for elucidating the molecular mechanisms involved in plant stress responses. This review provides an overview of the intricate relationship between ROS, redox metabolism, and the nine different phytohormones signaling in plants, shedding light on potential strategies for enhancing drought tolerance for sustainable crop production.
The Role of Light Irradiation and Dendrimer Generation in Directing Electrostatic Self-Assembly
Authors: Agarwal Mohit , Zika Alexander , Yücel Müge , Schweins Ralf , Kohlbrecher Joachim , Gröhn Franziska
Published: January 2025

pH-responsive polyamidoamine (PAMAM) dendrimers are used as well-defined building blocks to design light-switchable nano-assemblies in solution. The complex interplay between the photoresponsive di-anionic azo dye Acid Yellow 38 (AY38) and the cationic PAMAM dendrimers of different generations is presented in this study. Electrostatic self-assembly involving secondary dipole–dipole interactions provides well-defined assemblies within a broad size range (10 nm–1 μm) with various shapes. The size and shape of these assemblies were determined using dynamic and static light scattering (DLS/SLS) and small-angle neutron scattering (SANS); ζ-potential measurements were performed to elucidate the charge characteristics, revealing the effective surface charge density of the nano-objects as an important parameter in the size and shape control. UV–vis spectroscopy and isothermal titration calorimetry (ITC) were employed to investigate the interaction on a molecular level and from a thermodynamic point of view. The results show that the amount of isomerized cis dye depends on the dendrimer generation because of a photoprotective effect through electrostatics for lower generations and through dipole–dipole interactions for higher generations; as the cis dye and trans dye bind with different strength, the amount of cis dye then again encodes the charge density and thereby the particle size and shape.
Sterically Induced Enhancement in the Electrochemical Stability of Salen-Type Cathode Materials
Authors: Novoselova Julia V. , Beletskii Evgenii V. , Lukyanov Daniil A. , Filippova Sofia S. , Rodionova Uliana M. , Sizov Vladimir V. , Alekseeva Elena V. , Levin Oleg V.
Published: January 2025

This study investigates the electrochemical degradation mechanisms of nickel–salen (NiSalen) polymers, with a focus on improving the material’s stability in supercapacitor applications. We analyzed the effects of steric hindrance near the nickel center by incorporating different bulky substituents into NiSalen complexes, aiming to mitigate water-induced degradation. Electrochemical performance was assessed using cyclic voltammetry, operando conductance, and impedance measurements, while X-ray photoelectron spectroscopy (XPS) provided insights into molecular degradation pathways. The results revealed that increased steric hindrance from methyl groups significantly reduced the degradation rate, particularly in water-containing electrolytes, by hindering water coordination to the Ni center. Among the studied polymers, the highly substituted poly[Ni(Saltmen)] exhibited superior stability with minimal capacity loss. Density functional theory (DFT) calculations further supported that steric protection around the Ni atom effectively lowers the probability of water coordination. These findings suggest that sterically enhanced NiSalen polymers may offer a promising path toward durable supercapacitor electrodes, highlighting the route of molecular engineering to enhance material stability.
Optimization of Combustion Parameters in the Fire Tube of Water Jacket Heating Furnace Based on FLUENT
Authors: Lu Mei , Tian Yuan , Wang Jie , Lv Congmin
Published: January 2025

The combustion calculation domain of a water jacket heating furnace was established, and the fuel consumption and air consumption were optimized based on FLUENT. The amount of air consumption is based on the theoretical value of combustion, an air excess coefficient of 1.2 is taken, and the fuel consumption rate is set at 110, 130, 150, 170, and 190 m3/h. A comparative analysis of the calculation results shows that when the fuel consumption rate is 170 m3/h, the fuel combustion in the fire tube is the most intense, the combustion temperature is the highest, and the average temperature on the inner wall of the fire tube is the highest. Based on the optimal fuel consumption rate of 170 m3/h, the air consumption continues to be optimized. The air consumption was characterized by the air excess coefficient, which was 1.05, 1.10, 1.15, 1.20, 1.25, and 1.30, respectively. The comparative analysis of the calculation results shows that the flame temperature and diffusion combustion are the highest in the fire tube when the air excess coefficient is 1.25, but the average temperature of the inner wall of the fire tube is low, and the heat transfer effect is not optimal, while the air coefficient is 1.15.
Improving Human Activity Recognition Through 1D-ResNet: A Wearable Wristband for 14 Workout Movements
Authors: Kim Sang-Un , Kim Joo-Yong
Published: January 2025

This study presents a 1D Residual Network(ResNet)-based algorithm for human activity recognition (HAR) focused on classifying 14 different workouts, which represent key exercises commonly performed in fitness training, using wearable inertial measurement unit (IMU) sensors. Unlike traditional 1D Convolutional neural network (CNN) models, the proposed 1D ResNet incorporates residual blocks to prevent gradient vanishing and exploding problems, allowing for deeper networks with improved performance. The IMU sensor, placed on the wrist, provided Z-axis acceleration data, which were used to train the model. A total of 901 data samples were collected from five participants, with 600 used for training and 301 for testing. The model achieved a recognition accuracy of 97.09%, surpassing the 89.03% of a 1D CNN without residual blocks and the 92% of a cascaded 1D CNN from previous research. These results indicate that the 1D ResNet model is highly effective in recognizing a wide range of workouts. The findings suggest that wearable devices can autonomously classify human activities and provide personalized training recommendations, paving the way for AI-driven personal training systems.
Advancing Sustainable Electrical Energy Technologies: A Multifaceted Approach Towards SDG Achievement
Authors: Kolhe Mohan Lal
Published: January 2025

The transition to a sustainable energy future is a complex yet essential global challenge, necessitating advancements in technology, supportive policy frameworks, and societal shifts [...]
Spatiotemporal Dynamics of Drought in the Huai River Basin (2012–2018): Analyzing Patterns Through Hydrological Simulation and Geospatial Methods
Authors: You Yuanhong , Zhang Yuhao , Lu Yanyu , Hao Ying , Tang Zhiguang , Hou Haiyan
Published: January 2025

As climate change intensifies, extreme drought events have become more frequent, and investigating the mechanisms of watershed drought has become highly significant for basin water resource management. This study utilizes the WRF-Hydro model in conjunction with standardized drought indices, including the standardized precipitation index (SPI), standardized soil moisture index (SSMI), and Standardized Streamflow Index (SSFI), to comprehensively investigate the spatiotemporal characteristics of drought in the Huai River Basin, China, from 2012 to 2018. The simulation performance of the WRF-Hydro model was evaluated by comparing model outputs with reanalysis data at the regional scale and site observational data at the site scale, respectively. Our results demonstrate that the model showed a correlation coefficient of 0.74, a bias of −0.29, and a root mean square error of 2.66% when compared with reanalysis data in the 0–10 cm soil layer. Against the six observational sites, the model achieved a maximum correlation coefficient of 0.81, a minimum bias of −0.54, and a minimum root mean square error of 3.12%. The simulation results at both regional and site scales demonstrate that the model achieves high accuracy in simulating soil moisture in this basin. The analysis of SPI, SSMI, and SSFI from 2012 to 2018 shows that the summer months rarely experience drought, and droughts predominantly occurred in December, January, and February in the Huai River Basin. Moreover, we found that the drought characteristics in this basin have significant seasonal and interannual variability and spatial heterogeneity. On the one hand, the middle and southern parts of the basin experience more frequent and severe agricultural droughts compared to the northern regions. On the other hand, we identified a time–lag relationship among meteorological, agricultural, and hydrological droughts, uncovering interactions and propagation mechanisms across different drought types in this basin. Finally, we concluded that the WRF-Hydro model can provide highly accurate soil moisture simulation results and can be used to assess the spatiotemporal variations in regional drought events and the propagation mechanisms between different types of droughts.
Characterizing Sparse Spectral Diversity Within a Homogenous Background: Hydrocarbon Production Infrastructure in Arctic Tundra near Prudhoe Bay, Alaska
Authors: Sousa Daniel , Baskaran Latha , Miner Kimberley , Bushnell Elizabeth Josephine
Published: January 2025

We explore a new approach for the parsimonious, generalizable, efficient, and potentially automatable characterization of spectral diversity of sparse targets in spectroscopic imagery. The approach focuses on pixels which are not well modeled by linear subpixel mixing of the Substrate, Vegetation and Dark (S, V, and D) endmember spectra which dominate spectral variance for most of Earth’s land surface. We illustrate the approach using AVIRIS-3 imagery of anthropogenic surfaces (primarily hydrocarbon extraction infrastructure) embedded in a background of Arctic tundra near Prudhoe Bay, Alaska. Computational experiments further explore sensitivity to spatial and spectral resolution. Analysis involves two stages: first, computing the mixture residual of a generalized linear spectral mixture model; and second, nonlinear dimensionality reduction via manifold learning. Anthropogenic targets and lakeshore sediments are successfully isolated from the Arctic tundra background. Dependence on spatial resolution is observed, with substantial degradation of manifold topology as images are blurred from 5 m native ground sampling distance to simulated 30 m ground projected instantaneous field of view of a hypothetical spaceborne sensor. Degrading spectral resolution to mimicking the Sentinel-2A MultiSpectral Imager (MSI) also results in loss of information but is less severe than spatial blurring. These results inform spectroscopic characterization of sparse targets using spectroscopic images of varying spatial and spectral resolution.
Hybrid Offset Position Encoding for Large-Scale Point Cloud Semantic Segmentation
Authors: Xiao Yu , Wu Hui , Chen Yisheng , Chen Chongcheng , Dong Ruihai , Lin Ding
Published: January 2025

In recent years, large-scale point cloud semantic segmentation has been widely applied in various fields, such as remote sensing and autonomous driving. Most existing point cloud networks use local aggregation to abstract unordered point clouds layer by layer. Among these, position embedding serves as a crucial step. However, current methods of position embedding have limitations in modeling spatial relationships, especially in deeper encoders where richer spatial positional relationships are needed. To address these issues, this paper summarizes the advantages and disadvantages of mainstream position embedding methods and proposes a novel Hybrid Offset Position Encoding (HOPE) module. This module comprises two branches that compute relative positional encoding (RPE) and offset positional encoding (OPE). RPE combines explicit encoding to enhance position features through attention, learning position bias implicitly, while OPE calculates absolute position offset encoding by considering differences with grouping embeddings. These two encodings are adaptively mixed in the final output. The experiment conducted on multiple datasets demonstrates that our module helps the deep encoders of the network capture more robust features, thereby improving model performance on various baseline models. For instance, PointNet++ and PointMetaBase enhanced with HOPE achieved mIoU gains of 2.1% and 1.3% on the large-scale indoor dataset S3DIS area-5, 2.5% and 1.1% on S3DIS 6-fold, and 1.5% and 0.6% on ScanNet, respectively. RandLA-Net with HOPE achieved a 1.4% improvement on the large-scale outdoor dataset Toronto3D, all with minimal additional computational cost. PointNet++ and PointMetaBase had approximately only a 0.1 M parameter increase. This module can serve as an alternative for position embedding, and is suitable for point-based networks requiring local aggregation.
Extending Satellite Predictions of Coral Disease Outbreak Risk to Non-Seasonal Coral Reef Regions
Authors: Yoshida Momoe , Heron Scott F.
Published: January 2025

Coral disease outbreaks have increased in frequency and extent worldwide since the 1970s, coinciding with the rapid increase in ocean warming. Summer and winter temperature-based metrics have proven effective in predicting coral disease outbreaks in seasonal coral reef regions. However, their utility is unknown in non-seasonal coral reef areas. Here, a new methodology, independent of seasonal patterns, is developed for application in both seasonal and non-seasonal coral reef regions. Percentile-based metric thresholds were defined from seasonal equivalents in the Great Barrier Reef (GBR) and tested in seasonal and non-seasonal coral reef regions of the tropical Pacific Ocean. Between new and existing methodologies, median differences of 0.00 °C (thresholds) and 0.00 °C-weeks (metrics) for Hot Snap and Cold Snap; and 0.01 °C (threshold) and −0.17 °C-weeks (metric) for Winter Condition were observed among reef pixels of the GBR. The new methodology shows strong consistency with the existing tools used for seasonal regions (e.g., R2 = 0.811–0.903; GBR case studies). Comparisons of the new metrics with disease observations were constrained by the limited availability of disease data; however, the comparisons undertaken suggest predictive capability in non-seasonal regions. To establish robust correlations, further direct comparisons of the new metrics with disease data across various non-seasonal regions and timeframes are essential. With ocean warming projected to persist in the coming decades, improving the predictive tools used to assess ecological impacts is necessary to support effective coral reef management.
Characterizing Tidal Marsh Inundation with Synthetic Aperture Radar, Radiometric Modeling, and In Situ Water Level Observations
Authors: Lamb Brian T. , McDonald Kyle C. , Tzortziou Maria A. , Tesser Derek S.
Published: January 2025

Tidal marshes play a globally critical role in carbon and hydrologic cycles by sequestering carbon dioxide from the atmosphere and exporting dissolved organic carbon to connected estuaries. These ecosystems provide critical habitat to a variety of fauna and also reduce coastal flood impacts. Accurate characterization of tidal marsh inundation dynamics is crucial for understanding these processes and ecosystem services. In this study, we developed remote sensing-based inundation classifications over a range of tidal stages for marshes of the Mid-Atlantic and Gulf of Mexico regions of the United States. Inundation products were derived from C-band and L-band synthetic aperture radar (SAR) imagery using backscatter thresholding and temporal change detection approaches. Inundation products were validated with in situ water level observations and radiometric modeling. The Michigan Microwave Canopy Scattering (MIMICS) radiometric model was used to simulate radar backscatter response for tidal marshes across a range of vegetation parameterizations and simulated hydrologic states. Our findings demonstrate that inundation classifications based on L-band SAR—developed using backscatter thresholding applied to single-date imagery—were comparable in accuracy to the best performing C-band SAR inundation classifications that required change detection approaches applied to time-series imagery (90.0% vs. 88.8% accuracy, respectively). L-band SAR backscatter threshold inundation products were also compared to polarimetric decompositions from quad-polarimetric Phased Array L-band Synthetic Aperture Radar 2 (PALSAR-2) and L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) imagery. Polarimetric decomposition analysis showed a relative shift from volume and single-bounce scattering to double-bounce scattering in response to increasing tidal stage and associated increases in classified inundated area. MIMICS modeling similarly showed a relative shift to double-bounce scattering and a decrease in total backscatter in response to inundation. These findings have relevance to the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission, as threshold-based classifications of wetland inundation dynamics will be employed to verify that NISAR datasets satisfy associated mission science requirements to map wetland inundation with classification accuracies better than 80% at 1 hectare spatial scales.
Temperature Is a Key Factor Affecting Total Phosphorus and Total Nitrogen Concentrations in Northeastern Lakes Based on Sentinel-2 Images and Machine Learning Methods
Authors: Qin Haoming , Fang Chong , Liu Ge , Song Kaishan , Li Zhuoshi , Li Sijia , Tao Hui , Yan Zhaojiang
Published: January 2025

Nitrogen and phosphorus are limiting nutrients in freshwater ecosystems, and the remote estimation of total phosphorus (TP) and total nitrogen (TN) in eutrophic waters is of great significance. This study utilized machine learning algorithms based on Sentinel-2 satellite imagery for remote estimation of TP and TN concentrations in Lake Xingkai, Chagan and Songhua. Results indicate that random forest (RF) and XGBoost regression algorithms perform better. The performance of the GBDT algorithm was slightly lower than that of the RF and XGBoost regression algorithms, the BP algorithm had overfitting, and the SVR algorithm had poor fitting performance. Results showed that the TN concentration inversion model based on the RF algorithm had the highest accuracy (R2 = 0.98, RMSE = 0.09, MAPE = 19.74%). The Extreme Gradient Boosting (XGB) model also performed well, though slightly less accurately than RF (R2 = 0.97, RMSE = 0.14, MAPE = 20.67%). For TP concentration, the XGB model’s performance (R2 = 0.82, RMSE = 0.08, MAPE = 24.89%) was comparable to that of the RF model (R2 = 0.82, RMSE = 0.07, MAPE = 29.55%). The RF algorithm was applied to all cloud-free Sentinel-2 satellite images of these typical lakes in northeastern China during the non-glacial period from 2017 to 2023, generating spatiotemporal distribution maps of TP and TN concentrations. Between 2017 and 2023, TP concentrations in Lake Xingkai, Chagan and Songhua showed increasing, decreasing, and initially decreasing then increasing patterns, respectively. A positive correlation between temperature and TP concentration was observed, as higher temperatures enhance biological activity. In contrast, a negative correlation was found with TN concentration, as higher temperatures promote phytoplankton growth and reproduction. This study not only offers a new method for monitoring eutrophication in lakes but also provides valuable support for sustainable water resource management and ecological protection goals.
A Fast Identification Method for Seismic Responses of Bridge Structures by Integrating Digital Signal Features and Deep Learning
Authors: Lv Zhaoxu , Ding Youliang , Guo Junxiao
Published: January 2025

A method of bridge structure seismic response identification combining signal processing technology and deep learning technology is proposed. The short-time energy method is used to intelligently extract the non-smooth segments in the sensor acquired signals, and the short-time Fourier transform, continuous wavelet transform, and Meier frequency cestrum coefficients are used to analyze the spectrum of the non-smooth segments of the response of the bridge structure, and the response feature matrix is extracted and used to classify sequences or images in the LSTM network and the Resnet50 network. The results show that the signal processing techniques can effectively extract the structural response features and reduce the overfitting phenomenon of neural networks, and the combination of signal processing techniques and deep learning techniques can recognize the seismic response of bridge structures with high accuracy and efficiency.
Benzo[1,2-b:6,5-b’]dithiophene-4,5-diamine: A New Fluorescent Probe for the High-Sensitivity and Real-Time Visual Monitoring of Phosgene
Authors: Zhang Yingzhen , Xiao Jun , Peng Ruiying , Feng Xueliang , Mao Haimei , Liu Kunming , Liu Zhenzhong , Ma Chunxin
Published: January 2025

The detection of highly toxic chemicals such as phosgene is crucial for addressing the severe threats to human health and public safety posed by terrorist attacks and industrial mishaps. However, timely and precise monitoring of phosgene at a low cost remains a significant challenge. This work is the first to report a novel fluorescent system based on the Intramolecular Charge Transfer (ICT) effect, which can rapidly detect phosgene in both solution and gas phases with high sensitivity by integrating a benzo[1,2-b:6,5-b’]dithiophene-4,5-diamine (BDTA) probe. Among existing detecting methods, this fluorescent system stands out as it can respond to phosgene within a mere 30 s and has a detection limit as low as 0.16 μM in solution. Furthermore, the sensing mechanism was rigorously validated through high-resolution mass spectrometry (HRMS) and density functional theory (DFT) calculations. As a result, this fluorescent probing system for phosgene can be effectively adapted for real-time, high-sensitivity sensing and used as a test strip for visual monitoring without the need for specific equipment, which will also provide a new strategy for the fluorescent detection of other toxic materials.
Multi-Structure-Based Refractive Index Sensor and Its Application in Temperature Sensing
Authors: Yan Zhaokun , Yan Shubin , Xu Ziheng , Chen Changxing , Cao Yuhao , Yan Xiaoran , Wang Chong , Wu Taiquan
Published: January 2025

In this paper, a new sensor structure is designed, which consists of a metal–insulator–metal (MIM) waveguide and a circular protrusion and a rectangular triangular cavity (CPRTC). The characterization of nanoscale sensors is considered using an approximate numerical method (finite element method). The simulation results show that the sharp asymmetric resonance generated by the interaction between the discrete narrow-band mode and the continuous wideband mode is called Fano resonance. The performance of the sensor is considerably influenced by CPRTC. The sensor structure has attained a sensitivity of 3060 nm/RIU and a figure of merit (FOM) of 53.68. In addition, the application of this structure to temperature sensors is also investigated; its sensitivity is 1.493 nm/°C. The structure also has potential for other nanosensors.
Optimized Airborne Millimeter-Wave InSAR for Complex Mountain Terrain Mapping
Authors: Xie Futai , Wang Wei , Sun Xiaopeng , Xie Si , Wei Lideng
Published: January 2025

The efficient acquisition and processing of large-scale terrain data has always been a focal point in the field of photogrammetry. Particularly in complex mountainous regions characterized by clouds, terrain, and airspace environments, the window for data collection is extremely limited. This paper investigates the use of airborne millimeter-wave InSAR systems for efficient terrain mapping under such challenging conditions. The system’s potential for technical application is significant due to its minimal influence from cloud cover and its ability to acquire data in all-weather and all-day conditions. Focusing on the key factors in airborne InSAR data acquisition, this study explores advanced route planning and ground control measurement techniques. Leveraging radar observation geometry and global SRTM DEM data, we simulate layover and shadow effects to formulate an optimal flight path design. Additionally, the study examines methods to reduce synchronous ground control points in mountainous areas, thereby enhancing the rapid acquisition of terrain data. The results demonstrate that this approach not only significantly reduces field work and aviation costs but also ensures the accuracy of the mountain surface data generated by airborne millimeter-wave InSAR, offering substantial practical application value by reducing field work and aviation costs while maintaining data accuracy.
Real-Time On-Device Continual Learning Based on a Combined Nearest Class Mean and Replay Method for Smartphone Gesture Recognition
Authors: Park Heon-Sung , Sung Min-Kyung , Kim Dae-Won , Lee Jaesung
Published: January 2025

Sensor-based gesture recognition on mobile devices is critical to human–computer interaction, enabling intuitive user input for various applications. However, current approaches often rely on server-based retraining whenever new gestures are introduced, incurring substantial energy consumption and latency due to frequent data transmission. To address these limitations, we present the first on-device continual learning framework for gesture recognition. Leveraging the Nearest Class Mean (NCM) classifier coupled with a replay-based update strategy, our method enables continuous adaptation to new gestures under limited computing and memory resources. By employing replay buffer management, we efficiently store and revisit previously learned instances, mitigating catastrophic forgetting and ensuring stable performance as new gestures are added. Experimental results on a Samsung Galaxy S10 device demonstrate that our method achieves over 99% accuracy while operating entirely on-device, offering a compelling synergy between computational efficiency, robust continual learning, and high recognition accuracy. This work demonstrates the potential of on-device continual learning frameworks that integrate NCM classifiers with replay-based techniques, thereby advancing the field of resource-constrained, adaptive gesture recognition.
Incorporating Multimodal Directional Interpersonal Synchrony into Empathetic Response Generation
Authors: Quan Jingyu , Miyake Yoshihiro , Nozawa Takayuki
Published: January 2025

This study investigates how interpersonal (speaker–partner) synchrony contributes to empathetic response generation in communication scenarios. To perform this investigation, we propose a model that incorporates multimodal directional (positive and negative) interpersonal synchrony, operationalized using the cosine similarity measure, into empathetic response generation. We evaluate how incorporating specific synchrony affects the generated responses at the language and empathy levels. Based on comparison experiments, models with multimodal synchrony generate responses that are closer to ground truth responses and more diverse than models without synchrony. This demonstrates that these features are successfully integrated into the models. Additionally, we find that positive synchrony is linked to enhanced emotional reactions, reduced exploration, and improved interpretation. Negative synchrony is associated with reduced exploration and increased interpretation. These findings shed light on the connections between multimodal directional interpersonal synchrony and empathy’s emotional and cognitive aspects in artificial intelligence applications.
IMU Sensor-Based Worker Behavior Recognition and Construction of a Cyber–Physical System Environment
Authors: Park Sehwan , Youm Minkyo , Kim Junkyeong
Published: January 2025

According to South Korea’s Ministry of Employment and Labor, approximately 25,000 construction workers suffered from various injuries between 2015 and 2019. Additionally, about 500 fatalities occur annually, and multiple studies are being conducted to prevent these accidents and quickly identify their occurrence to secure the golden time for the injured. Recently, AI-based video analysis systems for detecting safety accidents have been introduced. However, these systems are limited to areas where CCTV is installed, and in locations like construction sites, numerous blind spots exist due to the limitations of CCTV coverage. To address this issue, there is active research on the use of MEMS (micro-electromechanical systems) sensors to detect abnormal conditions in workers. In particular, methods such as using accelerometers and gyroscopes within MEMS sensors to acquire data based on workers’ angles, utilizing three-axis accelerometers and barometric pressure sensors to improve the accuracy of fall detection systems, and measuring the wearer’s gait using the x-, y-, and z-axis data from accelerometers and gyroscopes are being studied. However, most methods involve use of MEMS sensors embedded in smartphones, typically attaching the sensors to one or two specific body parts. Therefore, in this study, we developed a novel miniaturized IMU (inertial measurement unit) sensor that can be simultaneously attached to multiple body parts of construction workers (head, body, hands, and legs). The sensor integrates accelerometers, gyroscopes, and barometric pressure sensors to measure various worker movements in real time (e.g., walking, jumping, standing, and working at heights). Additionally, incorporating PPG (photoplethysmography), body temperature, and acoustic sensors, enables the comprehensive observation of both physiological signals and environmental changes. The collected sensor data are preprocessed using Kalman and extended Kalman filters, among others, and an algorithm was proposed to evaluate workers’ safety status and update health-related data in real time. Experimental results demonstrated that the proposed IMU sensor can classify work activities with over 90% accuracy even at a low sampling rate of 15 Hz. Furthermore, by integrating internal filtering, communication modules, and server connectivity within an application, we established a cyber–physical system (CPS), enabling real-time monitoring and immediate alert transmission to safety managers. Through this approach, we verified improved performance in terms of miniaturization, measurement accuracy, and server integration compared to existing commercial sensors.