bluuebunny/crossref_metadata_2025 huggingface dataset:
Highly Sensitive Temperature Sensing in Biological Region with Ratiometric Fluorescent Response
Authors: Li Yan , Yu Han , Li Hongjuan , Sun Shiguo , Yu Ruijin , Xu Yongqian
Published: February 2025

Poly(2-oxazoline) (POx), a typical thermoresponsive polymer with good biocompatibility, was conjugated with environment-sensitive tetraphenylenethene (TPE) and hydroxyphenylbenzoxazole (HBO) to achieve unique thermometer readings. Through phase transition induced by temperature, the thermometers can measure temperature in biologic range with ratiometric fluorescence response, ultrahigh sensitivity and good reversibility. Moreover, the thermometer can be used to measure the change in temperature with large fluorescence difference in living cells.
Free Radical Scavenging Activity and Inhibition of Enzyme-Catalyzed Oxidation by trans-aryl-Palladium Complexes
Authors: Etsè Koffi Sénam , Harrad Mohamed Anouar , Etsè Kodjo Djidjolé , Zaragoza Guillermo , Demonceau Albert , Mouithys-Mickalad Ange
Published: February 2025

Herein, nine square planar trans-arylbis(triphenylphosphine)palladium halides (PdX(PPh3)2Ar) were synthesized and fully characterized. The molecular structure of two complexes (1 and 2) have been determined by both X-Ray diffraction and described thanks to Hirshfeld surface analysis. Investigation of the antioxidant activities showed that most of the complexes exhibit a strong dose-dependent radical scavenging activity towards DPPH radical as well as in the ABTS radical scavenging test. Complexes 1 [PdI(PPh3)2(4-MeOC6H4)] and 3 [PdCl(PPh3)2(4-MeOC6H4)] showed the highest activity in the DPPH assay with EC50 values of 1.14 ± 0.90 and 1.9 ± 0.87 µM, respectively. In contrast, for the ABTS assay, quercetin (5.56 ± 0.97 µM) was slightly more efficient than the three complexes 1 (5.78 ± 0.98 µM), 2 (7.01 ± 0.98 µM), and 3 (11.12 ± 0.94 µM). The use of kinetic studies as a powerful parameter shows that complexes 1, 2, and 3 displayed the best antioxidant efficiency. The antioxidant effect of the nine palladium complexes has been also evaluated on the enzyme-catalyzed oxidation of the L012 probe (using HRP/H2O2) by using a chemiluminescence technique. As with the last model, complexes 1, 2, and 3 showed the best activity, with EC50 values of 3.56 ± 1.87, 148 0.71, and 5.8 ± 2.60 µM, respectively. Interestingly, those complexes (1, 2, and 3) even exhibited a higher dose-dependent activity than the quercetin (7.06 ± 2.56 µM) used as a standard. Taken together, the combined results reveal that the antiradical and enzyme (HRP) inhibitory activity of complexes decrease following the ligand order of p-OMePh > p-OAcPh >> Ph.
Whitening and Anti-Inflammatory Activities of Exosomes Derived from Leuconostoc mesenteroides subsp. DB-21 Strain Isolated from Camellia japonica Flower
Authors: Choi Byeong-Min , Lee Gibok , Hong Hyehyun , Park Chang-Min , Yeom Areum , Chi Won-Jae , Kim Seung-Young
Published: February 2025

In the present study, we investigated the anti-inflammatory and anti-melanogenic effects of Leuconostoc mesenteroides subsp. DB-21-derived exosomes (DB-21 exosomes), isolated from Camellia japonica flower in lipopolysaccharide (LPS)-induced RAW 264.7 macrophage cells and melanocyte-stimulating hormone (α-MSH)-induced B16F10 melanoma cells. We confirmed that DB-21 exosomes were not toxic to LPS-induced RAW 264.7 macrophage cells and α-MSH-induced B16F10 melanoma cells. Moreover, we confirmed that DB-21 exosomes inhibit the pro-inflammatory cytokines IL-6, IL-1β, TNF-α, PGE2, and the expression of inflammatory factors iNOS and COX-2. We also found that DB-21 exosomes have a concentration-dependent ability to inhibit melanin, TRP-1, TRP-2, tyrosinase, and MITF, which are factors involved in melanogenesis. Additionally, it inhibits the phosphorylation of Akt and GSK-3β, and MAP kinase pathway proteins such as ERK, JNK, and p38. We confirmed that DB-21 exosomes inhibit melanin synthesis in B16F10 cells through various pathways, and based on previous results, they may be used as a functional cosmetic material with anti-inflammatory and anti-melanogenic activities.
Poly (Acrylic Acid)-Sodium Alginate Superabsorbent Hydrogels Synthesized Using Electron-Beam Irradiation—Part III: An Evaluation of Their Degradation in Soil
Authors: Manaila Elena , Calina Ion Cosmin , Dumitru Marius , Craciun Gabriela
Published: February 2025

Global challenges in agriculture, in terms of water and nutrient loss control, require new approaches to maintaining or even increasing crop production. Promising materials, such as superabsorbent hydrogels of hybrid types obtained from natural polymers grafted with synthetic polymers, represent a viable solution to solve these problems and maintain a clean environment. In view of this, two types of hydrogels based on sodium alginate, acrylic acid and polyethylene oxide obtained using 5.5 MeV electron-beam irradiation were subjected to degradation through burial in the soil. Swollen hydrogels in two types of water (distilled and tap) and two types of nutrient solutions (synthetic nutrient solution and 100% natural organic nutrient solution), with different pHs of 5.40, 6.05, 7.45 and 7.66, were buried in soil for 30 and 60 days and then extracted and analyzed in terms of their mass loss, swelling behavior and cross-linking structure. The highest mass losses after both 30 and 60 days were recorded for the hydrogels buried in soils whose humidity was maintained by watering them with the basic solutions (tap water and the organic nutrient solution). Structural modifications associated with the degradation process were highlighted by decreases in the cross-link densities and increases in the mesh sizes and swelling. These results were confirmed using FTIR and SEM techniques.
A Cross-Sectional Study of Iodine Nutritional Status Among School-Age Children in Chongqing, China
Authors: Pang Peng , Xie Jun , Yang Mengping , Zhou Shuang , Zhang Yong
Published: February 2025

Objectives: The aim of the present study was to evaluate the iodine nutritional statuses of children aged 8 to 10 years in Chongqing Municipality in 2023. Methods: In this study, we employed multi-stage stratified cluster sampling of non-boarding children aged 8 to 10 years in all 39 counties (districts). The levels of iodine in household salt and those in random urine samples collected from the subjects were tested. In addition, thyroid gland volume was examined using the ultrasound method in subjects from 13 of the counties (districts). Results: Of the total 7751 children aged 8 to 10 years selected for inclusion in this study, the median salt iodine concentration (SIC) was 26.7 mg/kg, with an interquartile range (IQR) of 24.2–29.4 mg/kg. The median urinary iodine concentration (UIC) was 226.4 μg/L, with an IQR of 149.5–309.6 μg/L. The median UIC differed significantly between genders and across various regions (p < 0.001). The prevalence of total goiter in the children was 2.24% (58/2591), and significant differences were observed in the prevalence of goiter in different body mass index (BMI) groups (p < 0.001). The greatest prevalence of goiter was observed in obese children. Conclusions: In 2023, children in Chongqing Municipality aged 8 to 10 years as a whole had adequate iodine nutrition and met the national standard for the eradication of iodine deficiency disorders.
Microbiome-Based Therapeutics for Salt-Sensitive Hypertension: A Scoping Review
Authors: Shremo Msdi Abdulwhab , Haghparast Anahita , Garey Kevin W. , Wang Elisabeth M.
Published: February 2025

The purpose of this scoping review was to provide a comprehensive understanding of the current knowledge concerning the gut microbiome and SCFAs as emerging treatments for salt-sensitive hypertension. Relevant animal and human studies were identified via PubMed through August 2024. Twenty-four human (n = 9) and animal (n = 15) trials were included. Most human studies were observational (n = 6), aiming to compare gut microbiota differences between hypertensive and normotensive individuals. Three human studies evaluated microbiome-based interventions either via a sodium-restricted diet (n = 2) or prebiotic supplementation (n = 1). Fifteen animal trials involving either mice or rats were identified, all of which were interventional. These included dietary changes (n = 9), probiotic treatments (n = 1), postbiotic primarily bacterial metabolites (n = 4), and live biotherapeutic products (n = 4). All interventions were effective in decreasing blood pressure. Microbiome-based therapies as biologic modifiers for salt-sensitive hypertension are emerging. Substantial knowledge gaps remain, warranting further research to fully explore this promising therapeutic avenue.
Breastfeeding Shapes the Gut Microbiota and Its Structure Is Associated with Weight Gain Trajectories in Mexican Infants
Authors: Arguelles-Lopez Alejandra , Aguayo-Patrón Sandra V. , Barca Ana M. Calderón de la
Published: February 2025

Background: Rapid weight gain in early infancy increases the risk of childhood obesity, while exclusive breastfeeding can protect against it, depending on breastmilk composition, maternal diet, and infant gut microbiota. Objective: The objective of this study was to analyze the association between maternal diet, breastmilk components, infant gut microbiota, and weight gain in the first year of life of Mexican breastfed infants. Methods: This longitudinal study included 27 mothers with exclusively breastfed infants (≥5 months of age). We evaluated maternal diet and breastmilk composition at 5 months postpartum (pp), the infant fecal microbiota at 5 and 12 months pp using 16S rRNA gene sequencing, and weight gain as normal, rapid or slow weight gain (NWG, RWG or SWG) in periods 1 (0–5.5 months) and 2 (5.5–12 months). Results: Infants with NWG in periods 1 and 2 made up 51% and 56%, respectively. In period 1, ingested breastmilk protein content was higher for NWG infants than for infants with SWG (p = 0.01), and the protein content was negatively correlated with maternal BMI (r = −0.42, p = 0.02). The genera Veillonella (19.5%), Bifidobacterium (19.5%), and Escherichia-Shigella (16.8%) dominated the microbiota at 5 months. At 12 months, Bacteroides predominated, and the first two genera remained. Breastmilk fat correlated with Veillonella abundance (r = −0.50, p = 0.02) and oligosaccharides with Lachnospiraceae (r = 0.73, p = 0.03) at 5 months. There was a trend of a higher abundance of Bifidobacterium in NWG infants than in other infants in period 1, while infants with RWG and SWG had a higher abundance of Ruminococcus gnavus (p = 0.03) in period 1 and Alistipes in period 2 (p = 0.01), respectively. Conclusions: Breastfeeding shaped the gut microbiota of exclusively breastfed infants, and its structure was associated with infant weight gain trajectories.
Indonesian Nursing Educators’ Experiences with Developing Student-Centered Learning Methods
Authors: Grøndahl Vigdis Abrahamsen , Andersen Kirsti Lauvli , Helgesen Ann Karin , Asniar Asniar , Martinsen Randi , Septiani Riza , Baharuddin Dharina , Woersaa Signe Rasch , Willman Anna , Olsen Liv Berit
Published: February 2025

Background/Objectives: There is a rising global demand to educate independent and reflective nurses capable of addressing the evolving needs of healthcare systems and diverse populations. The Indonesian Nursing Act solidifies the professional status of nurses, although challenges persist including discrepancies between practice and educational standards. There is a need to increase the competence of Indonesian nurse educators and their ability to facilitate learning that can strengthen the nursing programs’ quality and improve the nurses’ ability to address various health issues in the population. The aim of the study was to describe the nurse educators’ experiences with developing student-active methods in nursing education in Indonesia. Methods: This study is part of an Erasmus+ project: Capacity Building in Nursing Education in Indonesia (CABNEI). A three-year educational program for nursing educators in Indonesia was developed. The current study used a qualitative, descriptive design with a questionnaire containing four open-ended questions focusing on the educators’ experiences with developing student-active methods and conditions affecting this process. Data were collected between January and November 2022 and analyzed using qualitative content analysis. Results: A total of 32 educators from two universities and two nursing schools in Indonesia participated. The analysis revealed the theme “A change of pedagogical approaches to nursing students’ learning” with three categories: “Setting the stage for pedagogical development in the nursing education”, “From correction to reflection”, and “Factors affecting the educators’ learning process”. Conclusions: The nursing educators’ experiences with developing student-active methods in Indonesia were positive. They reported incorporating additional pedagogical tools into their teaching methods and nursing programs. Management is vital in facilitating active-learning strategies. It is important to foster collaborative opportunities for educators and enhance their pedagogical skills to meet the healthcare system’s evolving needs and society’s demands for sustainable nursing expertise.
HCC and Immunotherapy: The Potential Predictive Role of Gut Microbiota and Future Therapeutic Strategies
Authors: Laface Carmelo , Lauricella Eleonora , Ranieri Girolamo , Ambrogio Francesca , Maselli Felicia Maria , Parlagreco Elena , Bernardi Giulia , Fea Elena , Numico Gianmauro
Published: February 2025

During the last decade, a new therapeutic revolution has involved the management of hepatocellular carcinoma (HCC). This is made possible thanks to the documented efficacy of immunotherapy for this disease. In addition, new evidence has demonstrated the role of the gut–liver axis and gut microbiota in host homeostasis, tumor development, and response to therapies. In particular, intestinal dysbiosis can alter the tumor microenvironment, leading to the activation of intracellular signaling pathways that promote carcinogenesis. The composition of gut microbiota proved to influence the immune checkpoint inhibitors (ICIs) efficacy and drug toxicities. Therefore, this review aims to deepen knowledge about the immunomodulatory role of gut microbiota and its possible employment as diagnostic and predictive biomarkers in diagnosis and response to HCC immunotherapy, respectively. The research was conducted through the analysis of Pubmed and Web of Science (WoS) databases for literature studies on the relationship between gut microbiota and HCC from 2015 to 2025.
Refined ADME Profiles for ATC Drug Classes
Authors: Menestrina Luca , Parrondo-Pizarro Raquel , Gómez Ismael , Garcia-Serna Ricard , Boyer Scott , Mestres Jordi
Published: February 2025

Background: Modern generative chemistry initiatives aim to produce potent and selective novel synthetically feasible molecules with suitable pharmacokinetic properties. General ranges of physicochemical properties relevant for the absorption, distribution, metabolism, and excretion (ADME) of drugs have been used for decades. However, the therapeutic indication, dosing route, and pharmacodynamic response of the individual drug discovery program may ultimately define a distinct desired property profile. Methods: A methodological pipeline to build and validate machine learning (ML) models on physicochemical and ADME properties of small molecules is introduced. Results: The analysis of publicly available data on several ADME properties presented in this work reveals significant differences in the property value distributions across the various levels of the anatomical, therapeutic, and chemical (ATC) drug classification. For most properties, the predicted data distributions agree well with the corresponding distributions derived from experimental data across fourteen drug classes. Conclusions: The refined ADME profiles for ATC drug classes should be useful to guide the de novo generation of advanced lead structures directed toward specific therapeutic indications.
A Step Towards Real-Time Release Testing of Pharmaceutical Tablets: Utilization of CIELAB Color Space
Authors: Brands René , Le Trieu Nam , Bartsch Jens , Thommes Markus
Published: February 2025

Background: The pharmaceutical industry is shifting from end-product testing towards real-time release testing. This approach is based on the continuous collection of process data and product information, which is finally utilized for the release decision. For continuous direct compression, spectroscopic technologies are preferred due to their short acquisition time and non-destructive nature. Methods: Here, the feasibility of the CIELAB color space was demonstrated for porosity and tensile strength. Five different formulations were processed, varying in particle size and deformation behavior. The compression forces were varied from 3 to 18 kN and the CIELAB color space was measured in-line using a UV/Vis probe implemented in the ejection position of the tablet machine. Results: Increasing the main compression force during tableting decreases the tablet surface roughness and porosity. In addition, the tablet tensile strength increases. These changes affected the reflection behavior of radiation on the tablet surface, resulting in a change in the chroma value C*. These dependencies were utilized for the in-line monitoring of porosity and tensile strength. Linear relations were observed for all formulations as exemplary, indicated by sufficient coefficients of determination and verification runs. Conclusions: Finally, UV/Vis diffuse reflectance spectroscopy in combination with a CIELAB color space transformation was demonstrated to be a suitable real-time release tool.
The Influence of Heat Treatment and Laser Alternative Surface Treatment Methods of Non-Alloy Steels: Review
Authors: Kapustynskyi Oleksandr , Višniakov Nikolaj
Published: February 2025

This paper focuses on the microstructural characteristics of non-alloy structural steels with carbon contents below 0.3% (further—Low-Carbon Steel—LCS), as well as the possible structural transformations and the resultant mechanical properties attainable through conventional heat treatment or alternative surface treatment methods. The principal microstructural constituents that govern the properties of these steels include both equilibrium and non-equilibrium phases, such as martensite, retained austenite, sorbite, and troostite. Conventional methodologies for enhancing rigidity involve the implementation of supplementary stiffening ribs, which augment rigidity while concomitantly contributing to an increase in overall weight or dimensions of the structure. In structures where supplementary stiffening ribs are incorporated within the thin-walled steel shell, this may reduce manufacturing efficiency and simplicity of design. Modern laser treatment technologies for thin-walled steel structures, however, involve modifying the internal microstructure and creating rigidity ribs within the structure itself, thus circumventing the need for additional elements.
Design of an Aperiodic Optical Phased Array Based on the Multi-Strategy Enhanced Particle Swarm Optimization Algorithm
Authors: Zang Zhuangzhuang , Wu Junjie , Huang Qingzhong
Published: February 2025

We have proposed a multi-strategy enhanced particle swarm optimization (PSO) algorithm to optimize the antenna spacing distribution of an optical phased array (OPA). The global search capability is improved by incorporating circle chaotic mapping initialization and an updated strategy based on adaptive inertia weights and dynamic learning factors. We used the peak side-lobe level (PSLL) at different steering angles as the fitness function, which effectively suppresses the rapid degradation of PSLL during scanning. Based on this approach, 32- and 64-channel aperiodic OPAs were designed with a scanning range of ±60°, with improvements of the PSLL of 1.94 and 2.05 dB at 60°, respectively. In addition, the analytical and numerical simulation results are in good agreement. We also analyzed the influence of spacing deviations on PSLL and found that the obtained OPAs exhibit sufficient robustness.
Granulometry and Functional Properties of Yuca Flour (Yucca decipiens Trel.) for Food Purposes
Authors: Martínez-Betancourt Selena R. , Cadena-Iñiguez Jorge , Loera-Alvarado Gerardo , Ruiz-Vera Víctor M. , Soto-Hernández Ramón Marcos , López-Padilla Concepción , García-Flores Dalia Abigail
Published: February 2025

Mexican yuca (Yucca decipiens Trel.) is native to the semi-desert region of north-central Mexico. Based on its medicinal uses, the flour produced from its leaves and stems was evaluated to determine new food uses. The flour was characterized based on granulometry, rheology, texture and functional properties, which were analyzed with the RStudio software. The results indicate that the Water Absorption Index (WAI) of yuca flour (0.11 mL g⁻1) is similar to that of wheat flour (0.56 mL g⁻1). However, the Fat Absorption Index (FAI) of yuca flour (0.40 mL g⁻1) is significantly lower than that of Saltillo Pinto bean flour (1.55 mL g⁻1). This suggests that yuca exhibits hydrophilic behavior comparable to that of wheat flour and requires less oil in potential formulations. The expansion capacity of yuca flour is similar to that of wheat flour, demonstrating a gluten-like behavior ideal for food applications that require this structural component. The flour also exhibited notable foaming properties, high stability and low fat content, highlighting its food potential. Fermentation matched the parameters of the Cereal & Grains Association’s physicochemical test methods 56–60; consequently, yuca flours are classified as the same as those produced from soft, weak wheat, supporting their use for fermentation processes. Internal friction values (0.85–0.92) suggest limited flow; however, its high density shows fine granulometry that facilitates the bagging, handling and storage of the flour, complying with the Mexican standards.
Additive Manufacturing via Direct Ink Writing of Customized Silicone Foam with Glycerol as Dispersed Phase for Diverse Applications
Authors: Tie Kenrick Weiting , Sim Jia Huey , Tey Jing Yuen , Yeo Wei Hong , Lee Zhi Hua , Ng Law Yong , Bee Soo Tueen , Lee Tin Sin , Abdullah Luqman Chuah
Published: February 2025

Direct ink writing (DIW) enables the additive manufacturing of silicone elastomers, offering an alternative to traditional moulding and casting methods for applications from healthcare products to machine–human interaction sensors. Polydimethylsiloxane (PDMS) foam, a porous silicone elastomer, is valued for its elasticity, chemical and thermal resistance, hydrophobicity, and biocompatibility. The emulsion templating method is a simple and cost-effective approach to create silicone foams by incorporating and later removing a sacrificial dispersed phase from the PDMS precursor. This study developed glycerol/PDMS emulsion-based inks for DIW using high-shear centrifugal mixing, with ethanol as a solvent to remove the glycerol template, creating silicone foam. An optimal glycerol dosage of 50 parts per hundred rubber (phr) produced foam with 27.63% porosity and pore diameter up to 4.66 µm. Each 10 phr increase in glycerol content raised porosity by 10% and average pore diameter by 2 µm. Both tensile and compressive behaviour inversely correlated with porosity, with a 10% porosity rise in the silicone foam reducing tensile strength by 0.07 MPa and stiffness by 0.02 MPa. Models with strong data alignment were developed to benefit researchers in 3D printing to customize silicone foams (pore properties, mechanical properties, compressive properties) based on specific application requirements.
South China Sea SST Fronts, 2015–2022
Authors: Belkin Igor M. , Zang Yi-Tao
Published: February 2025

High-resolution (2 km), high-frequency (hourly) SST data of the Advanced Himawari Imager (AHI) flown onboard the Japanese Himawari-8 geostationary satellite were used to derive the monthly climatology of temperature fronts in the South China Sea. The SST data from 2015 to 2022 were processed with the Belkin–O’Reilly algorithm to generate maps of SST gradient magnitude GM. The GM maps were log-transformed to enhance contrasts in digital maps and reveal additional features (fronts). The combination of high-resolution, cloud-free, four-day-composite SST imagery from AHI, the advanced front-preserving gradient algorithm BOA, and digital contrast enhancement with the log-transformation of SST gradients allowed us to identify numerous mesoscale/submesoscale fronts (including a few fronts that have never been reported) and document their month-to-month variability and spatial patterns. The spatiotemporal variability of SST fronts was analyzed in detail in five regions: (1) In the Taiwan Strait, six fronts were identified: the China Coastal Front, Taiwan Bank Front, Changyun Ridge Front, East Penghu Channel Front, and Eastern/Western Penghu Islands fronts; (2) the Guangdong Shelf is dominated by the China Coastal Front in winter, with the eastern and western Guangdong fronts separated by the Pearl River outflow in summer; (3) Hainan Island is surrounded by upwelling fronts of various nature (wind-driven coastal and topographic) and tidal mixing fronts; in the western Beibu Gulf, the Red River Outflow Front extends southward as the Vietnam Coastal Front, while the northern Beibu Gulf features a tidal mixing front off the Guangxi coast; (4) Off SE Vietnam, the 11°N coastal upwelling gives rise to a summertime front, while the Mekong Outflow and associated front extend seasonally toward Cape Camau, close to the Gulf of Thailand Entrance Front; (5) In the Luzon Strait, the Kuroshio Front manifests as a chain of three fronts across the Babuyan Islands, while west of Luzon Island a broad offshore frontal zone persists in winter. The summertime eastward jet (SEJ) off SE Vietnam is documented from five-day mean SST data. The SEJ emerges in June–September off the 11°N coastal upwelling center and extends up to 114°E. The zonally oriented SEJ is observed to be located between two large gyres, each about 300 km in diameter.
Accuracy Assessment of a Digital Elevation Model Constructed Using the KOMPSAT-5 Dataset
Authors: Lee Je-Yun , Hong Sang-Hoon , Lee Kwang-Jae , Won Joong-Sun
Published: February 2025

The Interferometric Synthetic Aperture Radar (InSAR) has significantly advanced in its usage for analyzing surface information such as displacement or elevation. In this study, we evaluated a digital elevation model (DEM) constructed using X-band KOMPSAT-5 interferometric datasets provided by the Korea Aerospace Research Institute (KARI). The 28-day revisit cycle of KOMPSAT-5 poses challenges in maintaining interferometric correlation. To address this, four KOMPSAT-5 images were employed in a multi-baseline interferometric approach to mitigate temporal decorrelation effects. Despite the slightly longer temporal baselines, the analysis revealed sufficient coherence (>0.8) in three interferograms. The height of ambiguity ranged from 59 to 74 m, which is a moderate height of sensitivity to extract topography over the study area of San Francisco in the USA. Unfortunately, only ascending acquisition mode datasets were available for this study. The derived DEM was validated against three reference datasets: Copernicus GLO-30 DEM, ICESat-2, and GEDI altimetry. A high coefficient of determination (R2 > 0.9) demonstrates the feasibility of the interferometric application of KOMPSAT-5.
KT-Deblur: Kolmogorov–Arnold and Transformer Networks for Remote Sensing Image Deblurring
Authors: Zhu Baoyu , Li Zekun , Lv Qunbo , Tan Zheng , Zhang Kai
Published: February 2025

Aiming to address the fundamental limitation of fixed activation functions that constrain network expressiveness in existing deep deblurring models, in this pioneering study, we introduced Kolmogorov–Arnold Networks (KANs) into the field of full-color/RGB image deblurring, proposing the Kolmogorov–Arnold and Transformer Network (KT-Deblur) framework based on dynamically learnable activation functions. This framework overcomes the constraints of traditional networks’ fixed nonlinear transformations by employing adaptive activation regulation for different blur types through KANs’ differentiable basis functions. Integrated with a U-Net architecture within a generative adversarial network framework, it significantly enhances detail restoration capabilities in complex scenarios. The innovatively designed Unified Attention Feature Extraction (UAFE) module combines neighborhood self-attention with linear self-attention mechanisms, achieving synergistic optimization of noise suppression and detail enhancement through adaptive feature space weighting. Supported by the Fast Spatial Feature Module (FSFM), it effectively improves the model’s ability to handle complex blur patterns. Our experimental results demonstrate that the proposed method outperforms existing algorithms in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics across multiple standard datasets, achieving an average PSNR of 41.25 dB on the RealBlur-R dataset, surpassing the latest state-of-the-art (SOTA) algorithms. This model exhibits strong robustness, providing a new paradigm for image-deblurring network design.
Improved Method for the Retrieval of Extinction Coefficient Profile by Regularization Techniques
Authors: Herrmann Richard Matthias , Ritter Christoph , Böckmann Christine , Graßl Sandra
Published: February 2025

In this work, we revise the retrieval of extinction coefficient profiles from Raman Lidar. This is an ill-posed problem, and we show that methods like Levenberg–Marquardt or Tikhonov–Phillips can be applied. We test these methods for a synthetic Lidar profile (known solution) with different noise realizations. Further, we apply these methods to three different cases of data from the Arctic: under daylight (Arctic Haze), under daylight with a high and vertically extended aerosol layer, and at nighttime with high extinction. We show that our methods work and allow a trustful derivation of extinction up to clearly higher altitudes (at about half a signal-to-noise ratio) compared with the traditional, non-regularized Ansmann solution. However, these new methods are not trivial and require a choice of parameters, which depend on the noise of the data. As the Lidar signal quality quickly decreases with range, a separation of the profile into several sub-intervals seems beneficial.
Deep Learning Based Pile-Up Correction Algorithm for Spectrometric Data Under High-Count-Rate Measurements
Authors: Huang Yiwei , Zheng Xiaoying , Zhu Yongxin , Trigano Tom , Bykhovsky Dima , Chen Zikang
Published: February 2025

Gamma-ray spectroscopy is essential in nuclear science, enabling the identification of radioactive materials through energy spectrum analysis. However, high count rates lead to pile-up effects, resulting in spectral distortions that hinder accurate isotope identification and activity estimation. This phenomenon highlights the need for automated and precise approaches to pile-up correction. We propose a novel deep learning (DL) framework plugging count rate information of pile-up signals with a 2D attention U-Net for energy spectrum recovery. The input to the model is an Energy–Duration matrix constructed from preprocessed pulse signals. Temporal and spatial features are jointly extracted, with count rate information embedded to enhance robustness under high count rate conditions. Training data were generated using an open-source simulator based on a public gamma spectrum database. The model’s performance was evaluated using Kullback–Leibler (KL) divergence, Mean Squared Error (MSE) Energy Resolution (ER), and Full Width at Half Maximum (FWHM). Results indicate that the proposed framework effectively predicts accurate spectra, minimizing errors even under severe pile-up effects. This work provides a robust framework for addressing pile-up effects in gamma-ray spectroscopy, presenting a practical solution for automated, high-accuracy spectrum estimation. The integration of temporal and spatial learning techniques offers promising prospects for advancing high-activity nuclear analysis applications.
EEG-Based Music Emotion Prediction Using Supervised Feature Extraction for MIDI Generation
Authors: Gomez-Morales Oscar , Perez-Nastar Hernan , Álvarez-Meza Andrés Marino , Torres-Cardona Héctor , Castellanos-Dominguez Germán
Published: February 2025

Advancements in music emotion prediction are driving AI-driven algorithmic composition, enabling the generation of complex melodies. However, bridging neural and auditory domains remains challenging due to the semantic gap between brain-derived low-level features and high-level musical concepts, making alignment computationally demanding. This study proposes a deep learning framework for generating MIDI sequences aligned with labeled emotion predictions through supervised feature extraction from neural and auditory domains. EEGNet is employed to process neural data, while an autoencoder-based piano algorithm handles auditory data. To address modality heterogeneity, Centered Kernel Alignment is incorporated to enhance the separation of emotional states. Furthermore, regression between feature domains is applied to reduce intra-subject variability in extracted Electroencephalography (EEG) patterns, followed by the clustering of latent auditory representations into denser partitions to improve MIDI reconstruction quality. Using musical metrics, evaluation on real-world data shows that the proposed approach improves emotion classification (namely, between arousal and valence) and the system’s ability to produce MIDI sequences that better preserve temporal alignment, tonal consistency, and structural integrity. Subject-specific analysis reveals that subjects with stronger imagery paradigms produced higher-quality MIDI outputs, as their neural patterns aligned more closely with the training data. In contrast, subjects with weaker performance exhibited auditory data that were less consistent.
Application of Multiple Deep Learning Architectures for Emotion Classification Based on Facial Expressions
Authors: Qian Cheng , Lobo Marques João Alexandre , de Alexandria Auzuir Ripardo , Fong Simon James
Published: February 2025

Facial expression recognition (FER) is essential for discerning human emotions and is applied extensively in big data analytics, healthcare, security, and user experience enhancement. This study presents a comprehensive evaluation of ten state-of-the-art deep learning models—VGG16, VGG19, ResNet50, ResNet101, DenseNet, GoogLeNet V1, MobileNet V1, EfficientNet V2, ShuffleNet V2, and RepVGG—on the task of facial expression recognition using the FER2013 dataset. Key performance metrics, including test accuracy, training time, and weight file size, were analyzed to assess the learning efficiency, generalization capabilities, and architectural innovations of each model. EfficientNet V2 and ResNet50 emerged as top performers, achieving high accuracy and stable convergence using compound scaling and residual connections, enabling them to capture complex emotional features with minimal overfitting. DenseNet, GoogLeNet V1, and RepVGG also demonstrated strong performance, leveraging dense connectivity, inception modules, and re-parameterization techniques, though they exhibited slower initial convergence. In contrast, lightweight models such as MobileNet V1 and ShuffleNet V2, while excelling in computational efficiency, faced limitations in accuracy, particularly in challenging emotion categories like “fear” and “disgust”. The results highlight the critical trade-offs between computational efficiency and predictive accuracy, emphasizing the importance of selecting appropriate architecture based on application-specific requirements. This research contributes to ongoing advancements in deep learning, particularly in domains such as facial expression recognition, where capturing subtle and complex patterns is essential for high-performance outcomes.
Automatic Quantification of Atmospheric Turbulence Intensity in Space-Time Domain
Authors: Gulich Damián , Tebaldi Myrian , Sierra-Sosa Daniel
Published: February 2025

Quantifying atmospheric turbulence intensity is a challenging task, particularly when assessing real-world scenarios. In this paper, we propose a deep learning method for quantifying atmospheric turbulence intensity based on the space-time domain analysis from videos depicting different turbulence levels. We capture videos of a static image under controlled air turbulence intensities using an inexpensive camera, and then, by slicing these videos in the space-time domain, we extract spatio-temporal representations of the turbulence dynamics. These representations are then fed into a Convolutional Neural Network for classification. This network effectively learns to discriminate between different turbulence regimes based on the spatio-temporal features extracted from a real-world experiment captured in video slices.
Evaluation of In-Ear and Fingertip-Based Photoplethysmography Sensors for Measuring Cardiac Vagal Tone Relevant Heart Rate Variability Parameters
Authors: Parikh Ankit , Lewis Gwyn , GholamHosseini Hamid , Rashid Usman , Rice David , Almesfer Faisal
Published: February 2025

This paper presents a study undertaken to evaluate the sensor systems that were shortlisted to be used in the development of a portable respiratory-gated transcutaneous auricular vagus nerve stimulation (taVNS) system. To date, all published studies assessing respiratory-gated taVNS have been performed in controlled laboratory environments. This limitation arises from the reliance on non-portable sensing equipment, which poses significant logistical challenges. Therefore, we recognised a need to develop a portable sensor system for future research, enabling participants to perform respiratory-gated stimulation conveniently from their homes. This study aimed to measure the accuracy of an in-ear and a fingertip-based photoplethysmography (PPG) sensor in measuring cardiac vagal tone relevant heart rate variability (HRV) parameters of root mean square of successive R-R interval differences (RMSSDs) and the high-frequency (HF) component of HRV. Thirty healthy participants wore the prototype sensor equipment and the gold standard electrocardiogram (ECG) equipment to record beat-to-beat intervals simultaneously during 10 min of normal breathing and 10 min of deep slow breathing (DSB). Additionally, a stretch sensor was evaluated to measure its accuracy in detecting exhalation when compared to the gold standard sensor. We used Bland–Altman analysis to establish the agreement between the prototypes and the ECG system. Intraclass correlation coefficients (ICCs) were calculated to establish consistency between the prototypes and the ECG system. For the stretch sensor, the true positive rate (TPR), false positive rate (FPR), and false negative rate (FNR) were calculated. Results indicate that while ICC values were generally good to excellent, only the fingertip-based sensor had an acceptable level of agreement in measuring RMSSDs during both breathing phases. Only the fingertip-based sensor had an acceptable level of agreement during normal breathing in measuring HF-HRV. The study highlights that a high correlation between sensors does not necessarily translate into a high level of agreement. In the case of the stretch sensor, it had an acceptable level of accuracy with a mean TPR of 85% during normal breathing and 95% during DSB. The results show that the fingertip-based sensor and the stretch sensor had acceptable levels of accuracy for use in the development of the respiratory-gated taVNS system.
Dynamic Edge Loading Balancing with Edge Node Activity Prediction and Accelerating the Model Convergence
Authors: Chen Wen , Liu Sibin , Yang Yuxiao , Hu Wenjing , Yu Jinming
Published: February 2025

In mobile edge computing networks, achieving effective load balancing across edge server nodes is essential for minimizing task processing latency. However, the lack of a priori knowledge regarding the current load state of edge nodes for user devices presents a significant challenge in multi-user, multi-edge node scenarios. This challenge is exacerbated by the inherent dynamics and uncertainty of edge node load variations. To tackle these issues, we propose a deep reinforcement learning-based approach for task offloading and resource allocation, aiming to balance the load on edge nodes while reducing the long-term average cost. Specifically, we decompose the optimization problem into two subproblems, task offloading and resource allocation. The Karush–Kuhn–Tucker (KKT) conditions are employed to derive the optimal strategy for communication bandwidth and computational resource allocation for edge nodes. We utilize Long Short-Term Memory (LSTM) networks to forecast the real-time activity of edge nodes. Additionally, we integrate deep compression techniques to expedite model convergence, facilitating faster execution on user devices. Our simulation results demonstrate that our proposed scheme achieves a 47% reduction in terms of the task drop rate, a 14% decrease in the total system cost, and a 7.6% improvement in the runtime compared to the baseline schemes.
Audible Noise Evaluation in Wind Turbines Through Artificial Intelligence Techniques
Authors: Ferreira da Silva Mathaus , Nunes Masson Juliano Emir , Santos Murillo Ferreira dos , Rodrigues Silva William , Wladimir Molina Iuri , Martins Gabriel Miguel Castro
Published: February 2025

In recent years, wind power has become a more attractive alternative energy source for overcoming environmental issues. Predictive maintenance is essential for wind power devices to ensure that these systems work reliably and with sufficient availability. This paper presents a method to work around failure detection in wind turbines using the sound emitted from their components. The proposed Artificial Intelligence (AI) model is based on unsupervised learning and image processing, through which the machine learning model learns to identify spectrograms from wind turbines under healthy conditions. The reconstruction of current data determines whether the input data have an uncommon noise, which translates into a possibility of failure or an effective one. The uncommon data are sent to a specialist network, which, through supervising learning, identifies a failure event and alerts operators to possible problems that the wind turbine could pass through, helping with preventive maintenance. The model offered satisfactory results in five tested wind turbines, in which some specific faults known by the operators were captured through the low similarity between the reconstructed data and the input. Additionally, this application could be extended to similar applications in industrial machinery within the scope of audible noises in rotative machine mechanisms.
Liquid Metal-Based Frequency and Pattern Reconfigurable Yagi Antenna for Pressure Sensing
Authors: Yang Xiaofeng , Ma Xiang , Yang Jiayi , Li Yang , Peng Meiping , Zheng Qi
Published: February 2025

In this work, a frequency- and pattern-reconfigurable Yagi antenna based on liquid metal (LM) switches is proposed for pressure sensing and health monitoring. The proposed antenna consists of a dipole radiator, a reflector, a director, a dielectric substrate, and four flexible LM switches. Benefitted from the switching effect of the LM switches under external pressure, the frequency and radiation pattern of the antenna can be reconfigured. When the LM switch is fully or partially turned on, the radiation directions of the antenna are bidirectionally end-shot and end-fired, respectively. The operating frequency of the antenna can be tuned from 2.28 GHz to 2.5 GHz. It is shown that a maximum gain of 6 dBi can be obtained. A sample was fabricated and measured, and the experimental results were in good agreement with the simulations. The reconfigurable antenna can be applied in wireless pressure-sensing and health-monitoring systems.
A Study of Null Broadening Algorithms for Navigation Receivers in Highly Dynamic Scenarios
Authors: Ji Yuanfa , He Tao , Chen Yu , Wen Chenggan , Sun Xiyan
Published: February 2025

Due to the narrow nulls formed by the Power Inversion (PI) algorithm, it fails to suppress jamming signals in highly dynamic scenarios effectively. This paper proposes a null broadening algorithm based on eigenvalue sorting. Unlike other algorithms, this one does not require prior knowledge of the direction of jamming. It is based on the Covariance Matrix Taper (CMT) algorithm, which orders the eigenvalues of the sampling covariance matrix. The sample covariance matrix’s eigenvalues are sorted to provide new sample data, and the rebuilt covariance matrix is then averaged forward and backward. The experimental results demonstrate that the proposed algorithm can effectively broaden the null. Compared with the CMT algorithm, the null in the jamming direction is, on average, approximately 22 dB deeper under the experimental conditions, and the gain in the direction of the sound signal is increased by around 15 dB. Moreover, the signal can be successfully acquired even when the input jamming-to-signal ratio (ISR) is relatively low. When there is a deviation in the jamming direction, the proposed algorithm demonstrates robust null broadening performance even with a small number of snapshots. The output SINR of the proposed algorithm exhibits a nearly linear relationship with the input SNR.
A First Verification of Sim2DSphere Model’s Ability to Predict the Spatiotemporal Variability of Parameters Characterizing Land Surface Interactions at Diverse European Ecosystems
Authors: Lekka Christina , Petropoulos George P. , Detsikas Spyridon E.
Published: February 2025

Land–atmosphere interactions (LSIs) involve intricate complex processes that drive critical exchanges of energy and matter that influence ecosystem and climate dynamics, with variations in ecosystem responses and feedback effects depending on their specific environmental characteristics. To this end, this study represents the first extensive validation of the Sim2DSphere model, to establish its coherence and use in the study of LSIs across a range of biomes and climatic zones. For this purpose, water and energy fluxes from a total of 10 eddy covariance sites and a total of 12 calendar days were analyzed. Earth observation (EO) data were integrated with ground observations at the different sites to execute the Sim2DSphere model. The diurnal dynamics of energy fluxes were compared against corresponding ground measurements. The results showed that the highest accuracy was observed for the grassland sites (R2 from >0.85; RMSE < 68.50 W m−2), whereas the lowest accuracy was found in forest sites (R2 from >0.80; RMSE < 75.0 W m−2). All in all, these initial results obtained herein are very promising and demonstrate the models’ promising potential in the study of LSIs at variant spatiotemporal resolutions.
DQRNet: Dynamic Quality Refinement Network for 3D Reconstruction from a Single Depth View
Authors: Liu Caixia , Zhu Minhong , Li Haisheng , Wei Xiulan , Liang Jiulin , Yao Qianwen
Published: February 2025

With the widespread adoption of 3D scanning technology, depth view-driven 3D reconstruction has become crucial for applications such as SLAM, virtual reality, and autonomous vehicles. However, due to the effects of self-occlusion and environmental occlusion, obtaining complete and error-free 3D shapes directly from 3D scans remains challenging, as previous reconstruction methods tend to lose details. To this end, we propose Dynamic Quality Refinement Network (DQRNet) for reconstructing complete and accurate 3D shape from a single depth view. DQRNet introduces a dynamic encoder–decoder and a detail quality refiner to generate high-resolution 3D shapes, where the former designs a dynamic latent extractor to adaptively select important parts of an object and the latter designs global and local point refiners to enhance the reconstruction quality. Experimental results show that DQRNet is able to focus on capturing the details at boundaries and key areas on ShapeNet dataset, thereby achieving better accuracy and robustness than SOTA methods.