bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of Ecosystem Service Value Trends and Drivers in the Yellow River Delta, China
Authors: Xu Qian , Zhang Zhiyi , Liu Xin , Wang Zihan , Ren Chen , Xia Tanlong , Sun Guangwei , Han Liusheng
Published: February 2025

Ecosystem service value (ESV) reflects ecosystem functions and benefits; however, the factors influencing ESV and the mechanisms driving it in wetlands and non-wetlands are not yet fully understood. The Yellow River Delta (YRD) is distinguished by the presence of numerous wetland areas that are both Reserve and non-Reserve and thus it was selected as the designated study area. In this study, the spatiotemporal structures of ESV in the YRD between 2000 and 2020 were studied using land cover change analysis and the equivalent factor methodology. In addition, we analyzed the drivers behind the geographical variability in ESV by applying the Geographical Detector method. The results showed that the land structure of the YRD National Nature Reserve was relatively stable, whereas the non-Reserve area exhibited greater fluctuations; that is, wetlands in the YRD non-Reserve area decreased by 11.43% compared with the more stable land structure in Reserve areas, where wetland decreased by 4.93%. Furthermore, disparities in the distribution of land use types gave rise to a discernible spatial distribution of overall ESV, with the northeast exhibiting significantly higher ESV levels compared to the southwest. Additionally, in the past two decades, the center of gravity of the ESV in both regions has shifted towards urban centers, and wetlands have migrated towards the coastline. The Normalized Difference Vegetation Index was identified as the main driver of ESV heterogeneity. The findings of this study are highly relevant to regional ecological conservation and the promotion of economic and social development.
A Multi-Scenario Simulation and Driving Factor Analysis of Production–Living–Ecological Land in China’s Main Grain Producing Areas: A Case Study of the Huaihe River Eco-Economic Belt
Authors: Hu Wenfeng , Cheng Junhao , Zheng Mengtian , Jin Xiaolong , Yao Junqiang , Guo Fei
Published: February 2025

The Huaihe River Eco-Economic Belt (HREEB) is a pivotal region in China’s national strategic planning. Land use in this region is crucial to improving ecological quality and ensuring food security. Using the PLUS model and Geodetector, we evaluated the contribution and interaction of 10 drivers to production–living–ecological land (PLEL) and performed multi-scenario simulations of PLEL. The results show that the following: (1) Ecological land is mainly affected by elevation (contribution value > 0.16 for forest, grassland, and water). Production land is influenced by topographic relief, elevation, and GDP per capita (contribution value > 0.13). Living land is driven by topographic relief, GDP per capita, elevation, and population density (contribution value > 0.13). Interaction analysis shows nonlinear or two-factor enhancement among factors. (2) Ecological land in the HREEB has a relatively stable spatial pattern. Simulation results indicate that ecological land will not exceed a 12% change in the next 50 years. (3) By 2075, simulations under various scenarios predict significant changes in land area. Compared with the natural development scenario, production land increased by 14.8% in the farmland protection scenario, and living land increased by 14.3% in the urban development scenario. This research is vital for managing and developing PLEL resources within the HREEB.
Integration of UAV Multispectral Remote Sensing and Random Forest for Full-Growth Stage Monitoring of Wheat Dynamics
Authors: Zhang Donghui , Qi Hao , Guo Xiaorui , Sun Haifang , Min Jianan , Li Si , Hou Liang , Lv Liangjie
Published: February 2025

Wheat is a key staple crop globally, essential for food security and sustainable agricultural development. The results of this study highlight how innovative monitoring techniques, such as UAV-based multispectral imaging, can significantly improve agricultural practices by providing precise, real-time data on crop growth. This study utilized unmanned aerial vehicle (UAV)-based remote sensing technology at the wheat experimental field of the Hebei Academy of Agriculture and Forestry Sciences to capture the dynamic growth characteristics of wheat using multispectral data, aiming to explore efficient and precise monitoring and management strategies for wheat. A UAV equipped with multispectral sensors was employed to collect high-resolution imagery at five critical growth stages of wheat: tillering, jointing, booting, flowering, and ripening. The data covered four key spectral bands: green (560 nm), red (650 nm), red-edge (730 nm), and near-infrared (840 nm). Combined with ground-truth measurements, such as chlorophyll content and plant height, 21 vegetation indices were analyzed for their nonlinear relationships with wheat growth parameters. Statistical analyses, including Pearson’s correlation and stepwise regression, were used to identify the most effective indices for monitoring wheat growth. The Normalized Difference Red-Edge Index (NDRE) and the Triangular Vegetation Index (TVI) were selected based on their superior performance in predicting wheat growth parameters, as demonstrated by their high correlation coefficients and predictive accuracy. A random forest model was developed to comprehensively evaluate the application potential of multispectral data in wheat growth monitoring. The results demonstrated that the NDRE and TVI indices were the most effective indices for monitoring wheat growth. The random forest model exhibited superior predictive accuracy, with a mean squared error (MSE) significantly lower than that of traditional regression models, particularly during the flowering and ripening stages, where the prediction error for plant height was less than 1.01 cm. Furthermore, dynamic analyses of UAV imagery effectively identified abnormal field areas, such as regions experiencing water stress or disease, providing a scientific basis for precision agricultural interventions. This study highlights the potential of UAV-based remote sensing technology in monitoring wheat growth, addressing the research gap in systematic full-cycle analysis of wheat. It also offers a novel technological pathway for optimizing agricultural resource management and improving crop yields. These findings are expected to advance intelligent agricultural production and accelerate the implementation of precision agriculture.
Effects of High-Voltage Discharge Plasma on Drying Properties, Microstructure, and Nutrients of Oat Grass
Authors: Wang Xixuan , Liu Zhidan , Zhao Peishuo , Song Zhiqing
Published: February 2025

In this study, the drying properties of new-mown oat grass were investigated using three methods: high-voltage discharge plasma drying (HVDP), hot-air drying (HAD), and natural air drying (AD). The HVDP process mainly generates discharge plasma between needle electrodes and a dielectric plate by changing the discharge voltage. HVDP, which is a new type of non-thermal drying technology, uses the energy exchange associated with the action of plasma and the non-uniform electric field force to accelerate the evaporation of water. The results show that HVDP has obvious advantages in terms of the drying rate and drying time, as well as reducing energy consumption while retaining nutrients. In particular, under the condition of 35 kV, HVDP not only effectively shortened the drying time and reduced the energy consumption but also selectively degraded the nutrient-reducing substances (e.g., lignin) and retained the substances positively correlated with the nutrient quality, significantly improving the nutrient content of the treated oat grass. In conclusion, as an innovative non-thermal drying technology, HVDP has great potential to improve the drying efficiency and reduce nutrient degradation in oat grass, providing an innovative solution to improve its quality and utilisation.
Research on Apple Recognition and Localization Method Based on Deep Learning
Authors: Zhao Zhipeng , Yin Chengkai , Guo Ziliang , Zhang Jian , Chen Qing , Gu Ziyuan
Published: February 2025

The development of robotic systems for apple picking is indeed a crucial advancement in agricultural technology, particularly in light of the ongoing labor shortages and the continuous evolution of automation technologies. Currently, during apple picking in complex environments, it is difficult to classify and identify the growth pattern of an apple and obtain information on its attitude synchronously. In this paper, through the integration of deep learning and stereo vision technology, the growth pattern and attitude of apples in the natural environment are identified, and three-dimensional spatial positioning is realized. This study proposes a fusion recognition method based on improved YOLOv7 for apple growth morphology classification and fruit position. Firstly, the multi-scale feature fusion network is improved by adding a 160 × 160 feature scale layer in the backbone network, which is used to enhance the model’s sensitivity in the recognition of very small local features. Secondly, the CBAM attention mechanism is introduced to improve the network’s attention to the target region of interest of the input image. Finally, the Soft-NMS algorithm is adopted, which can effectively prevent high-density overlapping targets from being suppressed at one time and thus prevent the occurrence of missed detection. In addition, the UNet segmentation network and the minimum outer circle and rectangle features are combined to obtain the unobstructed apple position. A depth image of the apple is obtained using an RGB-D camera, and the 3D coordinates of the apple picking point are obtained by combining the 2D coordinates in the RGB image with the depth value. The experimental results show that the recognition accuracy, recall and average recognition precision of the improved YOLOv7 are 86.9%, 80.5% and 87.1%, respectively, which are 4.2, 2.2 and 3.7 percentage points higher compared to the original YOLOv7 model; in addition, the average angular error of the apple position detection method is 3.964°, with an accuracy of 94%, and the error in the three-dimensional coordinate positioning of the apple is within the range of 0.01 mm–1.53 mm, which can meet the demands of apple-picking system operation. The deep-learning-based stereo vision system constructed herein for apple picking robots can effectively identify and locate apples and meet the vision system requirements for the automated picking task performed by an apple-picking robot, with a view to laying the foundation for lossless and efficient apple picking.
Self-Administration of Meloxicam via Medicated Molasses Lick Blocks May Improve Welfare of Castrated Calves
Authors: Rudd Samantha , Lomax Sabrina , White Peter J. , Van der Saag Dominique
Published: February 2025

The self-administration of meloxicam via medicated feed is a novel concept that could enable non-invasive, pre-emptive and long-term analgesia. Forty Bos taurus male calves were randomly allocated to four treatment groups: no castration (PC), surgical castration (NC), surgical castration with subcutaneous meloxicam (M), and surgical castration with medicated lick blocks (ML). Data collection occurred at various timepoints over 13 days following treatment. Plasma concentration of meloxicam was greater in ML compared to M calves at all timepoints except day 1 (p < 0.001); however, variation between ML individuals was high (standard deviation = 1.68). There was no effect of treatment on scrotal diameter or scrotal temperature. Wound morphology scores were improved in ML compared to NC calves. Eating observations were greater in ML calves immediately post-castration, followed by M calves at 3 h post-castration (p < 0.001). ML calves were observed locomoting more (p = 0.0032) and lying less (p < 0.001) than PC calves. These findings indicate that meloxicam-medicated lick blocks may provide a practical option for a longer duration of pain mitigation for surgically castrated calves. Conclusions are limited by the complexities of assessing pain in cattle, and further research into the toxicity effects of continued administration of meloxicam is recommended.
Factors Modulating the Occurrence of the Selective-Value Effect in Tufted Capuchin Monkeys (Sapajus spp.)
Authors: D’Onofrio Alessandra , Gastaldi Serena , Addessi Elsa
Published: February 2025

During foraging decisions, animals often make irrational choices. The selective-value effect refers to the lack of preference for an option consisting of one highly preferred item plus one less preferred item compared to a single highly preferred item. A similar bias is the less-is-better effect, in which individuals prefer a single highly preferred item rather than an option that includes both a highly preferred item and a less preferred one. Here, we investigated the occurrence of these decisional biases in twelve tufted capuchin monkeys (Sapajus spp.) by exploring, for the first time in this genus, both the role of the relative value of the highly vs. less preferred food and the length of the intertrial interval. Overall, capuchins made rational choices by preferring the mixed option over the single one. However, on an individual level, some capuchins exhibited the selective-value effect. Mirroring previous findings in apes, both the relative value of the highly vs. less preferred food and the length of the intertrial interval modulated capuchins’ preference for the mixed option. This study provides further insight into the factors influencing violations of rationality in primate food choices, suggesting that these decisions might reflect cognitive adaptations for optimizing sensory feedback and nutrient intake rather than being irrational.
Skin Appendage Proteins of Tetrapods: Building Blocks of Claws, Feathers, Hair and Other Cornified Epithelial Structures
Authors: Holthaus Karin Brigit , Steinbinder Julia , Sachslehner Attila Placido , Eckhart Leopold
Published: February 2025

Reptiles, birds, mammals and amphibians, together forming the clade tetrapods, have a large diversity of cornified skin appendages, such as scales, feathers, hair and claws. The skin appendages consist of dead epithelial cells that are tightly packed with specific structural proteins. Here, we review the molecular diversity and expression patterns of major types of skin appendage proteins, namely keratin intermediate filament proteins, keratin-associated proteins (KRTAPs) and proteins encoded by genes of the epidermal differentiation complex (EDC), including corneous beta-proteins, also known as beta-keratins. We summarize the current knowledge about the components of skin appendages with a focus on keratins and EDC proteins that have recently been identified in reptiles and birds. We discuss gaps of knowledge and suggest directions of future research.
Clinical Scaleup of Humanized AnnA1 Antibody Yielded Unexpected High Reticuloendothelial (RES) Uptake in Mice
Authors: Xu Lu Lucy , Singh Satyendra Kumar , Nayback Chelsea , Metebi Abdullah , Agnew Dalen , Buss Tim , Schnitzer Jan , Zinn Kurt R.
Published: February 2025

Background/Objectives: A mouse antibody directed against truncated Annexin A1 showed high tumor retention in pre-clinical cancer models and was approved by the National Cancer Institute Experimental Therapeutics (NExT) program for humanization and large batch cGMP production for toxicology and clinical trials. In this process, a contractor for Leidos accidentally produced a mutated version of humanized AnnA1 (hAnnA1-mut) with a single nucleotide deletion in the terminal Fc coding region that increased the translated size by eight amino acids with random alterations in the final twenty-four amino acids. We investigated the tissue distribution of hAnnA1-mut, hAnnA1, mAnnA1, and isotope-matched human IgG1 under various injection and conjugation conditions with C57BL/6, FVB, and BALB/c nude mice strains. Methods: Biodistribution studies were performed 24 h after injection of Tc-99m-HYNIC radiolabeled antibodies (purity > 98%). Non-reducing gel electrophoresis studies were conducted with IR680 labeled antibodies incubated with various mouse sera. Results: Our results showed that Tc-99m-HYNIC-hAnnA1 had low spleen and liver retention not statistically different from Tc-99m-HYNIC-IgG1 and Tc-99m-HYNIC-mAnnA1, with corresponding higher blood levels; however, Tc-99m-HYNIC-hAnnA1-mut had high levels in the spleen and liver with differences identified among the mouse strains, radiolabeling conditions, and injection routes. Histopathology showed no morphological change in the liver or spleen from any conditions. Gel electrophoresis showed an upward shift of hAnnA1-mut, consistent with the binding of blood serum protein. Conclusions: The changes in the Fc region of hAnnA1-mut led to higher liver and spleen uptake, suggesting the antibody’s recognition by the innate immune system (likely complement protein binding) and subsequent clearance. Future clinical translation using hAnnA1 and other antibodies needs to limit protein modifications that could drastically reduce blood clearance.
Osteosarcoma Cells and Undifferentiated Human Mesenchymal Stromal Cells Are More Susceptible to Ferroptosis than Differentiated Human Mesenchymal Stromal Cells
Authors: Smirnova Yuliya D. , Hanetseder Dominik , Derigo Lukas , Gasser Andreas Sebastian , Vaglio-Garro Annette , Sperger Simon , Brunauer Regina , Korneeva Olga S. , Duvigneau Johanna Catharina , Marolt Presen Darja , Kozlov Andrey V.
Published: February 2025

Current research suggests that promoting ferroptosis, a non-apoptotic form of cell death, may be an effective therapy for osteosarcoma, while its inhibition could facilitate bone regeneration and prevent osteoporosis. Our objective was to investigate whether the susceptibility to and regulation of ferroptosis differ between undifferentiated (UBC) and differentiated (DBC) human bone marrow stromal cells, as well as human osteosarcoma cells (MG63). Ferroptosis was induced by either inhibiting glutathione peroxidase 4 (GPX4) using RSL3 or blocking all glutathione-dependent enzymes through inhibition of the glutamate/cysteine antiporter with Erastin. Lipid peroxidation was assessed using the fluorescent probe BODIPY™581/591C11, while Ferrostatin-1 was used to inhibit ferroptosis. We demonstrate that neither Erastin nor RSL3 induces ferroptosis in DBC. However, both RSL3 and Erastin induce ferroptosis in UBC, while Erastin predominantly induces ferroptosis in MG63 cells. Our data suggest that ferroptosis induction in undifferentiated hBMSCs is primarily regulated by GPX4, whereas glutathione S-Transferase P1 (GSTP1) plays a key role in controlling ferroptosis in osteosarcoma cells. In conclusion, targeting the key pathways involved in ferroptosis across different bone cell types may improve the efficacy of cancer treatments while minimizing collateral damage and supporting regenerative processes, with minimal impact on cancer therapy.
Total Cost of Ownership Prediction in Chilled Water Plants: Contributing Factors and Role of Artificial Intelligence
Authors: Ulfath Rubaiath E , Pang Toh Yen , Cole Ivan , Stewart Iain , Cheng Chi-Tsun
Published: February 2025

This study investigates key parameters and applications of artificial intelligence (AI) in predicting the total cost of ownership (TCO) for chilled water plants (CWPs). Forecasting the TCO of CWPs is challenging due to the diverse and dynamic factors and parameters that influence it, necessitating understanding their complex correlations and causations. While AI and non-AI approaches have improved parameter prediction accuracy in different engineering applications, comprehensive literature reviews on chiller TCO prediction methodologies and their influencing factors are limited. This systematic review addresses three objectives: (1) to identify the key parameters in estimating TCO of CWPs, (2) to examine the existing techniques employed in TCO forecasting and their benefits in energy and cost savings, and (3) to evaluate how AI enhances TCO prediction accuracy and robustness. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, this review analyzed studies from 2017 to 2024 sourced from the Web of Science and Scopus databases. This study identifies several key parameters influencing TCO, including cooling load, energy consumption, chiller capacity, and the Coefficient of Performance (COP). The review shows that AI-driven models, such as deep learning and machine learning algorithms, have improved the accuracy and robustness of TCO predictions, and it further demonstrates scenarios where AI outperforms conventional prediction and forecasting methods. Notably, the current review shows that AI techniques are predicted to be capable of reducing total life cycle costs by up to 18%, based on modeling estimates.
Cloud-Based Internet-of-Things System for Long-Term Bridge Bearing Monitoring Using Computer Vision
Authors: Kim Gunhee , Shin Junsik , Won Jongbin , Park Jongwoong
Published: February 2025

Bearings play a crucial role in mitigating loads, maintaining stability, and transferring forces between superstructures and substructures. However, bearing failures caused by external factors can compromise structural safety. Therefore, continuous monitoring of bearing displacement is essential, yet current inspection methods are labor-intensive and unsuitable for long-term management. To address this, researchers have proposed systems such as Linear Variable Differential Transformers (LVDTs) and computer vision-based monitoring methods to track bearing displacement over time. However, reliance on external power sources and complex installation processes has limited their widespread application. This paper proposes an automated monitoring system integrating low-power IoT sensors, computer vision, and cloud computing. The system features an event-driven power mechanism to minimize energy consumption and utilizes vision-based displacement measurement techniques, providing both portability and efficiency. Applied in a real-world setting for nine months, the system successfully enabled the long-term monitoring of bridge bearings. The results demonstrate its effectiveness in overcoming traditional limitations and highlight its potential in supporting automated, data-driven assessments of structural stability.
An Enhanced Contrastive Ensemble Learning Method for Anomaly Sound Detection
Authors: Liao Jingneng , Yang Fei , Lu Xiaoqing
Published: February 2025

This paper proposes an enhanced contrastive ensemble learning method for anomaly sound detection. The proposed method achieves approximately 6% in the AUC metric in some categories and achieves state-of-the-art performance among self-supervised models on multiple benchmark datasets. The proposed method is effective in automatically monitoring the operating conditions of the production equipment by detecting the sounds emitted by the machine, to provide an early warning of potential production accidents. This method can significantly reduce industrial monitoring costs and increase monitoring efficiency to improve manufacturing facility productivity effectively. Existing detection methods face challenges with data imbalance caused by the scarcity of anomalous samples, leading to performance degradation. This paper proposes an enhanced data augmentation method that improves model robustness by allowing the data to retain the original features while adding noise close to the real environment through a simple operation. Secondly, model feature extraction is enhanced by using channel attention to fuse time-frequency features. Thirdly, this paper proposes a simple anomaly sample generation method, which can automatically generate real pseudo anomaly samples to help the model gain anomaly detection capability and reduce the impact of data imbalance. Finally, this paper proposes a statistical-based bias compensation that further mitigates the impact of data imbalance by distributing samples through statistical induction. Experimental verification confirms that these changes enhance anomalous sound detection capability.
Using AI-Based Tools to Quantify the Technical Condition of Bridge Structural Components
Authors: Trach Roman , Tyvoniuk Volodymyr , Wierzbicki Tomasz , Trach Yuliia , Kowalski Jan , Szymanek Sylwia , Dzięcioł Justyna , Statnyk Ihor , Podvornyi Andrii
Published: February 2025

The main task of the operation of engineering structures is to ensure the stability of structures with aggressive external influences, which have a complex probabilistic nature. The reliable functioning of bridge structures requires the development and application of modern systems for inspection and assessment of the technical condition of the structure to take timely measures to ensure the safe operation of the structure in changing operating conditions. With the rapid development of AI, modern approaches are increasingly adopted, offering distinct advantages compared to classical methods. The article aims to develop an AI-based model for quantifying the technical condition of bridge structural components based on data obtained from the survey. To achieve this goal, the authors analyzed existing approaches to the inspection and assessment of bridges and studied the experience of using AI in bridge assessment. Based on the Polish Principles of Bridge Technical Condition Assessment, three datasets were formed to quantify the condition of the bridge components made from reinforced concrete: bridge deck, span structures, and piers and abutments. This study created and compared the performance of five AI-based models: XGBoost, Decision Trees, Random Forest, Support Vector Regression, and Artificial Neural Networks (ANNs). The initial comparison revealed relatively low performance across all models, with the ANN model showing a slight advantage. Subsequently, nine ANN models were optimized to achieve higher performance levels. The performance of models was conducted based on a comparison of mean absolute percentage error (MAPE) and R2 metrics. The ANN model with ReLU activation functions for hidden layers and the RMSprop optimizer achieves optimal performance at 100 epochs (MAPE = 3.5%; R2 = 0.994). The practical implementation of such a model can considerably reduce uncertainties stemming from subjective expert judgments and enhance the accuracy of assessments.
Machine Learning-Based Model for Emergency Department Disposition at a Public Hospital
Authors: Sezik Savaş , Cingiz Mustafa Özgür , İbiş Esma
Published: February 2025

With the increasing global demand for artificial intelligence solutions, their role in medicine is also expected to grow as a result of their advantage of easy access to clinical data. Machine learning models, with their ability to process large amounts of data, can help solve clinical issues. The aim of this study was to construct seven machine learning models to predict the outcomes of emergency department patients and compare their prediction performance. Data from 75,803 visits to the emergency department of a public hospital between January 2022 to December 2023 were retrospectively collected. The final dataset incorporated 34 predictors, including two sociodemographic factors, 23 laboratory variables, five initial vital signs, and four emergency department-related variables. They were used to predict the outcomes (mortality, referral, discharge, and hospitalization). During the study period, 316 (0.4%) visits ended in mortality, 5285 (7%) in referral, 13,317 (17%) in hospitalization, and 56,885 (75%) in discharge. The disposition accuracy (sensitivity and specificity) was evaluated using 34 variables for seven machine learning tools according to the area under the curve (AUC). The AUC scores were 0.768, 0.694, 0.829, 0.879, 0.892, 0.923, and 0.958 for Adaboost, logistic regression, K-nearest neighbor, LightGBM, CatBoost, XGBoost, and Random Forest (RF) models, respectively. The machine learning models, especially the discrimination ability of the RF model, were much more reliable in predicting the clinical outcomes in the emergency department. XGBoost and CatBoost ranked second and third, respectively, following RF modeling.
ROLQ-TEE: Revocable and Privacy-Preserving Optimal Location Query Based on Trusted Execution Environment
Authors: Li Bao , Zhou Fucai , Xu Jian , Wang Qiang , Li Jiacheng , Feng Da
Published: February 2025

With the advent of cloud computing, outsourced computing has emerged as an increasingly popular strategy to reduce the burden of local computation. Optimal location query (OLQ) is a computationally intensive task in the domain of big data outsourcing, which is designed to determine the optimal placement of a new facility from a set of candidate locations. However, location data are sensitive and cannot be shared with other enterprises, so privacy-preserving optimal location query becomes particularly important. Although some privacy-preserving works have been proposed, they still suffer from other challenges, such as irrevocable query permissions and high communication overhead. To overcome these challenges, we propose a revocable and privacy-preserving optimal location query scheme based on TEE (Trusted Execution Environment). We employ a basic hash structure within the TEE to compute the intersection data of both parties. We use the concept of reverse nearest neighbor (RNN) to assess the impact of candidates, and then select the optimal facility location. In addition, to implement the revocation of query permissions, we introduce a key refresh strategy that adopts identity and timestamp. We evaluate the performance of the proposed scheme using real datasets, and the experimental results indicate strong practicality.
Patch-Font: Enhancing Few-Shot Font Generation with Patch-Based Attention and Multitask Encoding
Authors: Memon Irfanullah , Hassan Muhammad Ammar Ul , Choi Jaeyoung
Published: February 2025

Few-shot font generation seeks to create high-quality fonts using minimal reference style images, addressing traditional font design’s labor-intensive and time-consuming nature, particularly for languages with large character sets like Chinese and Korean. Existing methods often require multi-stage training or predefined components, which can be time-consuming and limit generalizability. This paper introduces Patch-Font, a novel single-stage method that overcomes the limitations of prior approaches, such as multi-stage training or reliance on predefined components, by integrating a patch-based attention mechanism and a multitask encoder. Patch-Font jointly captures global style elements (e.g., overall font family characteristics) and local style details (e.g., serifs, stroke shapes), ensuring high fidelity to the target style while maintaining computational efficiency. Our approach incorporates triplet margin loss with hard positive/negative mining to disentangle style from content and a style fidelity loss to enhance local style consistency. Experiments on Korean (printed and handwritten) and Chinese fonts demonstrate that Patch-Font outperforms state-of-the-art methods in style accuracy, perceptual quality, and generation speed while generalizing robustly to unseen characters and font styles. By simplifying the font creation process and delivering high-quality results, Patch-Font represents a significant step forward in making font design more accessible and scalable for diverse languages.
Utilization and Bio-Efficacy of Carotenoids, Vitamin A and Its Vitaminoids in Nutricosmetics, Cosmeceuticals, and Cosmetics’ Applications with Skin-Health Promoting Properties
Authors: Adamantidi Theodora , Lafara Maria-Parthena , Venetikidou Maria , Likartsi Eleni , Toganidou Ioanna , Tsoupras Alexandros
Published: February 2025

Following the significant advancement in the cosmetic field over the past few decades, carotenoids, vitamin A, and vitaminoids have emerged as pivotal components in the formulation of cosmetic products due to their diverse bioactive properties. Delving into a general approach on vitamin A and its derivatives’ structure, activity, biochemical way of action, and benefits, their role towards promoting mainly skin health is thoroughly detailed. For this purpose, vitamin A, vitaminoids, and carotenoids of animal, marine, plant, herb, and microorganism sources were extensively reviewed in order to evaluate their health benefits regarding skin protection. Vitamin A and its derivatives of any source heavily contributed to specific skin-related functions, including their anti-aging, skin regeneration, wound healing, hyperpigmentation, and acne treatment activity, by primarily supporting hydration, skin elasticity, and barrier repair. This review also entails recent advances in the delivery systems of these compounds, such as microencapsulation and nanoemulsions, while their potential side effects are addressed as well. Ultimately, limitations and future perspectives of vitamin A, vitaminoids, and carotenoids, considering their utilization in nutricosmetic, cosmeceutical, and cosmetic products, are further discussed.
The Progress in Treatment of β-Thalassemia and Research on Animal Models of the Disease
Authors: Zhang Shumeng , Dong Zexia , Yan Wenhao , Wang Wenjing , Pei Yangli , Feng Zheng
Published: February 2025

β-thalassemia is one of the most prevalent single-gene recessive disorders worldwide, characterized by the impaired synthesis of β-globin chains, which leads to ineffective erythropoiesis and results in anemia and iron overload, along with various complications. Therefore, establishing animal models that closely resemble β-thalassemia is essential for studying the pathogenesis and treatment of this disease. This article reviews the molecular mechanisms underlying β-thalassemia; highlights the research advancements of several common clinical treatment methods; and provides a summary of prevalent animal models of β-thalassemia, including mice, rabbits, and cynomolgus monkeys. Although the mouse model reproduces certain pathological characteristics of β-thalassemia, it is limited in its ability to correct specific gene mutations, making it less effective for certain aspects of the disease. In contrast, the rabbit model, which more closely resembles human physiology, offers an improved approximation. Furthermore, the cynomolgus monkey model outperforms both the mouse and rabbit models in exhibiting the severe phenotype of β-thalassemia, thanks to its notable genetic and physiological similarities to humans. This article aims to provide a comprehensive reference for researchers conducting animal experimental studies in relation to β-thalassemia.
Design and Development of Hugging Mechanism for Capturing Chest and Back Auscultatory Sounds
Authors: Tsumura Ryosuke , Ogawa Takuma , Umeno Reina , Baba Ryuta , Morishima Yuko , Yoshinaka Kiyoshi , Iwata Hiroyasu
Published: February 2025

Robotic auscultation has the potential to solve problems associated with gender issues by allowing examinations that eliminate the need for physical contact between doctor and patient. Aiming toward a robotic auscultation device capable of safely acquiring chest and back auscultatory sounds simultaneously, this study aimed to develop a unique actuator-less hugging mechanism with a multi-acoustic sensor array that can be transformed to wrap around the chest and back to fit the patient’s body shape. The mechanism consists of a twin-articulated arm with multi-layer gear coupling and a cam mechanism for power transmissions. The hugging motion is generated by pushing the cam mechanism by the patient. The force applied to the cam mechanism acts as the driving force for the twin-articulated arm. The trajectory of the arm changes depending on the distance that the cam mechanism is pressed, and it was designed to fit typical body types (obese, standard, and slender). Our results demonstrated that the proposed mechanism was able to be transformed for each body type, and its positional error was less than 15 mm in all body types. This means that the proposed mechanism is capable of safely acquiring chest and back auscultatory sounds whilst simultaneously fitting to various body shapes.
Factorization and Closed Form of Quantum Density Operators and Related Multiplicity
Authors: Cariolaro Gianfranco , Ruffa Edi
Published: February 2025

The final goal of this paper is to organize the tools needed to study digital Quantum Communications, where classical information is entrusted to quantum states represented by density operators. A density operator is usually defined starting from a set of kets in the Hilbert space and a probability distribution. A fundamental problem in Quantum Communications is the factorization of such operators of the form ρ=γγ*, where γ is a matrix called a density factor (DF). The environments considered are finite dimensional Hilbert space (discrete variables) and infinite dimensional Hilbert space (continuous variables). Using discrete variables, the multiplicity and the variety of DFs are investigated using the tools of matrix analysis, arriving in particular to establish the DF with minimal size. With continuous variables, the target is the closed-form factorization, which is achieved with recent results for the important class of Gaussian states. Finally, an application is carried out in Quantum Communications with noisy Gaussian states.
Sodium-Ion Batteries: Applications and Properties
Authors: Bača Petr , Libich Jiří , Gazdošová Sára , Polkorab Jaroslav
Published: February 2025

With the growing interest in reducing CO2 emissions to combat climate change, humanity is turning to green or renewable sources of electricity. There are numerous issues associated with the development of these sources. One of the key aspects of renewable energy sources is their problematic controllability, namely the control of energy production over time. Renewable sources are also associated with issues of recycling, utilization in different geographical zones, environmental impact within the required area, and so on. One of the most discussed issues today, however, is the question of efficient use of the energy produced from these sources. There are several different approaches to storing renewable energy, e.g., supercapacitors, flywheels, batteries, PCMs, pumped-storage hydroelectricity, and flow batteries. In the commercial sector, however, mainly due to acquisition costs, these options are narrowed down to only one concept: storing energy using an electrochemical storage device—batteries. Nowadays, lithium-ion batteries (LIBs) are the most widespread battery type. Despite many advantages of LIB technology, the availability of materials needed for the production of these batteries and the associated costs must also be considered. Thus, this battery type is not very ideal for large-scale stationary energy storage applications. Sodium-ion batteries (SIBs) are considered one of the most promising alternatives to LIBs in the field of stationary battery storage, as sodium (Na) is the most abundant alkali metal in the Earth’s crust, and the cell manufacturing process of SIBs is similar to that of LIBs. Unfortunately, considering the physical and electrochemical properties of Na, different electrode materials, electrolytes, and so on, are required. SIBs have come a long way since they were discovered. This review discusses the latest developments regarding the materials used in SIB technology.
Biophysics of Voice Onset: A Comprehensive Overview
Authors: DeJonckere Philippe H. , Lebacq Jean
Published: February 2025

Voice onset is the sequence of events between the first detectable movement of the vocal folds (VFs) and the stable vibration of the vocal folds. It is considered a critical phase of phonation, and the different modalities of voice onset and their distinctive characteristics are analysed. Oscillation of the VFs can start from either a closed glottis with no airflow or an open glottis with airflow. The objective of this article is to provide a comprehensive survey of this transient phenomenon, from a biomechanical point of view, in normal modal (i.e., nonpathological) conditions of vocal emission. This synthetic overview mainly relies upon a number of recent experimental studies, all based on in vivo physiological measurements, and using a common, original and consistent methodology which combines high-speed imaging, sound analysis, electro-, photo-, flow- and ultrasound glottography. In this way, the two basic parameters—the instantaneous glottal area and the airflow—can be measured, and the instantaneous intraglottal pressure can be automatically calculated from the combined records, which gives a detailed insight, both qualitative and quantitative, into the onset phenomenon. The similarity of the methodology enables a link to be made with the biomechanics of sustained phonation. Essential is the temporal relationship between the glottal area and intraglottal pressure. The three key findings are (1) From the initial onset cycles onwards, the intraglottal pressure signal leads that of the opening signal, as in sustained voicing, which is the basic condition for an energy transfer from the lung pressure to the VF tissue. (2) This phase lead is primarily due to the skewing of the airflow curve to the right with respect to the glottal area curve, a consequence of the compressibility of air and the inertance of the vocal tract. (3) In case of a soft, physiological onset, the glottis shows a spindle-shaped configuration just before the oscillation begins. Using the same parameters (airflow, glottal area, intraglottal pressure), the mechanism of triggering the oscillation can be explained by the intraglottal aerodynamic condition. From the first cycles on, the VFs oscillate on either side of a paramedian axis. The amplitude of these free oscillations increases progressively before the first contact on the midline. Whether the first movement is lateral or medial cannot be defined. Moreover, this comprehensive synthesis of onset biomechanics and the links it creates sheds new light on comparable phenomena at the level of sound attack in wind instruments, as well as phenomena such as the production of intervals in the sung voice.
Renal Abscess Associated with SGLT2 Inhibitors Administration in Heart Failure Without Other Previous Risk Factors: A Case Report
Authors: Munteanu Madalina Andreea , Nicolae Camelia , Popescu Razvan Ionut , Rusescu Andreea , Paun Nicolae , Nanea Tiberiu Ioan
Published: February 2025

Background and Clinical Significance: Renal abscess represents one infectious urological complication with lethal potential. The treatment of this pathology may differ depending on the severity of the symptoms and the size of the infectious collection. Diabetes, immunosuppression, and associated urinary pathologies are most frequently responsible for the development of abscesses. This case report presents the first documented case of a renal abscess associated with Sodium-glucose cotransporter 2 (SGLT2) inhibitors in a person without previous predisposing pathologies. Case Presentation: A 62-year-old patient presented to the emergency department for pain in the right flank, vomiting, and dysuria. The patient’s medical history revealed Heart Failure New York Heart Association (NYHA) Class II, Coronary Artery Disease (CAD) with prior angioplasty, and permanent Atrial Fibrillation. No prior urological or immunosuppressive conditions were detected. The Computed Tomography (CT) evaluation confirmed the ultrasound suspicion of a right renal abscess performed in the emergency room. The only risk factor identified was the initiation of SGLT2 inhibitor therapy for cardiac pathology approximately 2 months before. According to the small dimensions and urine culture, the abscess was successfully treated with antibiotic administration in collaboration with the urology department. The infectious process was remitted within 2 weeks. Conclusions: To our knowledge this is the first documented case of a renal abscess associated with SGLT2 inhibitor administration in a person without previous predisposing risk factors. Despite the relatively low incidence of urinary tract infections (UTIs) associated with SGLT2 inhibitors, their widespread use in the treatment of various socially significant conditions highlights the need for both patients and medical specialists to be aware of all potential risks and pay increased attention to these cases.
Role of the AIM2 Inflammasome in Cancer: Potential Therapeutic Strategies
Authors: Colarusso Chiara , Terlizzi Michela , Di Caprio Simone , Falanga Anna , D’Andria Emmanuel , d’Emmanuele di Villa Bianca Roberta , Sorrentino Rosalinda
Published: February 2025

Absent in melanoma 2 (AIM2) is a member of the innate immune sensors that recognizes cytosolic nucleic acids, leading to inflammasome assembly. In recent years, several studies in the oncology field have highlighted the presence of cytoplasmic double-stranded DNA (dsDNA) following necrosis and/or genomic instability, which is typical of malignant transformation. The recognition of dsDNA by the AIM2 inflammasome either in cancer cells or in immune cells can further exacerbate inflammatory processes on the basis of cancer progression. In this context, the role of AIM2 in cancer is still controversial in that some authors assume that AIM2 activation has pro-tumor activities, while others define it as anti-tumor. This discrepancy may be due to the nature of the cells where AIM2 is expressed or the histology of the tumor. This review aims to provide an overview of the controversial role of AIM2 in cancer, taking into consideration the pharmacological tools currently available to modulate AIM2 activity in cancer.
Wearable Wireless Functional Near-Infrared Spectroscopy System for Cognitive Activity Monitoring
Authors: Victorio Mauro , Dieffenderfer James , Songkakul Tanner , Willeke Josh , Bozkurt Alper , Pozdin Vladimir A.
Published: February 2025

From learning environments to battlefields to marketing teams, the desire to measure cognition and cognitive fatigue in real time has been a grand challenge in optimizing human performance. Near-infrared spectroscopy (NIRS) is an effective optical technique for measuring changes in subdermal hemodynamics, and it has been championed as a more practical method for monitoring brain function compared to MRI. This study reports on an innovative functional NIRS (fNIRS) sensor that integrates the entire system into a compact and wearable device, enabling long-term monitoring of patients. The device provides unrestricted mobility to the user with a Bluetooth connection for settings configuration and data transmission. A connected device, such as a smartphone or laptop equipped with the appropriate interface software, collects raw data, then stores and generates real-time analyses. Tests confirm the sensor is sensitive to oxy- and deoxy-hemoglobin changes on the forehead region, which indicate neuronal activity and provide information for brain activity monitoring studies.
In Situ Biofilm Monitoring Using a Heat Transfer Sensor: The Impact of Flow Velocity in a Pipe and Planar System
Authors: Netsch Andreas , Sen Shaswata , Horn Harald , Wagner Michael
Published: February 2025

Industrially applied bioelectrochemical systems require long-term stable operation, and hence the control of biofilm accumulation on the electrodes. An optimized application of biofilm control mechanisms presupposes on-line, in-situ monitoring of the accumulated biofilm. Heat transfer sensors have successfully been integrated into industrial systems for on-line, non-invasive monitoring of biofilms. In this study, a mathematical model for the description of the sensitivity of a heat transfer biofilm sensor was developed, incorporating the hydrodynamic conditions of the fluid and the geometrical properties of the substratum. This model was experimentally validated at different flow velocities by integrating biofilm sensors into cylindrical pipes and planar mesofluidic flow cells with a carbonaceous substratum. Dimensionless sensor readings were correlated with the mean biovolume measured gravimetrically, and optical coherence tomography was used to determine the sensors’ sensitivity. The biofilm sensors applied in the planar flow cells revealed an increase in sensitivity by a factor of 6 compared to standard stainless steel pipes, as well as improved sensitivity at higher flow velocities.