Miracle-dz/UniFAR-FTUs huggingface dataset:
Fibroblast Growth Factor in Diabetic Foot Ulcer: Progress and Therapeutic Prospects

Diabetic foot ulcer (DFU) is a combination of neuropathy and various degrees of peripheral vasculopathy in diabetic patients resulting in lower extremity infection, ulcer formation, and deep-tissue necrosis. The difficulty of wound healing in diabetic patients is caused by a high glucose environment and various biological factors in the patient. The patients’ skin local microenvironment changes and immune chemotactic response dysfunction. Wounds are easy to be damaged and ulcerated repeatedly, but difficult to heal, and eventually develop into chronic ulcers. DFU is a complex biological process in which many cells interact with each other. A variety of growth factors released from wounds are necessary for coordination and promotion of healing. Fibroblast growth factor (FGF) is a family of cell signaling proteins, which can mediate various processes such as angiogenesis, wound healing, metabolic regulation and embryonic development through its specific receptors. FGF can stimulate angiogenesis and proliferation of fibroblasts, and it is a powerful angiogenesis factor. Twenty-three subtypes have been identified and divided into seven subfamilies. Traditional treatments for DFU can only remove necrotic tissue, delay disease progression, and have a limited ability to repair wounds. In recent years, with the increasing understanding of the function of FGF, more and more researchers have been applying FGF-1, FGF-2, FGF-4, FGF-7, FGF-21 and FGF-23 topically to DFU with good therapeutic effects. This review elaborates on the recently developed FGF family members, outlining their mechanisms of action, and describing their potential therapeutics in DFU.
Critical Shear Stress for Erosion of Sand-Mud Mixtures and Pure Mud

The erosion threshold of sand-mud mixtures is investigated by analyzing the momentum balance of a sand particle or a mud parcel in the mixture bed surface, and a formula for the critical shear stress of sand-mud mixtures is developed, which also applies for pure sand and mud. The developed formula suggests that the variation of the critical shear stress of sand-mud mixtures over mud content is mainly caused by the varying dry bulk density of the mud component in the mixture. The developed formula reproduces well the variation of the critical shear stress of sand-mud mixtures over mud content and can predict the critical shear stress of both sand-mud mixtures and pure mud in the process of consolidation. The developed formula promises to be convenient for application by relating the critical shear stress to mud content and the dry bulk density of sediment.
Transcriptomic Profile and Probiotic Properties of Lactiplantibacillus pentosus Pre-adapted to Edible Oils

In this study, we determined whether pre-adapting Lactiplantibacillus pentosus strains, isolated from Aloreña green table olives, to vegetable-based edible oils improved their robustness and functionality; this may have great importance on their stress response during fermentation, storage, and digestion. Pre-adapting the strains to the corresponding oils significantly increased their probiotic functionality (e.g., auto-aggregation, co-aggregation with pathogens, and mucin adhesion), although results depended on the strain and the oil used for pre-adaptation. As such, we selected olive-adapted (TO) L. pentosus AP2-16, which exhibited improved functionality, and subjected it to transcriptomic profiling with the aim to understand the molecular mechanisms involved in the adaptation and the increased functionality. Global transcriptomic analysis of oil-adapted (olive or almond) and non-adapted (control) L. pentosus AP2-16 realized that 3,259 genes were expressed, with 2,779 mapped to the reference database. Comparative transcriptomic analysis showed that 125 genes (olive vs. control) and 108 genes (olive vs. almond) became significantly differentially expressed. TO L. pentosus AP2-16 responded by rerouting its metabolic pathways to balance energy production and storage, cell growth and survivability, host interactions (glycoconjugates), and other physiological features. As such, the pre-adaptation of lactobacilli with olive oil switches their transcriptional network to regulate robustness and functionality, possibly representing a novel approach toward the design and manufacture of probiotic products with improved stability and functionality.
How Does Mentoring Affect the Creative Performance of Mentors: The Role of Personal Learning and Career Stage

Mentoring has become a vital strategy for improving employee performance and organizational development. A few previous literature studies made a detailed study on the benefits of mentees. The creative performance of mentors that improves from mentoring, however, only draws little attention. This article extends this line of inquiry by shedding light on whether, how, and when mentoring affects the creative performance of mentors, which is a crucial topic in research and practice. Based on the conservation of the resources theory (COR) and relational cultural theory (RCT), this article investigates the influence mechanism and boundary conditions of mentoring on the creative performance of mentors by conducting a multisource empirical study in China. The result shows that there is a positive impact of mentoring on the creative performance of mentors. We also reveal that the relationship is mediated by personal learning, especially the relational job learning dimension. Furthermore, this article finds that the career stage of mentors moderates the relationship between mentoring and personal learning, namely, the relationship is stronger among mentors at the later career stage. The empirical findings show implications for an understanding of how the creative behavior of mentors benefits mentoring and can be beneficial for developing the targeted measures to promote competitive advantages for organizations.
Change from Metastable Cubic to Stable Tetragonal Form of Submicron Barium Titanate

The change from metastable cubic to stable tetragonal form was investigated on the submicron BaTiO3 which was prepared by the hydrolysis of titanium tetraisopropoxide in barium hydroxide solution and by crystallization in the glass of BaO–TiO2–SiO2–Al2O3 system. The precipitated BaTiO3 was found to be the aggregates of minute grains of about 100 A, to have large amount of strain and to absorb about 6% of OH and alcoholic radicals, some of them absorbed chemically. It is in a metastable cubic form. After the heating above 800 °C, it changed to stable tetragonal form in association with weight loss and decrease in lattice strain. The samples heated at 800–900 °C did not show the Curie point on the DTA curve although they were tetragonal. The samples heated at 1300 °C had the Curie point about 122 °C. The glass-devitrified BaTiO3 was also cubic and had large lattice strain. In order to change to tetragonal form, the heating above 1000 °C was needed. The metastable cubic form of submicron BaTiO3 seemed to be .
Formation-Flying SAR Receivers in Far-From-Transmitter Geometry: Signal Model and Processing Scheme

The paper focuses on the concept of a formation-flying synthetic aperture radar (FF-SAR) bistatic system composed of a set of compact, low-weight satellite receivers in close formation (within 1 km) placed in the same low-Earth orbit at large distance (about 100 km) from a transmitter. Each receiver is conceived to fit a 12-unit CubeSat. A signal model adapted to the proposed formation geometry is also presented, and a corresponding processing scheme to achieve range swath widening and signal-to-noise ratio (SNR) improvement is illustrated and discussed.
Building Facade Completion Using Semantic-Synchronized GAN

Due to the difference in high-rise distance, laser incident angle, and occlusion, incomplete point cloud of building facades is incomplete. The existing point cloud facade completion work does not fully explore the semantic constraints. Thus, it is difficult to deal with the facades with large-area missing. This paper proposes a novel facade completion framework for point clouds. By introducing semantic constraints and semantic-synchronized GAN, we solve the facade completion problem with large-area missing while the semantic consistency is augmented.
On-Site/In Situ Continuous Detecting ppb-Level Metal Ions in Drinking Water Using Block Loop-Gap Resonators and Machine Learning

Microwave measurements and machine learning algorithms are presented to estimate metal ion concentrations in drinking water. A novel block loop gap resonator (BLGR) as a microwave probe is designed and fabricated to estimate Pb ion concentrations in city water as low as 1 ppb with an rms error of 0.18 ppb. No physical contact between the BLGR probe and the water sample allows <italic>on-site/in situ</italic> continuous detection of ppb-level metal ion concentrations. The <inline-formula> <tex-math notation="LaTeX">$S_{11}$ </tex-math></inline-formula> raw data (amplitude and phase) from the BLGR are used to classify and estimate metal ion concentrations using a support vector regression algorithm. The performance of the proposed method to estimate Pb concentrations in the presence of interfering metal ions (Cu<sup>2+</sup>, Fe<sup>3+</sup>, and Zn<sup>2+</sup>) is also evaluated, and it is found that the average measurement error remains less than 13%.
Interannual Variability of Biomass (SMOS Vegetation Optical Depth) Over the Contiguous United States

Interannual variability in biomass represented by SMOS vegetation optical depth (VOD) and precipitation was assessed over the Contiguous United States. The greatest interannual variability in both VOD and precipitation occurred in shrubs and herbaceous (grasslands), with forests the least variable. At a continental scale, VOD was strongly correlated with annual precipitation. Results showed a significant correlation coefficient (∼ 0.93) between interannual variability of precipitation and biomass, indicating that the interannual variability of precipitation could be a good predictor of the interannual variability of biomass.
Multi-Scale SAR Ship Classification with Convolutional Neural Network

Ship classification in Synthetic Aperture Radar (SAR) images is significant but its application based on Convolutional Neural Network (CNN) has not been adequately studied. Considering that there will be the loss of SAR ship spatial information as the network deepening in CNN, which is a great obstacle for the further improvement of algorithm accuracy. Thus, to deal with the problem, in this paper, a novel multi-scale CNN (MS-CNN) is proposed. MS-CNN can utilize the multi-scale features to enhance the feature expression ability by the following three steps, namely flattening, integrating and classifying. As a result, the experiments on the OpenSARShip dataset show that MS-CNN can increase the classification accuracy by 4.81% than benchmark network.
Anomaly Detection in Post Fire Assessment

Over the last few years, natural disasters elevated dangerously in terms of immensity and prevalence over areas covered by forest and urban woodlands. Fast-spreading nature of the wildfires determine quick uncontrollable situations' causing significant effects in short periods. Despite increased difficulty in image processing approaches due to temporal resolution, complexity of spectral bands and illumination conditions, imagery data streams available from sun-synchronous satellites provide geospatial intelligence in monitoring and preventing fire threats. In this paper, we proposed a local scale burned area estimation framework that employs multispectral images in a deep learning architecture for detecting burned surfaces at patch level. This goal is accomplished by using an autoencoder (AE) network in which the latent feature layer learns normal background distribution, beneficial to background reconstruction. Furthermore, an outlier detection method (OCSVM) is used with aggregated features, latent and covariance components, in order to estimate burned coverage. Our method operates on data retrieved from Sentinel-2 (S2) constellation streaming source, which mainly contain normal scenes and limited fire affected spots.
Influence of the Mosaicking Algorithm on Sentinel-1 Land Cover Classification Over Rough Terrain

The aim of this study was to assess different strategies for wall to wall SAR-based (Sentinel-1) land cover mapping over mountainous areas. The first strategy was to classify by-orbit and combine the resulting classifications. The second strategy derived land cover from ascending-descending SAR mosaics. In both cases, the inverse of the scattering area was used for weighting. Slightly more accurate results were obtained using by-orbit classification, with larger omission errors for urban areas and forest being observed when classifying the SAR mosaics. Such errors were caused by mismatches between orbits caused by image normalization errors and/or directional effects.
Validation Satellite Sea Surface Temperature in the Coastal Regions

Validation of daily Optimum Interpolation Sea Surface Temperature (OISST) data from 1982 to 2018 was performed by comparison with quality-controlled in-situ water temperature data from Korea Oceanographic Data Center observations in the Korean coastal regions. In contrast to the relatively high accuracy of the SSTs in the open ocean, the SSTs of the coastal regions exhibited large root-mean-square errors (RMSE) and a bias, which tended to be amplified towards the coastal lines. The coastal SSTs in the Yellow Sea presented much higher RMSE and bias due to the appearance of cold water on the surface induced by tidal mixing over shallow bathymetry. The long-term trends of OISSTs were also compared with those of in-situ water temperatures over decades. Although the trends of OISSTs deviate from those of in-situ temperatures in coastal regions, the spatial patterns of the OISST trends revealed a similar structure to those of in-situ temperature trends.
Assessment of CNN-Based Methods for Single Tree Detection on High-Resolution RGB Images in Urban Areas

Maintaining vegetation cover in cities is a key component to keep cities safe and resilient. The monitoring of trees is usually done with LiDAR data or multi and hyperspectral images. In this sense, remote sensing RGB images are presented as a cheaper and easier processing solution. Here, we proposed to evaluate deep learning-based methods combined with high-resolution RGB images to detect single-trees in the urban environment. Three state-of-the-art methods are tested: Faster-RCNN, RetinaNet, and ATSS. A total of 220 images were used, in which we manually labeled 3382 trees. For the proposal task, our findings show that ATSS is 3% more accurate than Faster-RCNN and 4% than RetinaNet. However, in a qualitative inspection, Faster-RCNN and RetinaNet seems to be better at this task. Our findings shows the need of further research for developing suitable tools for urban tree detection. This tools can help cities top achieve a more sustainable and resilient environment especially to face climate change.
Deep Neural Networks for Mapping Integrated Crop-Livestock Systems Using Planetscope Time Series

Mapping highly dynamic cropping systems using satellite image time series is still challenging even when robust approaches are used. We assessed the potential of using high spatial and temporal resolution PlanetScope time series and deep neural networks (Convolutional Neural Networks (CNN) in one dimension - Conv1D, Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP)) for mapping integrated crop-livestock systems (ICLS) and different land covers in the western region of São Paulo State, Brazil. We used 10-day and 15-day composite EVI and NDVI time series (both individually and combined) as input data in the neural network classifiers. Conv1D using both EVI and NDVI 10 day-composite time series outperformed the other classifiers evaluated in this study (LSTM and MLP), allowing improved discrimination of land parcels with ICLS in our study area.
A Communication Efficient Federated Learning Approach to Multi Chest Diseases Classification

Recently, deep learning (DL) has been playing a crucial role in aiding disease detection and started a vital role in medical image analysis. Due to its numerous advantages such as promising accuracy, ease of scalability, and fast diagnosis, DL has been a practical alternative to laboratory-based disease detection methods, especially for the Covid-19 detection. DL also has some disadvantages for privacy concerns, legal regulations, and hardware needs. To address this issue, we adopted the federated learning (FL) approach which is a novel data-private collaborative learning procedure that enables leveraging the big and diverse data securely without collecting the data into a central place. This paper introduces a CNN architecture for multi chest-diseases classification from chest Xray (CXR) images. The proposed classification model has reached % 93.34 accuracy on the task. A combination of various publicly available datasets also presented with 2SS33 CXR images, a mixture of Covid-19, viral or bacterial pneumonia, lung opacity, and normal cases. The proposed FL based model also achieves the tasks with % 92.96 accuracy. Weight pruning and quantization were implemented to reduce the communication cost by 10x of FL which is usually the primary bottleneck in FL. The communication efficient federated training achieves the task with % 92.44 accuracy with a negligible loss in the accuracy. Lastly, the results of central training, federated training and communication efficient federated training were given and compared with the experiments. It is expected that the proposed model might help to encourage organizations or researchers to develop their own models, to improve collaboration, to increase data utility, and to get more benefits from unused data by adopting the federated learning approach.
Tropical Cyclone Size Estimation Using Deep Convolutional Neural Network

The accurate estimation of tropical cyclone (TC) size is one of the key steps in TC forecasting and disaster warninglmanagement. In this study, we proposed the use of deep convolutional neural networks (CNN) to estimate the size of tropical cyclone. To the best of our knowledge, this is the first study to estimate the size of tropical cyclones using deep learning methods; we use about 1,000 tropical cyclones which contain about 30,000 infrared remote sensing images as the data set. Compared with the best track archives, the mean error of our proposed model is 24nmi, the error is even smaller than the Multiplatform Tropical Cyclone Surface Winds Analysis (MTCSWA) operated by National Oceanic and Atmospheric Administration(NOAA), which shows the great potential of deep learning in estimating the size of tropical cyclones.
Correlation Between NDVI and Sentinel-1 Derived Features for Maize

Operational agricultural applications of remote sensing, such as crop monitoring or irrigation scheduling, often rely on the Normalized Difference Vegetation Index (NDVI) obtained from multispectral observations. Yet, cloud cover limits its availability, so the possibility to estimate it from SAR data is appealing, as it would enable a complementary monitoring of crops. The objective of this article is to evaluate the correlation of NDVI with several SAR features obtained from Sentinel-1 data over maize. Eighteen maize fields, located in the province of Navarre (Spain), were analyzed in two agricultural campaigns. Nine SAR features were evaluated, including: the backscattering coefficients in VH and VV polarizations, their ratio, product, sum and difference, as well as the Radar Vegetation Index (RVI), the Vertical Dual De-Polarization Index (VDDPI) and the Normalized Difference Polarization Index (NDPI). The correlations obtained in linear and dB units were compared, as well as the influence of temporal smoothing. The highest correlation was obtained with VH backscatter expressed in dB.
Random Blur Data Augmentation for Scene Text Recognition

In this paper, we propose to apply data augmentation approaches that provide more diverse training images, thus helping train more robust deep models for the Scene Text Recognition (STR) task. The data augmentation methods are Random Blur Region (RBR) and Random Blur Units (RBUs). Specifically, we first introduce RBR designed for the STR task. In training, RBR randomly selects a region and sets the pixels in this region with an average value. However, when RBR provides more various training samples for STR, it may make the samples ambiguous and reduce the recognition accuracy. To address the above problem, we also propose RBUs that divides the blur region into several units. Note that the pixels of one unit share the same value. In this way, RBUs can provide additional readable training samples and help train more robust deep models. Extensive experiments on several STR datasets show that RBUs achieve highly competitive performance. Besides, RBUs are complementary to commonly used data augmentation techniques.
Recent Advances in Computer-Aided Medical Diagnosis Using Machine Learning Algorithms With Optimization Techniques

Artificial intelligence is a spectacular part of computer engineering that has earned a compelling diversion in the field of medical data classification due to its state-of-art algorithmic strength and learning capabilities. Machine Learning is a major sub-domain of artificial intelligence, where it has become one of the most promising fields in computer science. In recent years, there is a large spectrum of healthcare and biomedical data that has been growing intensely. Due to the huge labeled or unlabeled data, it is important to have a compact and robust machine learning solution for classification. Several optimizers have been deployed to improve the inclusive performance of machine learning models. The classification of machine learning models depends on several factors. This comprehensive review paper aims to insight into the current stage of optimized machine learning success on medical data classification. An increasing number of unstructured medical data has been utilizing in machine learning algorithms to predict intuitions. But it is difficult to inherent immense intuition from those data. So machine learning researchers have utilized state-of-art optimizers and novel feature selection techniques to overcome and emend the performance accuracy. We have highlighted some recent literature, which exhibits the robust impact of optimizers and feature selection on machine learning techniques on medical data characterization. On the other hand, a clean-cut introduction on machine learning and theoretical outlook of widely utilized optimization techniques like genetic algorithm, gray wolf optimization, and particle swarm optimization are discussed for initial understanding of the optimization techniques.
MAPLE: A Machine Learning based Aging-Aware FPGA Architecture Exploration Framework

In this paper, we develop a framework called MAPLE to enable the aging-aware FPGA architecture exploration. The core idea is to efficiently model the aging-induced delay degradation at the coarse-grained FPGA basic block level using deep neural networks (DNNs). For each type of the FPGA basic block such as LUT and DSP, we first characterize its accurate delay degradation via transistor-level SPICE simulation under a versatile set of aging factors from the FPGA fabric and in-field operation. Then we train one DNN model for each block type to quickly and accurately predict the complex relation between its delay degradation and comprehensive aging factors. Moreover, we integrate our DNN models into the widely used Verilog-to-Routing toolflow (VTR 8) to support analyzing the impact of aging-induced delay degradation on the entire large-scale FPGA architecture. Experimental results demonstrate that MAPLE can predict the delay degradation of FPGA blocks 104 to 107 times faster than transistor-level SPICE simulation, with a prediction error less than 0.7%. Our case study demonstrates that FPGA architects can effectively leverage MAPLE to explore better aging-aware FPGA architectures.