Miracle-dz/UniFAR-FTUs huggingface dataset:
Review of Low Aspect Ratio Blade Dynamics for Electrical Axial Fans and Compressors

The quest for lighter and shorter propulsion systems has led to the reduction of axial compressor and fan blade chord-lengths. Theoretical and experimental results show that the geometrical aspect-ratio criterion significantly affects the overall performance of axial flow compressors and fans. In conducting this review, it was found that the experimental results differ from one literature source to another. Highlighting that the way in which blade aspect ratio affects the performance of axial flow compressors and fans is still not fully understood. Nonetheless, the reviewed literature has still proven valuable for compressor and fan design. This paper focuses on creating a review of the available literature relating to how low aspect ratio blades may affect the performance of electrical axial compressors and fans.
A COVID-19 Epidemics Trend Prediction Algorithm Based on LSTM

The polynomial regression algorithm can fit almost all data sequences by adding high-order terms of independent variables, but on long sequences, it can only reflect the development trends of the sequences, and the predicted value is not accurate enough. RNN has great advantages in predicting and fitting time sequence data, especially Long Short-Term Memory (LSTM) network has good performance in predicting long time sequence. This paper respectively adopts polynomial regression and LSTM analysis and predict the effect of epidemic prevention and control policies in various countries to control the epidemic and the economic recession. The experiment results show the Covid-19 epidemics trend prediction algorithm based on LSTM has better prediction effects, and is helpful in practical life.
Machine Learning Based Student Academic Performance Prediction

Every educational system organizational goal is to provide a good and fruitful knowledge to the students. Now a days most of the educational institutions are spending most of their time and economy on finding out students' performance. By analyzing the performance, they identify certain cluster of the students for whom they must give extra bit of care and actions, so that they performance gets enhanced. Researchers have recently proposed several machine learning-based algorithms for predicting academic achievement. In this paper, Linear Regression algorithm and Random Forest algorithm are used to predict a student's academic achievement. On the basis of confusion matrix, accuracy, precision, recall, and F1 ranking, the performance of two algorithms was compared to that of existing algorithms. The Random Forest algorithm-based prediction is more accurate, according to the results report.
Machine Learning-Based Optimal Cell Balancing Mechanism for Electric Vehicle Battery Management System

Cell balancing is a vital function of battery management system (BMS), which is implemented to extend the battery run time and service life. Various cell balancing techniques are being focused due to the growing requirements of larger and superior performance battery packs. The passive balancing approach is the most popular because of its low cost and easy implementation. As the balancing energy is dissipated as heat by the balancing resistors, an appropriate thermal scheme of the balancing system is necessary, to keep the BMS board temperature under a tolerable limit. In this paper, optimum selection of balancing resistor with respect to degree of cell imbalance, balancing time, C- rate, and temperature rise using machine learning (ML) based balancing control algorithm is proposed to improve the balancing time and optimal power loss management. Variable resistors are utilised in the passive balancing system, in order to optimize the power loss and to obtain optimal thermal characterization. The performance of the proposed system is evaluated using back propagation neural network (BPNN), radial basis neural network (RBNN), and long short term memory (LSTM). Error analysis of the balancing system is done to optimize balancing parameters and the proposed algorithms are compared using performance indices such as mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) to validate the balancing model performance. The possible optimization scope for implementing passive balancing using machine learning algorithms are experimented in the Matlab-Simscape environment.
Relaying in the Internet of Things (IoT): A Survey

The deployment of relays between Internet of Things (IoT) end devices and gateways can improve link quality. In cellular-based IoT, relays have the potential to reduce base station overload. The energy expended in single-hop long-range communication can be reduced if relays listen to transmissions of end devices and forward these observations to gateways. However, incorporating relays into IoT networks faces some challenges. IoT end devices are designed primarily for uplink communication of small-sized observations toward the network; hence, opportunistically using end devices as relays needs a redesign of both the medium access control (MAC) layer protocol of such end devices and possible addition of new communication interfaces. Additionally, the wake-up time of IoT end devices needs to be synchronized with that of the relays. For cellular-based IoT, the possibility of using infrastructure relays exists, and noncellular IoT networks can leverage the presence of mobile devices for relaying, for example, in remote healthcare. However, the latter presents problems of incentivizing relay participation and managing the mobility of relays. Furthermore, although relays can increase the lifetime of IoT networks, deploying relays implies the need for additional batteries to power them. This can erode the energy efficiency gain that relays offer. Therefore, designing relay-assisted IoT networks that provide acceptable trade-offs is key, and this goes beyond adding an extra transmit RF chain to a relay-enabled IoT end device. There has been increasing research interest in IoT relaying, as demonstrated in the available literature. Works that consider these issues are surveyed in this paper to provide insight into the state of the art, provide design insights for network designers and motivate future research directions.
Antenna Sensor Based on AMC Array for Contactless Detection of Water and Ethanol in Oil

When ethanol and water are mixed into gasoline, the dielectric properties of gasoline will change. Based on this characteristic, we propose an antenna sensor and study its radiation and sensing performance in detection of ethanol gasoline in 6-7GHz band. In order to avoid the corrosion of the oil sample on the antenna, the antenna adopts a multilayer dielectric substrate structure. A $2\times 2$ artificial magnetic conductor (AMC) array is loaded on the bottom of the antenna to increase the gain of the sensor and reduce the return loss. The antenna sensor has good radiation pattern with a peak gain of 4 dB and high quality factor(Q) up to 69. Furthermore, the antenna sensor shows the advantages of high sensitivity and wide detection range on the detection of oil-water mixture and ethanol gasoline concentration. In order to verify the simulated results, the radiation and sensing performance of the antenna were measured, and the measured results were basically consistent with the simulated ones. Therefore, the proposed antenna sensor can be used for real-time wireless detection in ethanol gasoline transportation.
MEM-KGC: Masked Entity Model for Knowledge Graph Completion With Pre-Trained Language Model

The knowledge graph completion (KGC) task aims to predict missing links in knowledge graphs. Recently, several KGC models based on translational distance or semantic matching methods have been proposed and have achieved meaningful results. However, existing models have a significant shortcoming–they cannot train entity embedding when an entity does not appear in the training phase. As a result, such models use randomly initialized embeddings for entities that are unseen in the training phase and cause a critical decrease in performance during the test phase. To solve this problem, we propose a new approach that performs KGC task by utilizing the masked language model (MLM) that is used for a pre-trained language model. Given a triple (head entity, relation, tail entity), we mask the tail entity and consider the head entity and the relation as a context for the tail entity. The model then predicts the masked entity from among all entities. Then, the task is conducted by the same process as an MLM, which predicts a masked token with a given context of tokens. Our experimental results show that the proposed model achieves significantly improved performances when unseen entities appear during the test phase and achieves state-of-the-art performance on the WN18RR dataset.
An End-to-End Anti-Jamming Target Detection Method Based on CNN

Target detection in the presence of main-lobe deception jamming has received intensive attention. Generally, traditional target detection with additive jamming can be regarded as an adjacent process of jamming suppression and target detection. Since target information can be lost in jamming suppression, the detection performance can degrade severely. Aiming at this issue, an end-to-end anti-jamming target detector is proposed based on convolutional neural network (CNN) in the presence of interrupted-sampling repeater jamming. The CNN method first converts the single-pulse compression filter into a series of feature extraction filters, accumulating the energy of various features in various dimensions and on multiple scales. Then, a feature fusion filter added after the feature extraction stage is utilized to suppress jamming features while simultaneously retaining the actual target features. The two stages can be achieved in the CNN structure, and all the filters in this method are obtained by training the CNN. Numerical simulations show the efficiency of the proposed method.
First intramolecular Diels–Alder reactions using chromone derivatives: synthesis of chromeno[3,4-b]xanthones and 2-(benzo[c]chromenyl)chromones

A series of novel (E)-2-(2-propargyloxystyryl)chromones and (E,E)-2-[4-(2-propargyloxyphenyl)buta-1,3-dien-1-yl]chromones were designed and synthesized via aldol condensation of 2-methylchromones with 2-propargyloxy(benzaldehyde and cinnamaldehyde), respectively. Both chromone derivatives were used as substrates in microwave-assisted intramolecular Diels–Alder reactions, affording chromeno[3,4-b]xanthones and 2-(benzo[c]chromenyl)chromones. This is the first report involving chromone derivatives in intramolecular Diels–Alder reactions for the synthesis of new oxygen heterocycles, namely xanthone- and flavone-type compounds.
Improved Gas Separation of PEBAX-CSWCNTs Mixed Matrix Membranes

In the present study, mixed matrix membranes (MMMs) were prepared using PEBAX Â® 3000 as polymer matrix and single-wall carbon nanotubes (SWCNTs) functionalized with carboxyl groups as nanofillers. The effects of the nanofillers on separation of CO 2 /N 2 and CO 2 /CH 4 were investigated. The pristine PEBAX membrane indicated gas selectivity values of 23 and 13 for CO 2 /N 2 and CO 2 /CH 4 , respectively. However selectivity of the modified membrane for gas pairs of CO 2 /N 2 and CO 2 /CH 4 improved to the values of 106.4 and 31.3, respectively. In other words, selectivity of modified membranes compared to those of unmodified ones enhanced greatly. The dramatic increase in gas selectivity of the mixed matrix membranes can be attributed to the polar groups of caboxyl-functionalized single-wall carbon nanotubes (CSWCNTs). While CO 2 permeability of MMMs increaesd, permeability of nonpolar gases (N 2 and CH 4 ) decreased. FTIR spectra depicted that there were inter/intramolecular forces between ether and amide groups of the polymer chains. For PEBAX membrane filled with 10 wt% CSWCNTs, the peaks of C-O-COŒ N-H, and H-N-C=O functional groups shifted to lower values due to the formation of hydrogen bonds between polar carboxyl groups of CSWCNTs and amide/ether groups of PEBAX copolymer. Relative crystallinity values of the membranes with various CSWCNTs content were calculated using I”H f data obtained from DSC measurements. Results demonstared that the rise in content of CSWCNTs brought about the decrement in crystallinity values of polyamide segments. The morphology of the membrane containing 10 wt% CSWCNTs was also investigated emplying AFM images, and a suitable compatability and adhere between PEBAX and CSWCNTs was last confirmed.
A Novel Two-stage Prediction Model to Classify Functional Connectivity for Brain Disease Diagnosis

Neuroscience studies have demonstrated that inherent interactions in human brain are associated with various brain diseases. The functional connectivity (FC) analysis in the resting-state functional magnetic resonance imaging (rs-fMRI) has attracted increasing attention. Recently, the application of deep learning algorithms has shown great potential in exploring the diagnosis biomarker for brain disease. However, the high dimensionality and small sample size properties of the FC patterns usually restrict the training of deep learning models and limit the model generalization performance. To solve this problem, this paper proposed a novel prediction model with a two-stage design to extract and classify FC features for the brain diseases diagnosis. Our model employed a flexible configuration that contained a deep learning-based FC feature extraction stage followed by a separate classification stage. In the first stage, stacked sparse denoising autoencoders were employed to extract and learn the abstract feature representations of the initial FC patterns with both unsupervised and supervised learning steps. In the second stage, the learnt features were used to train a separate simple classifier for brain disease classification. We conducted experiments on a large-scale fMRI dataset to construct FC patterns and distinguish autism patients from healthy controls. Results showed that our two-stage model outperformed the competing classification methods and showed robust generality for different brain nodes parcellation. Our study indicated that this flexible two-stage model design can further improve the classification performance and provide a promising approach for the computer-aided diagnosis of brain disorders.
Can the Structure Similarity of Training Patches Affect the Sea Surface Temperature Deep Learning Super-Resolution?

Meso- and fine-scale sea surface temperature (SST) is an essential parameter in oceanographic research. Remote sensing is an efficient way to acquire global SST. However, single infrared-based and microwave-based satellite-derived SST cannot obtain complete coverage and high-resolution SST simultaneously. Deep learning super-resolution (SR) techniques have exhibited the ability to enhance spatial resolution, offering the potential to reconstruct the details of SST fields. Current SR research focuses mainly on improving the structure of the SR model instead of training dataset selection. Different from generating the low-resolution images by downscaling the corresponding high-resolution images, the high- and low-resolution SST are derived from different sensors. Hence, the structure similarity of training patches may affect the SR model training and, consequently, the SST reconstruction. In this study, we first discuss the influence of training dataset selection on SST SR performance, showing that the training dataset determined by the structure similarity index (SSIM) of 0.6 can result in higher reconstruction accuracy and better image quality. In addition, in the practical stage, the spatial similarity between the low-resolution input and the objective high-resolution output is a key factor for SST SR. Moreover, the training dataset obtained from the actual AMSR2 and MODIS SST images is more suitable for SST SR because of the skin and sub-skin temperature difference. Finally, the SST reconstruction accuracies obtained from different SR models are relatively consistent, yet the differences in reconstructed image quality are rather significant.
Disentangling Climatic Factors and Human Activities in Governing the Old and New Forest Productivity

Forest ecosystem plays a vital role in the global carbon cycle and maintaining climate stability. However, how net primary productivity (NPP) dynamics of different stand ages of forest respond to climatic change and residual (being other climate factors or human activities) still remain unclear. In this study, firstly, forests are divided into two categories based on their stand age: forests appeared before appeared before the research period (Fold), and forests appeared during the research period (Fnew). Secondly, we improved a quantitative method of basic partial derivatives to disentangle the relative contributions of climatic factors, other climate factors, and human activities to the NPP of Fold and Fnew. Then, different scenarios were simulated to identify the dominant drivers for forest restoration and degradation. In this study, we hypothesized the residual of Fold was other climate factors rather than human activities. Our results revealed that from 2000 to 2019, Fold and Fnew of NPP in Yangtze River Basin showed a significant increment trend and precipitation was the major positive contributor among all of the climatic factors. We found that, in Fold, climate change and other climate factors contributed 9.77% and 28.33%, respectively, in explaining NPP. This finding unsupported our initial hypothesis and implied that residuals should be human activities for Fold. Furthermore, we found that human activities dominate either restoration or degradation of Fnew. This result may be due to the attenuated human disturbances and strengthened forest management, such as ecological policies, forest tending, closing the land for reforestation, etc. Therefore, based on disentangling the two types of factors, we concluded that human activities govern the forest change, and imply that the environment-friendly forest managements may favorite to improving the forest NPP against the impacts of climate change. Thus, effective measures and policies are suggested implement in controlling forest degradation in facing climate change.
Hyperspectral Dimensionality Reduction Based on Inter-Band Redundancy Analysis and Greedy Spectral Selection

Hyperspectral imaging systems are becoming widely used due to their increasing accessibility and their ability to provide detailed spectral responses based on hundreds of spectral bands. However, the resulting hyperspectral images (HSIs) come at the cost of increased storage requirements, increased computational time to process, and highly redundant data. Thus, dimensionality reduction techniques are necessary to decrease the number of spectral bands while retaining the most useful information. Our contribution is two-fold: First, we propose a filter-based method called interband redundancy analysis (IBRA) based on a collinearity analysis between a band and its neighbors. This analysis helps to remove redundant bands and dramatically reduces the search space. Second, we apply a wrapper-based approach called greedy spectral selection (GSS) to the results of IBRA to select bands based on their information entropy values and train a compact convolutional neural network to evaluate the performance of the current selection. We also propose a feature extraction framework that consists of two main steps: first, it reduces the total number of bands using IBRA; then, it can use any feature extraction method to obtain the desired number of feature channels. We present classification results obtained from our methods and compare them to other dimensionality reduction methods on three hyperspectral image datasets. Additionally, we used the original hyperspectral data cube to simulate the process of using actual filters in a multispectral imager.
Empirical Models for Spatio-Temporal Live Fuel Moisture Content Estimation in Mixed Mediterranean Vegetation Areas Using Sentinel-2 Indices and Meteorological Data

Live fuel moisture content (LFMC) is an input factor in fire behavior simulation models highly contributing to fire ignition and propagation. Developing models capable of accurately estimating spatio-temporal changes of LFMC in different forest species is needed for wildfire risk assessment. In this paper, an empirical model based on multivariate linear regression was constructed for the forest cover classified as shrublands in the central part of the Valencian region in the Eastern Mediterranean of Spain in the fire season. A sample of 15 non-monospecific shrubland sites was used to obtain a spatial representation of this type of forest cover in that area. A prediction model was created by combining spectral indices and meteorological variables. This study demonstrates that the Normalized Difference Moisture Index (NDMI) extracted from Sentinel-2 images and meteorological variables (mean surface temperature and mean wind speed) are a promising combination to derive cost-effective LFMC estimation models. The relationships between LFMC and spectral indices for all sites improved after using an additive site-specific index based on satellite information, reaching a R2adj = 0.70, RMSE = 8.13%, and MAE = 6.33% when predicting the average of LFMC weighted by the canopy cover fraction of each species of all shrub species present in each sampling plot.
Mapping Canopy Heights in Dense Tropical Forests Using Low-Cost UAV-Derived Photogrammetric Point Clouds and Machine Learning Approaches

Tropical forests are a key component of the global carbon cycle and climate change mitigation. Field- or LiDAR-based approaches enable reliable measurements of the structure and above-ground biomass (AGB) of tropical forests. Data derived from digital aerial photogrammetry (DAP) on the unmanned aerial vehicle (UAV) platform offer several advantages over field- and LiDAR-based approaches in terms of scale and efficiency, and DAP has been presented as a viable and economical alternative in boreal or deciduous forests. However, detecting with DAP the ground in dense tropical forests, which is required for the estimation of canopy height, is currently considered highly challenging. To address this issue, we present a generally applicable method that is based on machine learning methods to identify the forest floor in DAP-derived point clouds of dense tropical forests. We capitalize on the DAP-derived high-resolution vertical forest structure to inform ground detection. We conducted UAV-DAP surveys combined with field inventories in the tropical forest of the Congo Basin. Using airborne LiDAR (ALS) for ground truthing, we present a canopy height model (CHM) generation workflow that constitutes the detection, classification and interpolation of ground points using a combination of local minima filters, supervised machine learning algorithms and TIN densification for classifying ground points using spectral and geometrical features from the UAV-based 3D data. We demonstrate that our DAP-based method provides estimates of tree heights that are identical to LiDAR-based approaches (conservatively estimated NSE = 0.88, RMSE = 1.6 m). An external validation shows that our method is capable of providing accurate and precise estimates of tree heights and AGB in dense tropical forests (DAP vs. field inventories of old forest: r2 = 0.913, RMSE = 31.93 Mg ha−1). Overall, this study demonstrates that the application of cheap and easily deployable UAV-DAP platforms can be deployed without expert knowledge to generate biophysical information and advance the study and monitoring of dense tropical forests.