Miracle-dz/UniFAR-FTUs huggingface dataset:
Graph Representation Learning Towards Patents Network Analysis

Patent analysis has recently been recognized as a powerful technique for large companies in the world to lend them insight into the age of competition among various industries. This technique is considered a shortcut for developing countries since it can significantly accelerate their technology development. Therefore, as an inevitable process, patent analysis can be utilized to monitor rival companies and diverse industries. In this research, a graph representation learning approach employed to create, analyze and find similarities of the patents data registered in the Iranian Official Gazette. The patent records were scrapped and wrangled through the Iranian Official Gazette portal. Afterward, the key entities were extracted from the scrapped patents dataset to create the Iranian patents graph from scratch based on novel natural language processing and entity resolution technique. Finally, thanks to the utilization of novel graph algorithms and text mining methods, we identified new areas of industry and research from Iranian patent data, which can be used extensively to prevent duplicate patents, familiarity with similar and connected inventions, Awareness of legal entities supporting patents and knowledge of researchers and linked stakeholders in a particular research field.
Dynamic Modeling, Control and Simulation of a Wind and PV Hybrid System for Grid Connected Application Using MATLAB

This paper proposes a dynamic modeling and control strategy for a grid connected hybrid wind and photovoltaic (PV) energy system inter-connected to electrical grid through power electronic interface. A gearless permanent magnet synchronous generator (PMSG) is used to capture the maximum wind energy. The PV and wind systems are connected dc-side of the voltage source inverter through a boost converter individually and maintain a fixed dc output at dc link. A proper control scheme is required to operate power converters to match up the grid connection requirements. This study considered the performance of modeled hybrid system under different case scenarios. All simulation models are developed using MATLAB/Simulink. Index Terms— Wind turbine (WT) model,PMSG, Photovoltaic (PV) model, Grid side converter (PWM Inverter), Grid connected hybrid system model.
MARL-based Optimal Route Control in Multi-AGV Warehouses

Automated guided vehicles (AGVs) are an essential component for automation fulfillment centers, a kind of warehouse. Efficient control of the AGV leads to easier management of inventory in the fulfillment center. To increase the productivity of various warehouses including fulfillment centers, we propose a Multi-Agent Reinforcement Learning (MARL)-based algorithm for cooperative control of AGVs. The proposed algorithm is based on a popular cooperative MARL algorithm, and utilizes an additional technique for path control of AGVs to distinguish the sacrifices of each agent and compensate them accordingly. We evaluate the proposed algorithm in comparison with a basic MARL algorithm on two fulfillment center layouts and provide further insight via the visualization of the results.
System Capacity Analysis of Asynchronous FBMC and OFDM Systems in the Presence of Adjacent Channel Interference and Multipath Fading

This paper compares the performances of filter bank multicarrier (FBMC) and orthogonal frequency division multiplexing (OFDM) under multipath fading channels, in terms of their theoretical system capacities. OFDM generates high levels of out-of-band (OOB) radiation while being resistant to inter-symbol interference (ISI). In contrast, the effect of subcarrier-wise filtering in FBMC inhibits the generation of OOB radiation, which also reduces its adjacent channel interference (ACI). However, it suffers from ISI and inter-carrier interference (ICI), caused by multipath fading. The primary aim of this study is to demonstrate the theoretical system capacity of FBMC and OFDM systems, consider their ACI and multipath fading in an asynchronous scenario, and ascertain the superiority of each approach based on the parameters of guard band and delay spread.
SonoTweezer: An Acoustically Powered End-Effector for Underwater Micromanipulation

Recent advances in contactless micromanipulation strategies have revolutionized prospects of robotic manipulators as next-generation tools for minimally invasive surgeries. In particular, acoustically powered phased arrays offer dexterous means of manipulation both in air and water. Inspired by these phased arrays, we present SonoTweezer: a compact, low-power, and lightweight array of immersible ultrasonic transducers capable of trapping and manipulation of sub-mm sized agents underwater. Based on a parametric investigation with numerical pressure field simulations, we design and create a six-transducer configuration, which is small compared to other reported multi-transducer arrays (16–256 elements). Despite the small size of array, SonoTweezer can reach pressure magnitudes of 300 kPa at a low supply voltage of 25 V to the transducers, which is in the same order of absolute pressure as multi-transducer arrays. Subsequently, we exploit the compactness of our array as an end-effector tool for a robotic manipulator to demonstrate long-range actuation of sub-millimeter agents over a hundred times the agent’s body length. Furthermore, a phase-modulation over its individual transducers allows our array to locally maneuver its target agents at sub-mm steps. The ability to manipulate agents underwater makes SonoTweezer suitable for clinical applications considering water’s similarity to biological media, e.g., vitreous humor and blood plasma. Finally, we show trapping and manipulation of micro-agents under medical ultrasound (US) imaging modality. This application of our actuation strategy combines the usage of US waves for both imaging and micromanipulation.
Smart Anomaly Detection: Deep Learning modeling Approach and System Utilization Analysis

The objective of this project is to perform automated classification of anomalies of system within a large scale cloud production environment using neural network based models on time series data. In large clusters of mixed workload servers, the ability to automatically identify abnormal system utilization is a challenge due to the scale of the problem. To solve this problem, we use deep learning modeling techniques, Long-Short Term Memory (LSTM) models. The data set, to build and test the models, is from production systems over the span of two weeks. The models will use utilization metrics such as CPU, Memory, Network IO, Process Run Queue and Open Files. Anomalous usage in the production cluster is classified as (1) very low usageless than 5% across selected metrics and (2) known anomalous behaviors like memory leaks. This paper will explain how we can create a model that will identify the anomalies we want to flag, in the real world data. We are using Intel Optimized TensorFlow in containers distributed within a cluster of TensorFlow servers.
Optimization of a Conceptual Rainfall-Runoff Model using Evolutionary Computing methods

Application of artificial intelligence (AI) in hydrologic modeling is receiving increasing attention to explore new optimization approaches to the complex nonlinear high-dimensional models. This paper presents the application of two evolutionary computing (EC) methods to calibrate the parameter of a conceptual rainfall runoff model (i.e., the Tank model). Tank model has been selected as for several reasons including its simplicity in presenting the runoff processes, its flexible adaption and its capability to produce real catchment hydrograph if suitable parameters' calibration is attained. To this end, two global optimization methods (GOM), the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), are used to optimize the Tank model parameters, applied to actual rainfall runoff hydrograph. The performance of two methods have been compared under different scenarios and environments based on two objective functions namely, the root mean square error (RMSE) and Nash-Sutcliffe model efficiency coefficient (NSE). The results proved the capability of GOM to calibrate the 4-stage Tank model with 16 parameters, even under insufficient data availability (as in the case of UAE watersheds). Both techniques performed satisfactorily with slight superiority of GA over the PSO. Also, NSE proved that it can be considered a superior criterion, to use as objective function, compared to RMSE. The research concludes by recommending the best optimized 16 parameters of the tank model for the selected watershed and provides promising results for UAE watersheds.
A Hybrid Algorithm for Sparse Antenna Array Optimization of MIMO Radar

A sparse array is designed to achieve a narrower beam without increasing hardware costs. To reduce the peak side-lobe level and the grating lobe in the desired ambiguity-free region, a hybrid algorithm combining particle swarm optimization (PSO) and convex optimization is presented. While the main beam scans in the desired region, the positions and excitation of array elements are alternately optimized by PSO and convex optimization, respectively. The simulation results show that, compared with the PSO algorithm alone, the hybrid algorithm obtains the lower peak side-lobe level. Furthermore, the side lobes remain almost the same in the desired region when the beam scans. A multiple-input multiple-output (MIMO) radar prototype equipped with the designed sparse array is presented. The experimental result shows that two closely placed moving targets can be separated in two-dimension (2-D) by the MIMO system, which certificates the effectiveness of the proposed algorithm in actual scenarios.
TransNUNet: Using Attention Mechanism for Whole Heart Segmentation

Cardiac CT segmentation of the whole heart is one of the very complex and key technologies in the auxiliary treatment of cardiovascular diseases. It can help doctors analyze the lesion and other key areas. Because the data of cardiac CT images have the characteristics of small data volume and large size, there are higher requirements for the processing speed and segmentation accuracy of the algorithm. This research proposes a semantic segmentation network based on transformer. This method uses TransUNet as the main architecture, introduces an attention mechanism on its basis, and improves the loss function. Our experiment achieved a Dice score of 0.921 on the dataset from MM-WHS 2017 Challenge, and the results show that the method has good performance.
Overbooking Risk of Civil Aviation Transportation

Based on the experiences of managing risks of civil aviation companies, a Value at Risk (VaR) procedure was proposed to control overbooking risk.The calculation method of extra number of single line and the representation of VaR of multi line were given respectively. One whole company's VaR in a day was obtained from analyzing the relativity of return about different flights. Finally, an example on the risk analysis of one scheduled flight from Beijing to Hongkong was given to demonstrate the feasibility of VaR in the risk management of civil aviation transportation.
IoT Cybersecurity: On the Use of Machine Learning Approaches for Unbalanced Datasets

Machine learning can effectively be used to detect cyberattacks in IoT networks by learning patterns from previous attack datasets. Unfortunately, datasets used for training machine learning models to detect cyberattacks are almost always unbalanced. As training methods usually try to minimise the loss function by correctly classifying the instances of the majority class, the minority class instances are more likely to be misclassified. This paper aims to develop an insight into the effectiveness of two different approaches for handling unbalanced datasets – weighted loss function, and synthetic minority oversampling technique (SMOTE) in enhancing the capacity of two machine learning algorithms – artificial neural network (ANN) and light gradient boosting model (LGBM) to correctly classify minority class instances. The results suggest that both SMOTE and weighted loss function enhance the recall rate for minority classes significantly, however, it comes at the cost of slightly reduced precision. Moreover, it is found that LGBM, being an ensemble classifier, has an inherent capacity of learning from unbalanced data and hence, outperforms ANN in detecting minority class instances.
Selective Flushing of Modified Data for Smartphone Buffer Cache Management

Buffer cache plays an important role to hide the slow performance of smartphone storage systems. However, as buffer cache consists of volatile DRAM, data may be lost upon a sudden power failure situation. To cope with this situation, data in the buffer cache are periodically flushed to storage. Although periodic flushing improves the reliability of smartphone data, it degrades the efficiency of buffer cache greatly as it amplifies write traffic to storage. In this paper, we show that a small size of persistent memory can eliminate the periodic flushing overhead of smartphone buffer cache by making use of some distinct write I/O behaviors of smartphones. Specifically, our write I/O analysis from popular smartphone applications shows that a limited number of hot data account for most file writes in smartphones, whereas a large portion of file data accessed in smartphones are written only once. Based on this observation, we present the selective flushing policy that adds small persistent memory to buffer cache and keeps frequently modified data in persistent memory without flushing to storage. We perform storage flushing only for written-once data, which are prohibited from entering persistent memory for space efficiency. By replaying popular smartphone application I/O traces, we show that the proposed selective flushing policy reduces the storage write I/O by 25.8% on average and up to 37.8%.
Enhancing Object Clarity In Single Channel Night Vision Images Using Deep Reinforcement Learning

This paper implements a system of enhancing single channel night vision images using reinforcement learning approach and optimizing pixel prediction using q-table. We implemented some models to learn and process a small static images dataset using a reward bias q-table in a reinforcement learning architecture thus optimizing computational complexities and requirements of large dataset with the help of q-table. It also outperformed with respect to existing CNN models like SRCNN. Where SRCNN is observed to generate a PSNR of 24. S13 on average at 256 batch size. Our system generated a PSNR of 24.1 on average with results in a 10.29% increase of relative efficiency at 3000 epoch. It has shown a 10.39% and 10.36% increase of efficiency with respect to VDSR (at 12S batch size) model and DRCN (at filter number 16) model respectively.
Bipartite link prediction improvement using the effective utilization of edge betweenness centrality

Link prediction has become increasingly popular in recent years. Bipartite networks are still of interest for link prediction researchers, because many real-world networks have the bipartite property. Traditional unsupervised link prediction methods for bipartite networks only consider local properties of the network, neighborhood and degree. In this paper, we first propose a simple formula for edge betweenness centrality in bipartite networks as a global property, and then suggest a new ranking score for link prediction in bipartite networks based on the effective combination of local and global properties, BWX. We will test our proposed method with five baseline link prediction scoring functions (JI, PA, AA, RA, and CN) and five bipartite datasets. Results show the superiority of the proposed method in the case of two popular evaluation metrics, AUC and precision. Future investigation can be performed for other global properties of the networks, such as bipartite closeness centrality. Also, combination strategies of local and global network specifications may be studied more. Finally, comparing the results with state-of-the-art unsupervised algorithms can be a good future direction.
Exopolysaccharide Produced by Probiotic Bacillus albus DM-15 Isolated From Ayurvedic Fermented Dasamoolarishta: Characterization, Antioxidant, and Anticancer Activities

An exopolysaccharide (EPS) was purified from the probiotic bacterium Bacillus albus DM-15, isolated from the Indian Ayurvedic traditional medicine Dasamoolarishta. Gas chromatography-mass spectrophotometry and nuclear magnetic resonance (NMR) analyses revealed the heteropolymeric nature of the purified EPS with monosaccharide units of glucose, galactose, xylose, and rhamnose. Size-exclusion chromatography had shown the molecular weight of the purified EPS as around 240 kDa. X-ray powder diffraction analysis confirmed the non-crystalline amorphous nature of the EPS. Furthermore, the purified EPS showed the maximum flocculation activity (72.80%) with kaolin clay and emulsification activity (67.04%) with xylene. In addition, the EPS exhibits significant antioxidant activities on DPPH (58.17 ± 0.054%), ABTS (70.47 ± 0.854%) and nitric oxide (58.92 ± 0.744%) radicals in a concentration-dependent way. Moreover, the EPS showed promising cytotoxic activity (20 ± 0.97 μg mL–1) against the lung carcinoma cells (A549), and subsequent cellular staining revealed apoptotic necrotic characters in damaged A549 cells. The EPS purified from the probiotic strain B. albus DM-15 can be further studied and exploited as a potential carbohydrate polymer in food, cosmetic, pharmaceutical, and biomedical applications.
Socio-Ecological Effects on the Patterns of Non-native Plant Distributions on Hainan Island

Non-native plants spread to recipient areas via natural or human-mediated modes of dispersal, and, if the non-native species are invasive, introduction potentially causes impacts on native plants and local ecosystems as well as economic losses. Therefore, we studied the diversity and distributional patterns of non-native plant species diversity in the tropical island province of Hainan, China and its relationships with environmental and socioeconomic factors by generating a checklist of species and subsequently performing an analysis of phylogenetic diversity. To generate the checklist, we began with the available, relevant literature representing 19 administrative units of Hainan and determined the casual, naturalized, or invasive status of each species by conducting field surveys within 14 administrative units. We found that non-native plants of Hainan comprise 77 casual species, 42 naturalized species, and 63 invasive species. Moreover, we found that non-native plant species had diverse origins from North and South America, Africa, and Asia and that the most common species across administrative areas belong to the plant families Asteraceae and Fabaceae. Moreover, the numbers of non-native species distributed in the areas of Hainan bording the coast arer greater than those within interior areas of the province. Among the coastal areas, Haikou has the highest species richness and, simultaneously, the highest values for significantly, positively correlated predictor variables, population and GDP (R2 = 0.60, P < 0.01; R2 = 0.64, P < 0.01, respectively). In contrast, the landlocked administrative units of Tunchang and Ding’an have the smallest number of non-native species, while their populations are less than a quarter of that of Haikou and their GDP less than one tenth. Among natural environmental variables, we determined that the number of non-native species had the strongest correlation with the minimum temperature in the coldest month, which predicts a smaller number of non-native species. Additionally, non-native species are primarily distributed in urban and rural built-up areas and agricultural areas; areas that are dominated by human activities. Overall, our study provides a working checklist of the non-native plants of Hainan as well as a theoretical framework and reference for the control of invasive plants of the province.
Genome-wide Identification, Expression, and Functional Analysis of MdMSI Genes in Apples (Malus domestica Borkh.)

The multicopy suppressor of IRA (MSI) is a subfamily of WD40 repeat proteins, which is widely involved in plant growth and development. In order to explore the function of MdMSI members in abiotic stress, we identified eight MSI gene family members from the Malus × domestica reference genome. They were distributed on six chromosomes, and they had similar secondary and tertiary structures. We found a variety of regulatory elements in response to hormones and abiotic stress in MdMSI promoters. Through qRT-PCR analysis, it was revealed that MdMSIs were expressed in all tissues, especially in roots. The analysis results also revealed that the expression of MdMSIs was induced in varying degrees under salt, drought stress, and ABA treatments. Furthermore, we obtained the overexpression of MdMSI1-1 transgenic apple calli and Arabidopsis. The overexpression of MdMSI1-1 in calli and Arabidopsis played a negative regulatory role in salt stress response. Our work laid a foundation for further verifying the function of MSI genes under abiotic stress in apples.
Improving Imaging of the Brainstem and Cerebellum in Autistic Children: Transformation-Based High-Resolution Diffusion MRI (TiDi-Fused) in the Human Brainstem

Diffusion-weighted magnetic resonance imaging (dMRI) of the brainstem is technically challenging, especially in young autistic children as nearby tissue-air interfaces and motion (voluntary and physiological) can lead to artifacts. This limits the availability of high-resolution images, which are desirable for improving the ability to study brainstem structures. Furthermore, inherently low signal-to-noise ratios, geometric distortions, and sensitivity to motion not related to molecular diffusion have resulted in limited techniques for high-resolution data acquisition compared to other modalities such as T1-weighted imaging. Here, we implement a method for achieving increased apparent spatial resolution in pediatric dMRI that hinges on accurate geometric distortion correction and on high fidelity within subject image registration between dMRI and magnetization prepared rapid acquisition gradient echo (MPnRAGE) images. We call this post-processing pipeline T1 weighted-diffusion fused, or “TiDi-Fused”. Data used in this work consists of dMRI data (2.4 mm resolution, corrected using FSL’s Topup) and T1-weighted (T1w) MPnRAGE anatomical data (1 mm resolution) acquired from 128 autistic and non-autistic children (ages 6–10 years old). Accurate correction of geometric distortion permitted for a further increase in apparent resolution of the dMRI scan via boundary-based registration to the MPnRAGE T1w. Estimation of fiber orientation distributions and further analyses were carried out in the T1w space. Data processed with the TiDi-Fused method were qualitatively and quantitatively compared to data processed with conventional dMRI processing methods. Results show the advantages of the TiDi-Fused pipeline including sharper brainstem gray-white matter tissue contrast, improved inter-subject spatial alignment for group analyses of dMRI based measures, accurate spatial alignment with histology-based imaging of the brainstem, reduced variability in brainstem-cerebellar white matter tracts, and more robust biologically plausible relationships between age and brainstem-cerebellar white matter tracts. Overall, this work identifies a promising pipeline for achieving high-resolution imaging of brainstem structures in pediatric and clinical populations who may not be able to endure long scan times. This pipeline may serve as a gateway for feasibly elucidating brainstem contributions to autism and other conditions.