Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Self-Calibration Technique for Linear Array Based on Modified MVDR Adaptive Beamformer

Adaptive smart antenna is a combination of antenna arrays and beamforming algorithm. One of the important adaptive beamforming algorithms is Minimum Variance Distortionless Response algorithm (MVDR), which has optimal weight and very good output Signal-to-Interference Noise Ratio (SINR). MVDR is very sensitive to the steering vector errors that may occur due to manufacturing or calibration errors at antennas of the array. Signal suppress (selfnull) phenomena appeared due to this error, where mismatch between presume and actual signals Direction of Arrival (DOA) occurred. This error also may be occurred during the work time of smart antenna and after the final calibration test due the hard weather changes. According to this weather change, the shape of array changes and leads to error in the antenna interdistance. This leads to major degradation in the MVDR beamformer performance. In this paper, a novel diagnoses algorithm of smart antenna self-calibration is proposed. This algorithm aimed to overcome the calibration/manufacturing or shape distortion errors that may essentially exist or may occur by weather changes effect. According to this algorithm the reference element is re-localized to be in the middle of the Uniform Linear Array and the rest antenna elements distributed identically at the two sides of this reference. This configuration generates two identical sub-arrays at the two sides of the reference. Due to identical performance of the two subarrays, the inter-distance change between array’s antenna elements easily distinguish. Simulation results showed that the proposed algorithm has an effective response against steering vector errors since the desired signal is served by the main radiation beam while the interference is blocked by null.
Secagem de fatias de abboras (Cucurbita moschata, L.) por conveco natural e forada

Besides its low price, Pumpkin (Cucurbita moschata, L.) is an important source of provitamin-A, and when dehydrated it offers different options of utilizations and consumption. Natural and forced convection drying were compared according to the drying degree shrinkage as a function of temperature, and air velocity and product dimensions. The obtained results showed that slices with the volume of 6.25 cm3 in forced convection oven at 50 °C and at the velocity of 5.5 x 10-4 ms-1 resulted in a lower shrinkage products, so these conditions were recommended.
Physiological-Based Smart Stress Detector using Machine Learning Algorithms

This paper is focused on the development of an intelligent system to identify if one person is stress or not stress using physiological parameters through machine learning. In this study, the dataset was acquired from three hundred (300) male and female participants ages 18 to 25. The gathered dataset is composed of five (5) features (i.e. heart rate, systolic blood pressure, diastolic blood pressure, galvanic skin response and gender). An intelligent system was developed using machine learning algorithms for classification such as Linear Regression (LR), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) using Python IDE with sci-kit learn machine learning libraries. Google Colaboratory (Colab) was utilized to perform optimization using Gridsearch to identify the best parameters of each algorithm. Feature selection methods are implemented to identify the most significant features related to stress condition of one person. After optimization, the results showed that SVM has the best performance to classify if one person is stress or not stress with optimized training-testing accuracy score of 95.00% - 96.67%.
Understanding the Determinants of Infrastructure-as-a Service-Based E-Learning Adoption Using an Integrated TOE-DOI Model: A Nigerian Perspective

Cloud computing is a state-of-the-art paradigm that gives enormous advantages to Higher Education Institutions in developing countries to improve their education. In this regard, Cloud computing can change the way wherein access to and utilization of learning content by Higher Education Institutions in developing countries, in particular Nigeria, can be effectively accessed in a pay-per-use method. Consequently, lessening the expense of actualizing an independent e-learning system. While it has been extensively embraced for e-learning in first world nations, its adoption in Nigeria is exceptionally low. There is as well lack of empirical studies which investigate the factor that influence its adoption from the viewpoint of the Information and Communication Technology directorates in the Nigerian HEIs. Therefore, this study sought to explore the factors that influence the adoption of cloud computing for e-learning by amalgamating the Diffusion of Innovation as well as the Technological, Organizational, Environmental theory. A disproportionate stratified random sampling technique was used. The data were collected using a quantitative research design from 454 ICT-directorates managers in Nigerian universities. The response was assessed using SPSS and SmartPLS. The findings suggested that relative advantage, cost savings, competitive pressure, and service provider support should be considered by decision-makers in the Nigerian universities to promote the easy diffusion and adoption of the cloud services for e-learning.
Object Grasping and Manipulating According to User-Defined Method Using Key-Points

This paper presents an approach for grasping objects by the robotic manipulator at predefined points by userdefined grasping methods for cases of arbitrary position and orientation of objects. The key idea is to implement a detection algorithm for user-defined key-points on the reference object image. As these points can be detected and localized on the object surface, it is possible to generalize the initial sequence of robotic manipulations, pre-recorded for the single scene, to the set of situations with arbitrary object orientation and location within the workspace. The robotic arm had a depth camera, mounted on the end effector for 3D key-points detection and anthropomorphic robotic hand.
Optimized Design of Carrier Injection Terminal for Reliable Low-Temperature Poly-Si TFTs Under Dynamic Hot-Carrier Stress

Dynamic hot-carrier (HC) degradation can be effectively suppressed in four-terminal low-temperature poly-Si (LTPS) thin film transistors (TFTs) with a carrier injection terminal. In order to enhance the reliability of the four-terminal LTPS TFTs, geometry effect of the carrier injection terminal, such as width and location, on the dynamic HC degradation is systematically studied. It shows that a wider carrier injection terminal, which provides more carriers injection, can suppress the degradation more effectively. As for the location, if the injection terminal is adjacent to the edge of the drain depletion region, which extends into the channel at the falling time of the gate voltage pulses, the four-terminal TFTs can provide the best immunity to the dynamic HC degradation. The proposed optimization scheme can greatly enhance the reliability of LTPS TFTs, and the mechanism can be understood based on the nonequilibrium p-n junction degradation model.
The polarity protein Angiomotin p130 controls dendritic spine maturation

Our results furthermore identify Lats1-mediated serine 175 (S-175) phosphorylation of AMOT-130 as a critical regulatory step for its role in developing spines. Collectively, these findings provide insight into the understanding of how AMOT-130 loss-of-function influences synapse structure and may link to congenital spine defects observed in autism-spectrum disorder pathology. The actin cytoskeleton is essential for the structural changes in dendritic spines that lead to the formation of new synapses. Although the molecular mechanisms underlying spine formation are well characterized, the events that drive spine maturation during development are largely unknown. In this study, we demonstrate that Angiomotin (AMOT-130) is necessary for spine stabilization. AMOT-130 is enriched in mature dendritic spines and functions to stabilize the actin cytoskeleton by coupling F-actin to postsynaptic protein scaffolds. These functions of AMOT are transiently restricted during postnatal development by phosphorylation imposed by the kinase Lats1. Our study proposes that AMOT-130 is essential for normal spine morphogenesis and identifies Lats1 as an upstream regulator in this process. Moreover, our findings may link AMOT-130 loss and the related spine defects to neurological disorders.
Design and development of (1.2-2.7) GHz GaN HEMT based broadband power amplifier

Design, fabrication, assembly and S-parameters measurement results of a (1.2-2.7) GHz Microwave integrated circuit (MIC) power amplifier using gallium nitride (GaN) based high electron mobility transistor (HEMT) is presented. The output power of 38.5 dBm to 39.2 dBm with over 10 ±1 dB power gain is achieved. The power added efficiency (PAE) of 17% to 33% has been achieved over the band of (1.2-2.7) GHz with drain bias voltage of 25 Volts. The amplifier is operated in CW mod. MIC power amplifier has been designed using CGH40010F device. The main challenge in designing the broadband high power amplifier with flat gain is to achieve proper source and load impedance matching. The major application of wideband power amplifier is in electronic warfare and high end security communication.
A study on the interaction between two weevils Neochetina eichhorniae and N. bruchi, and the mirid Eccritotarsus catarinensis, as biological control agents of water hyacinth Eichhornia crassipes

Water hyacinth Eichhornia crassipes (Martius) Solms-Laubach is an invasive floating aquatic weed pest of South American origin that was distributed around the world primarily because of its attractive purple flowers. Due to its rapid growth, it is an important aquatic weed in tropical and subtropical regions worldwide. In Benin, it affects boat traffic, fishing and domestic use of water by about 200,000 inhabitants in several communities. When it became a serious problem in the late 1980s, the only control option approved by the government of Benin was biological control because it is sustainable and environmentally friendly. This dissertation examined the biological control agents released against the weed. Two studies were conducted on the interaction between arthropod biological control agents, using the mirid Eccritotarsus catarinensis (Carvalho), and two weevils, Neochetina eichhorniae (Warner) and N. bruchi Hustache. The third study, investigated the displacement of E. catarinensis in the laboratory culture by an indigenous mirid Nycticapsus sp. The objective of the first study was to determine the influence of adult weevil feeding scars on the mirid released to complement biological control by the weevil. Result show that adults and nymphs of E. catarinensis had high mortality (3-5 folds) on plants with high levels of old feeding scars by adult N. eichhorniae and N. bruchi. In contrast, mirids survived well on plants with recent adult weevil feeding scars or on undamaged plants. These results indicate that the mirid is compatible with the two weevil species under certain conditions and will therefore contribute to enhanced biological control of the weed.
Physical and molecular processes controlling the formation, structure and breakdown of the salivary pellicle

Objectives: Despite the importance of the pellicle in oral physiological and pathological processes, an understanding of the fundamental physical and molecular mechanisms underlying the structural formation and breakdown of this protein film remains unresolved. Therefore the work carried out herein attempts to elucidate the structural changes that the pellicle undergoes upon exposure to intrinsic and extrinsic factors. Such as; the role that mucins play in pellicle formation, the effect of a changing acidic environment or the structural changes that occur upon contact with dentifrice components. Methods: in vitro adsorbed pellicles were formed from the saliva (stimulated parotid saliva and stimulated whole mouth saliva) of 14 healthy volunteers, and studied using techniques including a quartz crystal microbalance with dissipation monitoring and a dual polarisation interferometer. The pellicles were then exposed to certain food and oral hygiene ingredients to observe the physical (e.g. surface mass, density, thickness and viscoelasticity) and chemical (e.g. protein composition) modifications that the pellicle undergoes when challenged in this way. Results: Mucins present in whole mouth saliva were shown to help form a more viscous pellicle compared to a pellicle formed from mucin free saliva (parotid saliva). Whereas, the pellicle formed from saliva containing 10 mM CaCl2 was more diffuse and less stable compared to pellicles formed from saliva containing 1mM CaCl2. Structural changes in the pellicle also took place upon exposure to pH4 and pH3 3 citrate buffers, changes that may be related to the isoelectric points of the proteins present in the pellicle. Finally, the polyanionic STP molecule was shown to be more effective than SDS at displacing pellicle form hydroxyapatite. Conclusion: This research demonstrated that the composition of saliva has an important effect on the physical properties of the adsorbed pellicle; and lays the foundations as to how regulating the calcium concentration of saliva provides a mechanism that can control the physical properties of the in-vitro formed pellicle. In addition, the structural changes that the pellicle undergoes under differing acidic environments, and upon exposure to components of oral hygiene products, were observed to help understand the mechanisms underlying the formation and breakdown of the salivary pellicle.
Semisupervised Center Loss for Remote Sensing Image Scene Classification

High-resolution remote sensing image scene classification is a scene-level classification task. Driven by a wide range of applications, accurate scene annotation has become a hot and challenging research topic. In recent years, convolutional neural networks (ConvNets) have achieved promising performance among a variety of supervised classification methods. However, due to the lack of clearly labeled remote sensing images, it may be difficult to further improve the performance of scene classification. To address this issue, we propose a novel semisupervised center loss for scene classification. The main innovation of our method is to develop a cooperative framework of supervised and unsupervised branches in an end-to-end way. Specifically, we consider the class centers as guiding factors between the supervised and unsupervised branches. The supervised branch relies on a small number of labeled samples to generate class centers, which serve as initialization centers for the unsupervised branch. Meanwhile, the unsupervised branch utilizes the easily available remote sensing images to correct the class centers for enhancing the discriminative power of supervised ConvNets. Experimental results on three public benchmarks have indicated that the proposed method is superior to supervised center loss based methods.
Video Based Vehicle Counting Using Deep Learning Algorithms

Traffic density in roads has been increasing day by day which needs intelligent transportation system that can handle the traffic. Traffic management has become inevitable for smart cities. The enormous increase in vehicle numbers has generated more pressure to manage traffic congestion especially during peak hours. If the traffic congestion at a particular point of time can be found, then that information can be useful for managing the traffic in different lanes and change the traffic light cycle dynamically according to the vehicle count in different lanes. In recent years video surveillance and monitoring has been gaining importance. Video can be analyzed which can be used to find the traffic density. Many useful information can be obtained by video processing like real time traffic density. Vehicle counting can be done by detecting the object, tracking it and then finally counting the objects. Many different techniques are available for object detection and tracking. Deep learning techniques for object detection led to remarkable improvements compared to conventional image processing techniques by removing the weakness in the conventional techniques. This paper provides a survey on various techniques available for vehicle detection and tracking.
Human Motion Target Recognition Using Convolutional Neural Network and Global Constraint Block Matching

The traditional human behavior recognition algorithm is easy to ignore the spatial constraint problem of feature blocks, which leads to poor recognition effect and low correct rate. Therefore, we proposed a human motion target recognition algorithm based on Convolution Neural Network (referred to as the “CNN”) and global constraint block matching. First, key frames of the human motion video were extracted, second, the local feature and global feature of key frames were analyzed, and CNN was used to perform feature fusion, then, according to the result of the feature fusion, a feature block was formed and the closest matching feature block is obtained, using the definition of spatial constraint, we considered the spatial data of human motion in the vertical direction, calculates the spatial constraint weight, and further completes the matching. Finally, the score of matching block and the spatial constraint weight were calculated, and the human motion targets are recognized based on the cumulative score. The experimental results show that the proposed algorithm has a high key frame extraction accuracy of more than 90% and less time consumed in feature fusion, high matching accuracy of feature blocks of more than 80%, and high feature blocks, the F-measure of human behavior recognition is 0.95 on average, and the overall recognition performance is good.
Molecular model of fission yeast centrosome assembly determined by superresolution imaging

of proteins to each other within the SPB and how each protein is assembled into a new structure during SPB duplication. These data enabled us to build the first comprehensive molecular model of the S. pombe SPB, resulting in structural and functional insights not ascertained through investigations of individual subunits, including functional similarities between Ppc89 and the budding yeast SPB scaffold Spc42, distribution of Sad1 to a ring-like structure and multiple modes of Mto1 recruitment.
Research on the Influencing Factors of Leading Technology Diffusion Based on Technology Acceptance Model

With the increasing use of traditional energy in the world, the problems of traditional energy exhaustion, environmental pollution and unsustainability are aggravated. Many countries in the world have turned their attention to the development of new energy and tried to find a clean, pollution-free and sustainable way. Therefore, the technology diffusion in the new energy industry has attracted more and more attention. This paper uses technology acceptance model to explore the influencing factors of technology diffusion in new energy industry: technology acceptance, technology readiness. We also use perceived usefulness and perceived ease of use to measure the relationship between technology acceptance and adoption behavior, which verified the positive correlation between technology acceptance and technology diffusion in new energy industry, as well as the regulatory role of Technology readiness. Meanwhile, in order to promote the effective diffusion of new energy industry leading technology, we give relevant countermeasures and suggestions from the perspective of enterprises and government based on the empirical results.
Robust Feature Extraction and Ensemble Classification Against Cyber-Physical Attacks in the Smart Grid

Intrusion detection systems (IDS) are crucial in threats monitoring for the cyber-physical security of electrical power and energy systems in the smart grid with increasing machine-to-machine communication. However, the multi-sourced, voluminous, correlated, and often noise-contained data, which record various concurring cyber and physical events, are posing significant challenges to the accurate distinction by IDS among events of inadvertent and malignant natures. To tackle such challenges, this paper proposes a robust end-to-end framework based on Stacked Denoising Autoencoder (SDAE) and Ensemble Machine Learning to extract new noise and attack-informed feature sets from cyber-physical system data and incorporate different sources of information for reliable event classification. The proposed framework first leverages SDAE to create lower-dimensional features that allow reconstruction of a noise-free input from noise-corrupted perturbations. By combining attack and noisy inputs, we extracted new, automatically-engineered features that can preserve and present information on normal, fault, and attack events against different synthetic but realistic noises for better classification. Considering the heterogeneous nature of the inputs, which are composed of PMU measurements, system logs, and IDS alerts, we further introduced ensemble learning-based multi-classifier classification with the Extreme Gradient Boosting (XGBoost) technique to classify the samples based on the SDAE-extracted features. Normalization and oversampling were also both performed to improve the uniformity and balance of the data. On a realistic dataset of 37 sub-types of normal, fault, and attack collected from co-simulations on a hardware-in-the-loop (HIL) testbed security testbed, the results have shown that the proposed SDAE+XGBoost solution achieves over 90% classification accuracy with the SDAE features and ensemble classifiers, an effective 8% increase over the state-of-the-art.
The daughter centriole controls ciliogenesis by regulating Neurl-4 localization at the centrosome

The two centrioles of the centrosome differ in age and function. Although the mother centriole mediates most centro-some-dependent processes, the role of the daughter remains poorly understood. A recent study has implicated the daughter centriole in centriole amplification in multiciliated cells, but its contribution to primary ciliogenesis is unclear. We found that manipulations that prevent daughter centriole formation or induce its separation from the mother abolish ciliogenesis. This defect was caused by stabilization of the negative ciliogenesis regulator CP110 and was corrected by CP110 depletion. CP110 dysregulation may be caused by effects on Neurl-4, a daughter centriole–associated ubiquitin ligase cofactor, which was required for ciliogenesis. Centrosome-targeted Neurl-4 was sufficient to restore ciliogenesis in cells with manipulated daughter centrioles. Interestingly, early during ciliogenesis, Neurl-4 transiently associated with the mother centriole in a process that required mother–daughter centriole proximity. Our data support a model in which the daughter centriole promotes ciliogenesis through Neurl-4–dependent regulation of CP110 levels at the mother centriole.