Miracle-dz/UniFAR-FTUs huggingface dataset:
Soluble salts, copper, zinc, and solids constituents in surface runoff from cattle manure compost windrows

Larney, F. J., Olson, A. F. , Miller, J. J. and Tovell, B. C. 2014. Soluble salts, copper, zinc, and solids constituents in surface runoff from cattle manure compost windrows. Can. J. Soil Sci. 94: 515-527. Composting has become widely adopted by the beef cattle feedlot industry in southern Alberta. Compost windrows subjected to heavy rainfall can lead to runoff whose properties may vary with compost maturity. A rainfall simulator generated runoff on days 18, 26, 40, 54, 81, 109 and 224 of manure composting. Runoff was collected in timed 5-L increments to 30 L, creating the variable “time during runoff event” (TDRE). Calcium, K and S showed significant maturity×TDRE interactions, especially earlier in the composting process, e.g., on day 18, Ca values increased from 34 mg L-1 for the initial 0- to 5-L runoff increment to 43 mg L-1 for the final 25- to 30-L increment. Most significant changes in runoff concentrations occurred between days 26 and 40, e.g., Cu levels fell by 67% and Zn levels by 78%. Even though compost Cu and Zn concentrations were higher during the latter stages of composting, their transport potential in runoff was curtailed due to binding with stable organic matter (OM). The C:N ratio of runoff solids decreased from 10.5 on day 18 to only 4.9 on day 224, suggesting the transport of very stable OM after compost curing. The study showed that runoff quality was influenced by compost maturity, which has implications for the timing of rainfall events relative to the maturity spectrum and the potential risk to surface water quality if runoff is not contained.
Sentiment analysis for product rating using a deep learning approach

Sentiment analysis is a study about opinions, emotions, and attitudes of the people towards an event or issue. Social networking is an invaluable medium for individuals to express their thoughts and views about any subject or topic, contributing to massive quantities of unstructured knowledge. These emotions can be processed and examined to analyze and obtain insights. Therefore, several machine learning and Natural Language Processing (NLP) based methods were used to examine these opinions. Because of the shifts in the sequential order, stream lengths, and complex logic, the exact sentiments in the consumer feedback are still difficult. Recently, deep learning methods have been used to attain improved results. The most common forms of deep learning method used are the Recurrent Neural Network (RNN) and the Convolutional Neural Network (CNN). Long short-term memory (LSTM), particularly with attentive layers, pays greater focus to the sentiment impact. LSTM has an advantage over alternative RNNs and other deep learning approaches because of relative insensitivity to gap length. Deep learning approaches have better accuracy over existing state-of-the-art approaches because of their ability to handle extensive realtime data and the power of feature extraction, but there is still room for improvement. This paper has used Long Short-Term Model (LSTM) model to predict the customer review’s opinion, attaining an accuracy of 93.66%. Furthermore, comparative analysis of the deep LSTM model with existing has been presented.
Antioxidant and gastroprotective properties of the fruits of Thuja occidentalis Linn

The study was designed to investigate the antioxida nt and gastroprotective properties of methanolic extract of the fruits of Thuja occidentalis (Cupressacae). Gastroprotective properties of extracts of the fruits of Thuja occidentalis (200 and 400 mg/kg b.w ., orally) were evaluated by employing pylorus ligated rat and aspirin was used to induce acute gastric ulcer model. The in-vitro antioxidant activity of the extract was assayed usi ng 2, 2-Diphenyl-1-1-picrylhydrazylhydrate (DPPH). There was a significant dose dependent decrease in ulcer index in the rats treated with methanolic extract was comparable to the standard drug Omeprazole (20mg/kg. b.w .). The results suggest that Thuja occidentalis possesses significant antioxidant. The extract was found to reduce the volume of gastric acid by 45% (P<0.05) and 69% (P<0.001) in 200 and 400 mg/kg respectively. At 400 mg/kg the rats showed significant regenerative changes of gastric epithelium. The present study confirmed the antiulcer activity of the fruits of Thuja occidentalis, which may be attributed to its antioxidant activity.
Performance comparison of native host and hyper-based virtualization VirtualBox

This paper is related to the issue of comparing the performances of hypervisor-based virtualization, with Oracle VM Virtual Box as a representative, and a native host. The measurements are carried out on the same PC and under the equivalent conditions, using the Filebench software. The used operating system is Ubuntu 20.04. 01. The goal of this paper is to show an impact of one and more VirtualBox virtual machines on the file system performances. The results have been illustrated in graphical form.
Popularizing Antenna Synthesis with Our Solved Problem : Some known unseen parameters revisited

An attempt is made to depict objects that are very obvious but have been managed to lead them to be hidden. But these important parameters are being revisited with our new proposed method of antenna synthesis. These formulations are based on a strong understanding of electromagnetics and the pure sciences of physics. There are too many laws of communications and electronics being matured in our study. It is anticipated that our work will be of value and acceptable to future researchers and industries.
Usage Prediction and Effectiveness Verification of App Restriction Function for Smartphone Addiction

In recent years, there has been a growing problem of smartphone addiction. As the excessive use of smartphones has negatively impacted our daily lives, many apps for reducing smartphone addiction have been developed around the world. In this study, we focus on the app restriction function, which is one of the key features of digital medicines for smartphone addiction, and analyze the usage of the function and verify its effectiveness. The results showed significant differences in both psychological and behavioral aspects between those who used the app restriction function and those who did not. Specifically, we found that the app restriction function was more likely to be used by those who were more aware of their smartphone addiction. We also found that the app restriction function was effective in lessening smartphone usage time, especially when the smartphone addiction is relatively moderate.
Collaborative learning for improving intellectual skills of dropout students using datamining techniques

In each year millions of students drop out without completing their educational course. In such a case, both the individual student and institution will have an effect of dropping out. The proposed research work pays significant research attention towards analysing the higher education and upper primary students to identify their behaviour, which leads them to discontinue in the early stage and stop the dropout by taking necessary action towards the dropout reason. This in turn results in the lack of skilled workspace and weaken the productive system of the country and also student dropouts are more likely to become as the recipients of unemployment subsidies. This research is more focused on the dimension reduction techniques, which involves both the feature selection and feature extraction methods. It also aims to implement better prediction in identifying dropout students and implement collaborative learning with engagement. When PCR measures, the key elements do not look at the reaction but rather at the predictors (by looking for a linear combination of the predictors that has the highest variance). It assumes that, the answer is correlated with the linear combination of the predictors with greatest variance. It is presume that, the regression plane differs when selecting the main variable in the other orthogonal direction, along the line and it does not differ. The second path is disregarded by selecting one component and not the other. Principal Component Analysis (PCA) is a method used for extracting features that use orthogonal linear projections to capture the database. This is illustrated in two phases. First phase is the development of dimension reduction using PCA to identify an accurate prediction variance of dropout students by using various ML algorithms and the second phase involves the developing of collaborative learning with engagement through social media and improves their intellectual skills by performing SVM hypothesis test.
An Ensemble Approach to Multi-Source Transfer Learning for Air Quality Prediction

Declining quality of air is one of the major challenges faced by cities across the globe today. A product of rapid industrialization and urbanization, air pollution has quickly manifested itself as an existential threat to human life in major urban centres of the world. Developing economies such as India and China are at the forefront in bearing the brunt of this phenomenon. Intelligent predictive analysis is critical for framing policies that can help in controlling the severe effects of air pollution. Although Deep Learning architectures have proven to be effective in reliably forecasting the concentration of hazardous air particles like PM2.5, their predictive capabilities are significantly reduced when the amount of data available is not enough for effective training. Newer monitoring stations often lack the resources and/or personnel to reliably collect meteorological and environmental data, thus suffering from crippling data-insufficiency issues which hinder the applicability of forecasting models. This paper proposes an ensemble approach for Multi-Source Transfer Learning, with the aim of mitigating the data shortage issue. The proposed method generates a cumulative prediction by transferring the knowledge learned from multiple source stations to a given target station, thereby better utilizing the data that is readily available from nearby stations to boost prediction performance.
DC and linearity performance of a novel Si0.6Ge0.4/Ge Dopingless Tunnel FET for ultra-low voltage applications

The Charged-Plasma (CP) concept-based Dopingless tunnel field-effect transistor (DLTFET) has emerged as a potential metal-oxide-semiconductor-field-effect transistor (MOSFET) successor, demonstrating immunity towards random dopant fluctuations (RDFs) and extremely low leakage currents. With that in mind, we investigate a Si0.6Ge0. 4/Ge hetero-junction DLTEFT with a high-κ dielectric (TiO2), represented by HJ HD DLTFET throughout this manuscript. The drain of HJ HD DLTFET is made up of Si0.6Ge0. 4 and source, and the channel is made up of Ge. Additionally, a high-κ dielectric has been applied to suppress off-state current and provide better insulation. Moreover, a comparative analysis is performed between conventional DLTFET and HJ HD DLTFET using the ATLAS device simulator. The simulation results for HJ HD DLTFET offers an improvement in on-state current (~1000 μA/μm), improved ION/IOFF ratio (~1014), and subthreshold swing (~36.14 mV/decade) at 1.5 V of the gate voltage (VG) and 0.3 V of the drain voltage (VD). Additionally, the comparative analysis of both devices is done for linearity in terms of second- and third-order voltage intercept points (VIP2, VIP3), third-order intercept input power (IIP3), intermodulation distortion (IMD3), and 1-dB compression point. The appreciable attributes demonstrated by HJ HD DLTFET promotes its adeptness in low-voltage and low-power applications.
Different Convolution Neural Network Based Models for Garbage Detection: A Review

Over the last couple of years, deep learning technology made tremendous progress to computer vision. The convolution neural networks have attained state-of-art performance on tasks such as the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). The deep convolution neural network models are trained by deep learning algorithms. In this paper firstly describe the development of deep learning and convolution neural network. The author introduces the basic model structure of the different CNN models, feature extraction, and max-pooling operation in the convolution neural network. This paper is basically focused on the deep learning techniques related to the convolution neural network like Le-Net 5, AlexNet, GoogleNet, etc. Then, the research development trend of deep convolution neural network models related to deep learning in object detection are reviewed.
Garbage Zero (Garb0): An IoT Framework for Effective Garbage Management in Smart Cities

Today, there is a significant trend of blending optimized waste management strategies with low power, low-cost Internet of Things (IoT) solutions. This paper discusses and investigates Garbage Zero (Garb0), an indigenous product developed for outdoor garbage bins. Garb0 aims at developing a power efficient IoT based real-time solid waste monitoring solution that helps cities to optimize their waste collection and maintain clean and green environment. The Garb0 sensor module can be installed within public garbage bins to provide real time fill level data. The related information regarding the current fill level of the garbage bin will be transmitted to a cloud using the Low Power Wide Area Networks (LPWAN) based Long Range (LoRa) technology [1][2], which will be further processed and forwarded to garbage collector’s mobile application and to the municipal dashboard for updating the status. Optimized schedule and route for collecting the garbage from the garbage bins will be generated on the garbage collector’s app and accordingly directs the driver to follow the most efficient route. The main focus of this paper is on the hardware and software architecture implemented to achieve low cost, low power consumption and long battery life.
Optimized Global Maximum Power Point Tracking of Photovoltaic Systems Based on Rectangular Power Comparison

Global maximum power point tracking (GMPPT) refers to the extraction of the maximum power from photovoltaic (PV) modules in real time under changing ambient conditions. Due to the installation of PV systems in densely built-up areas, partial shading scenarios are commonplace. Commercially established GMPPTs suffer from low tracking speeds and inefficiency. A novel GMPPT algorithm is proposed here based on the rectangular power comparison (RPC), which exploits the fundamental relationship between the shading factor, the bypass diode voltage and the global maximum power point. The entire theoretical formulation of RPC is presented systematically for the first time. This method boasts of increased conversion speeds owing to the precomputation of the module voltage versus the shading factor correlations using the regression of diode model from the experimentally obtained bypass diode characteristics. The proposed method is simple to implement with the computational complexity of order $n$ , which represents the number of uniquely shaded PV modules in a series string. The proposed approach addresses the much-needed intersection problem between the distributed and centralized PV systems and therefore targets PV strings which are most common in residential and small to medium scale commercial PV installations world over. The proposed approach is validated with the in-house developed prototype hardware set-up and software control implementation giving a 99% tracking efficiency with a recorded tracking time of 10 ms. The experimental results show 50 times improvement in speed and 95% increase in power gain as compared to the other popular existing methods namely scanning based GMPPT and local MPPT methods respectively, with negligible computational burden and less than 0.5% added cost to the conventional PV energy conversion system.
Sparse Code based Indexing for Image Retrieval

Recent technological developments have led to the rapid creation of large datasets of images at a low cost. Thus, it is necessary to develop methods capable of retrieving images from gigantic datasets quickly and reliably. Given the high computational cost of this task, it is necessary to create a compact representation of each element in the set of images to be searched. Sparse representation makes it possible to perform searches. Sparse signal representation is a powerful tool for capturing, representing and compressing signals in high dimensions. This collection is usually called a “dictionary”, and its elements are called “words” or “Atoms”. The idea of sparse representation represents images through a linear combination of few atoms in the dictionary. This research work is proposed to build an inverted index using sparse coding to ensure the relevant image retrieval. This work is devoted to studying and developing methods for image retrieval using dictionary learning. The proposed research work has developed a framework for dictionary learning and coefficient learning simultaneously. Here, KSVD based approach is used for dictionary learning and three optimization schemes viz. OR, LARS, Elastic-Net are used to optimize the sparse codes. From various aspects, the proposed framework shows consistent performance over existing approaches on two publically available & widely acceptable datasets, viz Coil and Caltech-256.
Automatic Detection of Ictal Activity in EEG Channels using Synchronization Attributes

To aid doctors with the process of scanning long-term Electroencephalogram (EEG) records for seizure detection, automatic seizure detectors (ASD) have been proposed. ASD have many clinical uses and have gained much recent interest in the past few decades. However, due to high computational loads of the ASD algorithms; their real-time application has been hindered. The aim of this work is to build a detector that has low complexity and can detect seizures with high sensitivity and minimum latency. To this end, this paper proposes a patient-specific seizure onset detector that uses a single feature based on neural synchrony as it has been shown that once the brain approaches ictal activity, neural activity becomes less chaotic and more synchronous. Leveraging on this phenomena, the condition number is utilized to measure the synchronization between EEG channels. The developed detector has three main stages: preprocessing, condition number calculation and classification. After filtering an input EEG record, the condition number of an EEG window is computed and fed into a classifier that determines whether the current window is normal or abnormal. Classification was done by two forms of a support vector machine (SVM) classifier; namely, binary SVM and a one-class SVM. The developed detector was evaluated using the CHB-MIT dataset and was evaluated using 10 patients from this dataset. The proposed detector achieved a sensitivity of 97% and a maximum false positive rate of five false alarms per hour. The performance of the detector is comparable to seizure detectors that use multiple features; but lightweight for use for real-time processing of EEG data.
Normalized Maximal Margin Loss for Open-Set Image Classification

This work aims to address the image classification problem under open-set protocol: classes in test set do not appear in the training set. Intuitively, convolutional Neural Network (CNN) with softmax loss is a straight-forward solution. However, the unknown class (is not predefined in the training set) makes the boundaries of intra-class and inter-class more blurred, which brings more challenges for image classification. Although some softmax variants, such as center loss, CosFace loss etc., focus on learning discriminative features by minimizing the intra-class distance, they do not explicitly maximize inter-class distance, which is more important for open-set problem verified by our experiments. Besides, even though deep metric learning, such as with the contrastive loss and the triplet loss, can learn discriminative features of intra-class and inter-class, it needs a time-consuming image sampling process during training. In this paper, we propose a novel normalized maximal margin (NMM) loss for open-set image classification, which not only explicitly minimizes intra-class distance and maximizes inter-class distance, but also defines their margins. Specially, after analyzing the advantage of angular space that the softmax loss normalized by the feature and weights through geometric interpretation, we make NMM work in angular space. Then, the validity of NMM for discriminative features learning is demonstrated from the view of geometric interpretation as well. After that, we innovatively determine the upper bound of inter-class margin by theoretical analysis. Finally, extensive experiments are conducted on popular datasets: CIFAR-100 (object recognition), ImageNet (image classification), LFW (face recognition) and MSMT17 (person re-identification) to verify the effectiveness of NMM. The experimental results show that NMM achieves very competitive performance.
Noise-Parameter Measurements of Very-Low-Noise Amplifiers at Ambient and Cryogenic Temperatures

Radio-telescope receivers are developed to sense very weak astronomical signals. The sensitivity of a radio telescope is inversely proportional to its noise temperature, which is largely dependent on the amount of noise generated in the receiver low-noise amplifier (LNA). In addition, a radio telescope sensitivity is also proportional to its collecting area; however, it is very costly to enhance sensitivity by increasing the collecting area. Given the high costs associated with increasing the collecting area, the development of methods of reducing LNA noise becomes critical to optimizing the operation of radio telescopes. Such LNA-noise reduction techniques must first be verified via laboratory measurements and then subsequently verified using field measurements on the telescope.
Design and Implementation of High-speed Low Voltage 4x4 Vedic Convolutor

Digital signal processors involve different operations to compute problems, convolution being one of the most important and fundamental among them. This paper proposes a CMOS-based linear, discrete convolutor system designed to increase speed of operation at a considerably low voltage level. Vedic multipliers formulated on the basis of Urdhva-Tiryagbhyam Sutra have been used as sole multiplication method at all levels of computation. Carry Look-ahead Adders (CLAs) have been employed as the chief methodology of addition. This amalgamation of Vedic multipliers and CLAs helps to curtail propagation delay values to bring about faster calculation. Moreover, power-delay product or PDP is found to be greatly lessened in this arrangement of a high-speed, low voltage convolutor structure simulated using 22nm CMOS technology. A thorough evaluation of the proposed architecture has been laid out in this paper.
Detection of early emphysema regions in HRCT images using Fuzzy-C Mean

Chronic obstructive pulmonary disease (COPD) is an irrevocable disorder and has a high mortality rate. It can be diagnosed by using several methods, e.g., traditional radiology imaging, clinical details, and pulmonary function tests (PFTs). Conventional radiology detects the emphysema in its late phases when the disease has already damaged an enormous lung field region. So, high resolution computed tomography (HRCT) is used to precisely estimate the disease's severity. Early emphysema, typically less than 10 mm in diameter, often got missed during a visual assessment of HRCT scans. So an attempt has been made to target this issue. To the best of our knowledge, early emphysema regions' detection using HRCT images was not addressed. This paper proposes a novel method by incorporating details of low attenuation areas (LAA) into Fuzzy-C Mean (FCM) algorithm to identify the uncertain regions of early emphysema regions proficiently in HRCT images. Experimental results are authenticated by using ground truth marked by the expert radiologist. The investigational outcomes show that the proposed method gives results close to expert radiologist's manual demarcations.