Miracle-dz/UniFAR-FTUs huggingface dataset:
Early detection of digital mammogram using kernel extreme learning machine

An automated computer‐assisted medical diagnosis that combines latest medical approaches and the advanced machine learning algorithms is a very crucial multidisciplinary technology, generating correct and noninvasive diagnoses of multiple diseases like breast cancer. The work proposed in this article focuses on the development of a biomedical computer‐assisted diagnosis model that can classify digital mammography as normal (healthy) or abnormal, and further, as malignant or benign. The proposed approach employs the discrete Chebyshev transform to extract the features. Then, the kernel principal component analysis technique is used to extract the discriminating features from the original feature vector. Subsequently, an optimized kernel extreme learning machine is proposed as a classifier to detect the tumors present in the mammographic images. Because the efficiency of the proposed classifier depends on its characteristic kernel variable, the main idea of the present work is to choose the most appropriate features from the downsized feature set and simultaneously obtain the optimized value of the aforementioned parameter. To validate the efficiency of the proposed work, the proposed scheme is performed on two publicly available data sets, namely the Mammographic Image Analysis Society data set and the INbreast data set. From the experimental analysis and its results, it is showed that for both normal–abnormal and malignant–benign classification, the proposed approach results in accuracy of 100% for the first data set. However, in the case of malignant–benign classification, the proposed approach gives an accuracy of 99.93% for the second data set. Further, it is also observed that the proposed approach exhibits highest performance as compared to that of the other approaches. Additionally, the ANOVA test is evaluated to demonstrate that the achievement of the proposed approach is significantly good than that of the other existing approaches.
Lack of Functional Trehalase Activity in Candida parapsilosis Increases Susceptibility to Itraconazole

Central metabolic pathways may play a major role in the virulence of pathogenic fungi. Here, we have investigated the susceptibility of a Candida parapsilosis mutant deficient in trehalase activity (atc1Δ/ntc1Δ strain) to the azolic compounds fluconazole and itraconazole. A time-course exposure to itraconazole but not fluconazole induced a significant degree of cell killing in mutant cells compared to the parental strain. Flow cytometry determinations indicated that itraconazole was able to induce a marked production of endogenous ROS together with a simultaneous increase in membrane potential, these effects being irrelevant after fluconazole addition. Furthermore, only itraconazole induced a significant synthesis of endogenous trehalose. The recorded impaired capacity of mutant cells to produce structured biofilms was further increased in the presence of both azoles, with itraconazole being more effective than fluconazole. Our results in the opportunistic pathogen yeast C. parapsilosis reinforce the study of trehalose metabolism as an attractive therapeutic target and allow extending the hypothesis that the generation of internal oxidative stress may be a component of the antifungal action exerted by the compounds currently available in medical practice.
Chemical Solution Deposition of Single‐Phase BiFeO3 Thin Films on Transparent Substrates

The production of high‐quality BiFeO3 thin films on cost‐effective transparent electrodes for visible light harvesting applications and devices remains a challenge. Here, the production of single‐phase nanostructured BiFeO3 thin films via chemical solution deposition (CSD) on transparent conductive fluorine‐doped tin oxide FTO glass substrates is reported. It is shown that BiFeO3 is of high purity using a variety of analytical tools and that the as‐obtained BiFeO3 thin films have a single‐grain single‐domain structure exhibiting ferroelectric switching under poling. The BiFeO3 samples show visible light absorption with a bandgap of 2.7 eV under all processing conditions. By changing the annealing atmosphere, it was possible to modify the photocurrent produced, which were (at 1.23 VNHE) 0.07 (O2‐annealed), 0.02 (air‐annealed), and 0.01 mA cm−2 (Ar‐annealed). This indicates a change in the mobile carriers available. The results show that it is possible to produce single‐phase BiFeO3 on a transparent conductive electrode system with controllable photoconductivity.
Post-harvest Green Pea Pod Rot Caused by Sclerotinia sclerotiorum in Korea

In June 2017, in Gangneung, Gangwon Province, South Korea, green pea pods exhibited post-harvest rot symptoms. The fungus was isolated from infected pea pods and cultured on potato dextrose agar for identification. The morphological characteristics were examined, sequences of the internal transcribed spacer region and the β-tubulin (βtub) gene were analyzed, and the pathogenicity was confirmed according to Koch’s postulates. The morphology, phylogenetic analysis, and pathogenicity tests confirmed that Sclerotinia sclerotiorum was the causal agent. This study reports the first case of post-harvest green pea pod rot caused by S. sclerotiorum in Korea.
ANU Centre for European Studies Briefing Paper Series Asylum Policy in the EU: The Case for Deeper Integration

Over the last decade the locus of policy-making towards asylum seekers and refugees has shifted away from national governments and towards the EU as the Common European Asylum Policy has developed. Most of the focus has been on the harmonisation of policies relating to border control, the processing of asylum claims and reception standards for asylum seekers. But this still falls far short of a fully integrated EU-wide policy. This paper examines the basis upon which a joint EU policy can be justified. I then ask whether superior outcomes can be achieved by harmonisation alone or if more centralised policy-making is necessary. I chart the progress of harmonisation and burden-sharing in the development of the Common European Asylum System and explore its effects. I also study the political feasibility of deeper policy integration by analysing public attitudes in the European Social Survey. I conclude that deeper integration is both desirable and politically possible.
Dealing with Post-Kogeko

Inter-organizational systems are only used to some extend within the domain of transportation and logistics. Distributed agent technologies show a large potential to build a new generation of inter-organizational systems that overcome the hurdles earlier technologies were not able to take (2, 4). In this paper we present an ongoing research project in which we try to establish a WebServices based agent platform to be used by a logistics service provider (LSP) in an inter-organizational context - which will connect the LSP's internal processes, with processes that span organizational boundaries and therefore is likely to result in interesting process improvements; cost savings and improved competitive advantage are expected outcomes. This paper describes the process we followed, and gives a glimp of our preliminary findings. We conclude the paper with a short discussion and our research agenda.
A semi‐automatic workflow for plot boundary extraction of irregularly sized and spaced field plots from UAV imagery

Advances in high‐throughput platforms such as UAVs (unoccupied aerial vehicles) facilitate rapid image‐based phenotypic data acquisition. However, existing plot‐level data extraction methods are unreliable if field plots differ in size and spacing, as often occurs in early‐generation plant breeding trials. To overcome the limitations of conventional plot extraction techniques, a combinational approach with both field‐map information and image classification techniques can be used to optimize plot extraction. The objective of this study was to develop a plot boundary extraction workflow for irregularly sized and spaced field plots from UAV imagery using plot spacing data and vegetation index‐based classifiers. An herbicide screening experiment consisting of three replications of 780 lentil (Lens culinaris Medik.) populations was foliar sprayed with saflufenacil. Aerial image acquisition was conducted during the peak vegetation stage using a RedEdge multispectral camera. A semi‐automatic workflow was compiled in eCognition software to extract lentil plot boundaries. Normalized difference vegetation index (NDVI) was calculated to locate the plots with vegetation and those with low NDVI or no vegetation, and pixel resizing based on plot size and orientation was used to draw the plot boundary. The extraction results showed a precise estimation of plot boundary for all the plots with a wide range of herbicide damage, including the plots with complete loss of vegetation. By using a simple convolutional filter (line filter), image thresholding, and pixel resizing, this approach avoided the use of complex algorithm‐based methodologies. Results suggest that this workflow can be extended to a wide range of phenotyping studies.
Toward Predicting Stay Time for Private Car Users: A RNN-NALU Approach

Predicting the stay time of private cars has various applications in location-based services and traffic management. Due to the associated randomness and uncertainty, achieving the promising performance of stay time prediction is a challenge. We propose an RNN-based encoder model to solve this problem, which consists of three components, i.e., an encoder module, an exception module, and an MLP dropout. First, we encode the stay behaviour into hidden vectors at a specific time to avoid the effect of time sparsity. The encoder module utilizes a multilayer perceptron (MLP) to learn spatiotemporal features from the historical trajectory data, such as the inherent relationship between the stop points and corresponding stay time. We proved a linear relationship problem that cannot be ignored in the stay time prediction problem. In particular, we have added basic arithmetic logic units to the network framework to find linear relationships. By reconstructing the basic arithmetic and logical relations of the network, we have improved the ability of the neural network to handle linear relations and the extrapolation ability of the neural network. Our method can remember the number patterns seen in the training set very well and infer this representation reasonably. Moreover, we utilize the dropout technique to prevent the prediction model from overfitting. We perform extensive experiments based on a large-scale real-world private car trajectory dataset. The experimental results demonstrate that our method achieves an RMSE of 0.1429 and a MAPE of 55.8533%. Furthermore, the results verify the effectiveness and advantages of the proposed model when compared with the benchmarks.
Are We Essential, or Sacrificial? The Effects of Felt Public Gratitude on Essential Worker Recovery Activities During COVID-19

COVID-19 has been characterized by unprecedented levels of public gratitude to some, but not all, essential workers. In this research, we integrate insights from the stigmatized occupations and gratitude literature to build theory on the positive and negative relationships between such displays of public gratitude and essential workers’ recovery activities. We argue that felt public gratitude positively relates to adaptive recovery activities (e.g., exercise) and negatively relates to maladaptive recovery activities (e.g., overdrinking). We further explain how felt public gratitude impacts (mal)adaptive recovery activities through (a) felt invisibility and (b) negative/positive affect. We find support for our predictions in a two-wave survey of 186 corrections officers (Study 1) and an experiment with 379 essential workers across a variety of industries (Study 2).
Cascade Fuzzy Neural Network Control for Ball and Beam Systems

In this paper, a ball and beam experiment platform is considered. Based on a fuzzy neural network (FNN), a desired positioning controller can be obtained subject to extraneous disturbances. A FNN structure not only preserves the fuzzy linguistic description logic, but also has the parameter-learning capability of neural networks. In this paper, a cascaded inner-outer loop scheme is constructed for the ball and beam system, where the controller parameters of the inner loop FNN can be adaptively tuned using gradient decent method. Simulation and experimental results indicate that the proposed cascade FNN control scheme can provide better robust responses.
Assessment of Brilliant Green and Eriochrome Black T dyes adsorption onto fava bean peels: kinetics, isotherms and regeneration study

This research aims to assess the employability of fava bean peels (FBP) as low-cost adsorbents in the removal of Brilliant Green (BG) and Eriochrome Black T (EBT) from aqueous solutions and therefore in colored wastewater treatment. Scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR), and pHPZC were used to characterize these materials. The constants of Langmuir, Freundlich, and Dubinin-Radushkevich have all been determined. The adsorption capacity of BG and EBT was 28.14 and 7.91 mg g–1, respectively, at 5 g L–1 FBP dose, 40 mg L–1 initial dye concentration, temperature 20°C, and contact time BG (80 min) and EBT (40 min). The calculated thermodynamic parameters indicated that adsorption of BG was endothermic and exothermic for EBT. The isotherm studies show that the adsorption experimental data of both dyes are fitted well by the Freundlich isotherm model. The adsorption of the two dyes studied on fava bean peels is perfectly described by a pseudo-second-order kinetic model. Cyclic experiments have indicated that the current bio-adsorbent can be effectively reused to remove BG and EBT from aqueous solutions. Removal efficiencies remained above 84% and 10% for BG and EBT respectively even after four adsorption–desorption cycles. As a consequence of these relevant results, our inexpensive and environmentally friendly material could provide enormous application prospects in large-scale wastewater treatment in the industrial level.
Low Voltage Switched Capacitor Circuits for Lowpass and Bandpass ∆Σ Converters

approved: abor C. Temes and Un-Ku Moon The most accurate method for performing analog signal processing in MOS (metal-oxide-semiconductor) integrated circuits is through the use of switchedcapacitor circuits. A switched-capacitor circuit operates as a discrete-time signal processor. These circuits have been used in a variety of applications, such as filters, gain stages, voltage-controlled oscillators, and modulators. A switched-capacitor circuit contains operational amplifiers (opamps), capaciuors, switches, and a clock generator. Capacitors are used to define the state variables of a system. They store charges for a defined time interval, and determine the state variables as voltage differences. Switches are used to direct the flow of charges and to enable the charging and discharging of capacitors. Nonoverlapping clock signals control the switches and allow charge transfer between the capacitors. Opamps are used in order to perform high-accuracy charge transfer from one capacitor to another. The goal of this research is to design and explore future low-voltage switchedcapacitor circuits, which are crucial for portable devices. Low-voltage operation is needed for two reasons: making reliable and accurate systems compatible with the submicron CMOS technology and reducing power consumption of the digital circuits. Redacted for Privacy To this end, three different switched-capacitor integrators are proposed, which function with very low supply voltages. One of these configurations is used to design a lowpass L modulator for digital-audio applications. This modulator is fabricated and tested demonstrating 80 dB dynamic range with a 1-V supply voltage. The second part of this research is to show that these low-voltage circuits are suitable for modern wireless communication applications, where the clock and signal frequencies are very high. This part of the research has focused on bandpass analog-to-digital converters. Bandpass analog-to-digital converters are among the key components in wireless communication systems. They are used to digitize the received analog signal at an intermediate center frequency. Such converters are used for digital FM or AM radio applications and for portable communication devices, such as cellular phones. The main block, in these converters, is the resonator, which is tuned to a particular center frequency. A resonator must be designed such that it has a sharp peak at a specific center frequency. However, because of circuit imperfections, the resonant peak gain and/or the center frequency are degraded in existing architectures. Two novel switched-capacitor resonators were invented during the second part of this research. These resonators demonstrate superior performance as compared to previous architectures. A fourth-order low-voltage bandpass /I modulator, using one of these resonators, has been designed. © Copyright by Mustafa Keskin December 7, 2001 All Rights Reserved Low Voltage Switched Capacitor Circuits for Lowpass and Bandpass i Converters
BACTERIOME ASSOCIATED WITH Rhizophora mangle SEDIMENTS WITHIN BRAZIL SEMI-ARID MANGROVES

Microorganisms play important roles in nutrient cycling in mangrove ecosystems and knowledge on the plant/microorganism association is essential to better understand the functioning of this environment. Rhizophora mangle is the dominant tree species within Brazilian mangroves and little information is available on the microbiota associated with this plant species. In this context, the aim of this study was to survey the taxonomic diversity of bacteria in the R. mangle root zones in mangroves within the semi-arid region of Northeast Brazil submitted to different human disturbances, intending to determine the bacterialfingerprint associated with this habitat. The total DNA extracted from sediments of different mangroves was pooled and used for construction of 16S rDNA cloning libraries, which resulted in 663 sequences with an average size of 809 bp. All mangroves were rich in different phyla of the Bacteria domain, with Acidobacteria, Bacteroidetes, Chloroflexi, and Proteobacteria being detected in all locations. Proteobacteria was dominant in all mangroves, and it was mainly represented by Alpha, Delta, and Gammaproteobacteria. The greatest richness was found in the Timonha river mangrove, with 13 phyla, a location considered more preserved compared to other mangroves. The lowest richness was found in Ceará river mangrove, with only seven phyla. This mangrove is threatened by intense urbanization. The results clearly showed that the taxonomic diversity of bacteria from mangroves subjected to intense urbanization have decreased, highlighting the risks of these changes for the functioning of important microbe-mediated processes and related ecosystem services. Keywords: brazilian mangroves, semi-arid, Rhizophora mangle, rRNA 16S, clone library.
Dual Mode-Multiple Output SEPIC Converter Integrated with Passive Ripple Cancelling Circuit for Standalone PV Energy Harvesting System

This document addresses an exhaustive standalone Photovoltaic (PV) energy harvesting system considering two crucial issues: system efficiency and cost effectiveness. It contributes a compact resolution with a combined feature of Dual Mode-Multiple Output (DMMO) associated with input ripple reduction technique. Control strategy incorporates with aspect of Maximum Power Point Tracking (MPPT) and output voltage levels regulation. A theoretical analysis is conducted to evaluate the effect of ripple current on PV power. Proposed dual mode converter achieves efficiency of 98.36% and 97.76% respectively for mode-1 and mode-2 operation. However, simulation is performed applying MATLAB/SIMULINK tools to analyze the feasibility of the recommended system.
Dynamical electromagnetic actuation system for microscale manipulation

Abstract Electromagnetic actuation systems (EMA) have excelled themselves in microscale manipulation. Yet, the fastened structure of the current systems tethers the controlled workspace. In this paper, a new electromagnetic actuation principle is investigated. The actuator structure consists of a pair of coaxially movable electromagnets integrated to a robotic manipulator. The pair induces a coaxial homogeneous magnetic field or gradient to control the magnitude of the magnetic torque or force by changing the distance between the electromagnets asymmetrically. The robotic manipulator, on the other hand, transports the pair at five degrees of freedom to manipulate a microrobot in 3D space by closed-loop control with integrated vision feedback system. Numerical analyses are performed to investigate the induced electromagnetic field at the symmetrical/asymmetrical configuration of the coaxial pair. Accordingly, a correlation between the magnitude of the magnetic force and the asymmetric distance is obtained for flexible force control. A proof of concept prototype is constructed to validate the proposed actuation principle and evaluate its performance experimentally. The experimental results verify the numerical analysis and show the system applicability of inducing controlled forces on a micro-object in 2D and 3D workspaces at a velocity range of 65 to 157 $\mu$ m/s. Moreover, micromanipulation on a helical route is also demonstrated with an absolute error mean from the reference path of 191 $\mu$ m.
EFS-DNN: An Ensemble Feature Selection-Based Deep Learning Approach to Network Intrusion Detection System

In recent years, the scale of networks has substantially evolved due to the rapid development of infrastructures in real networks. Under the circumstances, intrusion detection systems (IDSs) have become the crucial tool to detect cyberattacks, malicious actions, and anomaly behaviors that threaten the credibility and integrity of information services in networks. The feature selection technologies are commonly applied in various intrusion detection algorithms owing to the potential of improving performance and speeding up decision-making. However, existing feature selection-based intrusion detection methods still suffer from high computational complexity or the lack of robustness. To mitigate these challenges, we propose a novel ensemble feature selection-based deep neural network (EFS-DNN) to detect attacks in networks with high-volume traffic data. In particular, we leverage light gradient boosting machine (LightGBM) as the base selector in the ensemble feature selection module to enhance the robustness of the selected optimal subset. Besides, we utilize a deep neural network with batch normalization and embedding technique as the classifier to improve the expressiveness. We conduct extensive experiments on three public datasets to demonstrate the superiority of the EFS-DNN compared with baselines.