Miracle-dz/UniFAR-FTUs huggingface dataset:
Characteristics of Ferroelectric Logic Gates Using a SPICE-Based Model

A SPICE-based model of an n-channel ferroelectric field effect transistor has been developed based on both theoretical and empirical data. This model was used to generate the I-V characteristic of several logic gates. The use of ferroelectric field effect transistors in memory circuits is being developed by several organizations. The ability of FFETs to have different characteristics depending on the initial polarization can be used to create logic gates. These gates can have properties not available to standard CMOS logic gates, such as memory, reconfigurability and memory. This paper investigates basic properties of FFET logic gates.
A Hybrid Model to Predict Formulation Dependent Granule Growth in a Bi-Component Wet Granulation Process

In this study, a hybrid modeling framework was developed for predicting size distribution and content uniformity of granules in a bi-component wet granulation system with components of differing hydrophobicities. Two bi-component formulations, (1) ibuprofen-USP and micro-crystalline cellulose and (2) micronized acetaminophen and micro-crystalline cellulose, were used in this study. First, a random forest method was used for predicting the probability of nucleation mechanism (immersion and solid spread), depending upon the formulation hydrophobicity. The predicted nucleation mechanism probability is used to determine the aggregation rate as well as the initial particle distribution in the population balance model. The aggregation process was modeled as Type-I: Sticking aggregation and Type-II: Deformation driven aggregation. In Type-I, the capillary force dominant aggregation mechanism is represented by the particles sticking together without deformation. In the case of Type-II, the particle deformation causes an increase in the contact area, representing a viscous force dominant aggregation mechanism. The choice between Type-I and II aggregation is determined based on the difference in nucleation mechanism that is predicted using the random forest method. The model was optimized and validated using the granule content uniformity data and size distribution data obtained from the experimental studies. The proposed framework predicted content non-uniform behavior for formulations that favored immersion nucleation and uniform behavior for formulations that favored solid-spreading nucleation.
Electrical and chemical diagnostics of transformers insulation. A. Aged transformer samples

This paper describes the application of two relatively new diagnostic techniques for the determination of insulation condition in aged transformers. The techniques are: (a) measurements of interfacial polarization spectra by a DC method; and (b) measurements of molecular weight and its distribution by gel permeation chromatography. Several other electrical properties of the cellulose polymer were also investigated. Samples were obtained from a retired power transformer and they were analysed by the developed techniques. Six distribution transformers were also tested with the interfacial polarization spectra measurement technique, and the molecular weight of paper/pressboard samples from these transformers were also measured by the gel permeation chromatography. The variation of the results through different locations in a power transformer is discussed in this paper. The possible correlation between different measured properties was investigated and discussed in this paper.
Own-Party Bias: How Voters Evaluate Electoral Outcomes

Abstract A voluminous literature documents that citizens' perceptions of democracy are shaped by electoral victories and defeats, but what reasoning do citizens use to evaluate parties as winners or losers? Drawing on research on partisan-motivated reasoning, I propose an own-party bias in winner–loser evaluations according to which voters evaluate the electoral fate of their party more favourably than that of other parties. Data gathered in the aftermath of the Danish parliamentary election in 2015 support this expectation. Citizens are more inclined to interpret the election outcome as successful for their preferred party, regardless of the actual election result. This is more pronounced the stronger their partisan attachment and among the less politically knowledgeable, who also assign less importance to objective indicators of electoral success. The findings have implications for our understanding of electoral winners and losers and of how electoral results shape party support and polarization.
A Study on Image Segmentation Method for Image Processing

Image processing techniques are essential part of the current computer technologies and that it plays vital role in various applications like medical field, object detection, video surveillance system, computer vision etc. The important process of Image processing is Image Segmentation. Image Segmentation is the process of splitting the images into various tiny parts called segments. Image processing makes to simplify the image representation in order to analyze the images. So many algorithms are developed for segmenting images, based on the certain feature of the pixel. In this paper different algorithms of segmentation can be reviewed, analyzed and finally list out the comparison for all the algorithms. This comparison study is useful for increasing accuracy and performance of segmentation methods in various image processing domains.
The Most Relevant Factors Affecting the Perioperative Death Rate in Patients with Acute Coronary Syndrome and COVID-19, Based on Annual Follow-Up in the ORPKI Registry

Background: The COVID-19 pandemic is significantly affecting the functioning of the entire healthcare system. The disease itself may be associated with thromboembolic complications. The purpose of this study is to compare patients with acute coronary syndrome (ACS) and patients with ACS who were diagnosed with COVID-19 in terms of their clinical profile, management, treatment complications, and prognosis. Methods: We analyzed 47,940 cases of patients treated for ACS in 2020, including 44,952 patients (93.8%) who were not diagnosed with COVID-19 and 2988 patients (6.2%) who tested positive for COVID-19. Results: Patients with COVID-19 were significantly more likely to experience out-of-hospital sudden cardiac arrest (7.9 vs. 1.1%; p < 0.0001) and be transported directly to a catheterization laboratory (21.3% vs. 8.1%; p < 0.0001). Mortality was significantly higher in this group (0.9% vs. 0.4%; p < 0.0001). The risk of perioperative death was increased by age over 65 years, use of glycoprotein IIb/IIIa inhibitors (GPI IIb/IIIa), femoral access, critical left main stem coronary artery (LMCA) vascular lesions, ST elevation myocardial infarction (STEMI), and no-reflow phenomenon. Conclusions: Despite the pandemic, patients with COVID-19 were treated equally to healthy patients. Efficient organization of the healthcare system allowed the prompt transportation of patients to catheterization laboratories. The study group was characterized by a worse prognosis that was affected by multiple factors.
The Real Time System of Hair Detection in Mushroom Based on Computer Vision

By analysing the Mushroom image, the paper puts forward a kind of line-structure extraction algorithm combination of local gray value and continuity of line direction . After the operations in many aspects of basis image processing, such as gray-scale, denoising , segmentation, contour detection and morphological, this article has developed a set of hair detection system based on computer vision for the Mushroom. The experimental results show this system could well meet the actual needs, and has a broad market prospect.
Developing classification-based named entity recognizers (NER) for Sambalpuri and Odia applying support vector machines (SVM)

This paper demonstrates the development of named Entity Recognizers (NER) applying Support Vector Machines (SVM) for Sambalpuri and Odia. The Sambalpuri corpus amounts to 112k word tokens out of which 5,887 are named entities. On the contrary, 250k ILCI corpus has been applied for Odia out of which 18,447 tokens are named entities. The former accurately recognizes 96.72% whereas the latter provides 98.10% accuracy.
A validation system for data preservation in HEP

Preserving data from past experiments and preserving the ability to perform analysis with old data is of growing importance in many domains of science, including High Energy Physics (HEP). A study group on this issue, DPHEP, has been established in this field to provide guidelines and a structure for international collaboration on data preservation projects in HEP. This contribution presents a framework that allows experimentalists to validate their software against a previously defined set of tests in an automated way. The framework has been designed with a special focus for longevity, as it makes use of open protocols, has a modular design and is based on simple communication mechanisms. On the fabrics side, tests are carried out in a virtual environment using a cloud infrastructure. Within the framework, it is easy to run validation tests on different hardware platforms, or different major or minor versions of operating systems. Experts from IT or the experiments can automatically detect failures in the test procedure by the help of reporting tools. Hence, appropriate actions can be taken in a timely manner. The design and important implementation aspects of the framework are shown and first experiences from early-bird users will be presented.
Oil spill trajectory prediction assessment using multi model ocean currents - case study of MT Dawn Oil spill 2017

An assessment was carried out to judge the performance of the modeled ocean currents in oil spill trajectory prediction. Ocean circulation is the key factor in determining the drift pattern of the spilled marine oil pollutant. General National Oceanic and Atmospheric Administration Operational Modeling Environment (GNOME), an oil spill trajectory model, in diagnostic mode was set for simulating drift pattern of Heavy Fuel Oil (HFO). On 28 January 2017, 0345 hrs, Indian Standard Time (IST), approximately 196.4 MT of HFO was spilled due to vessel collision. The oil spill model was set and run during 28-Jan-2017 to 05-Feb-2017 with 196 tons of HFO. Wind velocity fields were obtained from European Centre for Medium-Range Weather Forecasts (ECMWF). The modeled ocean currents were obtained from High resolution Operational Ocean Forecasting and reanalysis System (HOOFS) with two model set ups such as Indian ocean (IO) and Bay Of Bengal (BOB). Ocean current pattern were also obtained from Hybrid Co-ordinate Ocean Model (HYCOM) and Global Ocean Data Assimilation System (GODAS) based Modular Ocean Model (GM4P1). The oil drift patterns were simulated individually for the spillage due to MT Dawn vessel, by forcing GNOME with the above said wind and ocean currents. Radar data obtained for 29-Jan-2017, from Sentinel -1A was processed for detecting oil slicks. The respective drift patterns obtained were compared individually with the oil slick signatures of Sentinel -1A on 29-Jan-2017. It was found that the drift pattern obtained while using the ocean currents of HOOFS_BOB was in better agreement with that of the observed slicks. Unlike other oil drift patterns, offshore spread of the slicks are well captured while using the ocean currents of HOOFS_BOB. This paper illustrates the method of oil spill trajectory prediction using various ocean currents and validating the simulated drift with the ground truth. It also emphasize the need of using various modeled ocean currents in assessing the performance of oil spill trajectory model.
Energy Management in High RER Multi-Microgrid System via Energy Trading and Storage Optimization

The increased addition of DERs, their intermittent nature, and the high cost of ESSs are the key hurdles for interconnected multi-microgrids to operate economically. It is important to have a system that is simple to adopt, computationally inexpensive, does not share private data. In this paper, an energy management scheme is proposed for multiple interconnected renewable energy resources within the multi-microgrid paradigm. The core objectives aimed at minimizing the operational cost of each microgrid and utilize RER most economically without any load interruptions. The proposed scheme is performed in two steps. In the first step, the load is shifted in accordance with the daily price curve via resource scheduling i.e., charging and discharging of battery storage, day-ahead forecast of RER generation, and respective loads. In the second stage, the cost of energy is further reduced using opportunistic trading with neighboring microgrids in real-time. The mismatch between forecasted generation and load is also adjusted via either energy trading or with efficient scheduling of available power that is stored during off-peak hours. The effectiveness of the proposed scheme is applied to a test system consisting of four interconnected microgrids. Results indicate that daily average power imported from the utility grid and peak demand is changed from 103 kW to 162 kW, and from 661 kW to 527 kW, respectively. Likewise, as per comparison, significantly favorable results have been achieved in terms of efficient available resources utilization, market trading, and reduction of the respective bills.
Dilated and soft attention‐guided convolutional neural network for breast cancer histology images classification

Breast cancer is one of the most common types of cancer in women, and histopathological imaging is considered the gold standard for its diagnosis. However, the great complexity of histopathological images and the considerable workload make this work extremely time‐consuming, and the results may be affected by the subjectivity of the pathologist. Therefore, the development of an accurate, automated method for analysis of histopathological images is critical to this field. In this article, we propose a deep learning method guided by the attention mechanism for fast and effective classification of haematoxylin and eosin‐stained breast biopsy images. First, this method takes advantage of DenseNet and uses the feature map's information. Second, we introduce dilated convolution to produce a larger receptive field. Finally, spatial attention and channel attention are used to guide the extraction of the most useful visual features. With the use of fivefold cross‐validation, the best model obtained an accuracy of 96.47% on the BACH2018 dataset. We also evaluated our method on other datasets, and the experimental results demonstrated that our model has reliable performance. This study indicates that our histopathological image classifier with a soft attention‐guided deep learning model for breast cancer shows significantly better results than the latest methods. It has great potential as an effective tool for automatic evaluation of digital histopathological microscopic images for computer‐aided diagnosis.
Optimization of texture profile analysis parameters for commercial guava preserve

Submitted on September 22 , 2020 and accepted on February 14 th , 2021. 1 Departamento de Ciência dos Alimentos, Universidade Federal de Lavras, Lavras, Minas Gerais, Brazil. marieleengalimentos@gmail.com; vasconcelosmariaufla@gmail.com; joaodedeus@ufla.br; sborges@dca.ufla.br; fqueiroz@ufla.br 2 Programa de Pós Graduação em Saúde e Nutrição, Escola de Nutrição, Universidade Federal de Ouro Preto, Ouro Preto, Minas Gerais, Brazil. ana.clara2@aluno.ufop.edu.br 3 Departamento de Agricultura, Universidade Federal de Lavras, Lavras, Minas Gerais, Brazil. paulanogueiracuri@yahoo.com. br 4 Departamento de Alimentos, Escola de Nutrição, Universidade Federal de Ouro Preto, Ouro Preto, Minas Gerais, Brazil. patricia.pereira@ufop.edu.br *Corresponding author: patricia.pereira@ufop.edu.br Optimization of texture profile analysis parameters for commercial guava preserve
Assessing the impacts of projected climate changes on maize (Zea mays) productivity using crop models and climate scenario simulation

ABSTRACT Context. Investigating agronomic responses of dryland maize (Zea mays L.) systems under global change could provide important insights in designing climate-resilient cropping systems. Aims and methods. In this study, we integrated Agricultural Production Systems sIMulator (APSIM) with Representative Concentration Pathways 8.5 and 20 Global Climate Models to systematically: (1) calibrate and validate APSIM using large-field study conducted in East-Central Texas; (2) evaluate the impacts of climate change on maize productivity and risks; and (3) investigate the variations in growth stage lengths. Key results. Results indicated that APSIM simulated grain yield, biomass production, precipitation productivity (PP; kg ha-1 mm-1) and developmental stage transition agreed well with observation (NRMSE < 14.9%). Changes in temperature and precipitation shortened growing seasons and affected available water, resulting in widely varied yield and PP. Mean grain yield changed from -34.8 to +19.7%, mean PP were improved 9.2-36.5%. The grain production could be maintained at least the standard of 75% of historical in most cases, but with greater risks for achieving higher threshold (50% of baseline). Finally, simulations indicated shortened days (4-13 days) for reaching key developmental stages for maize. Conclusions and implications. The results advocate adoptions of management practice that incorporating early sowing, irrigations at sowing/VT stages, and selections of late-maturing cultivars for better sustainability and higher productivity.
Oral History and Organizational Research: Challenges of Building Knowledge about the Past

Abstract Oral history has been increasingly used in management research in recent years, bringing to the forefront the view of individuals about past organizational phenomena. However, this use has not yet fully explored the construction of knowledge about the past. Instead, it has focused on studying the present and, therefore, hardly distinguishes oral history from qualitative methods such as case studies and in-depth interviews. How then should we use oral history and its historiography that has made it quite distinctive in history? How should we use individuals’ views to construct new knowledge of the past? This paper addresses these two questions, advocating for the use of oral history both as a theoretical-methodological approach and subfield of history, as well as firmly engaged with historical organizational studies. To that end, we review the trajectory of oral history, then we analyze 16 Brazilian papers on oral history, highlighting the distinctive characteristics of the approach, and, finally, we present research possibilities in historical organizational studies.
Toward a methodology for studying the application of open source innovation practices in non-software domains

This article represents an initial step in the development of a research methodology that will be applied later to study open source innovation practices in company projects where the released technology or asset is other than software code. A brief review of four possible analytical approaches is presented based on the research literature on open innovation, horizontal user innovation networks, business ecosystems and value creation and appropriation. The OpenAccess project of the Silicon Integration Initiative (Si2) is considered as a potential case study of non-software open source project.
Developmental trajectories of spatial‐sequential and spatial‐simultaneous working memory in Down syndrome

Abstract Background Working memory (WM) is generally considered an area of weakness in the cognitive profile associated with Down syndrome (DS). The great majority of studies explored WM in this population through a comparison with typical development (TD) on the basis of mental age or developmental level. However, it is also relevant to understand how these skills develop and whether such development could be more related to chronological or developmental level. In the present study, we explored cross‐sectional developmental trajectories of spatial‐sequential and spatial‐simultaneous WM in individuals with DS across chronological age and developmental level. Typically developing children (TD) of similar mental age were also included as a comparison group. Methods Eighty‐four individuals with DS (aged between 7 and 30 years) and 327 children with TD (aged between 4 and 8 years) were administered with tasks to assess spatial‐sequential and spatial‐simultaneous WM, together with tasks to assess both general verbal and spatial developmental levels. Results and conclusion Performance in spatial‐simultaneous WM task was lower compared with spatial‐sequential WM task in both groups. In the case of individuals with DS, the developmental trajectories of chronological age are better described through a segmented model showing increased performance until approximately 13 years of age, followed by a rather flat progress. In the case of TD children, developmental trajectories are better described through a linear model in the spatial‐simultaneous WM task when chronological age is considered; in the spatial‐sequential WM, the increase in performance with age was however characterised by a discontinuity at age 6. The increase in performance followed a linear pattern in both groups (DS and TD) without substantial differences between the types of measure used (verbal vs. spatial) when the developmental level is considered.
Organizational Learning Capacity as sustainable Competitive Advantage: Role of Leader’s Emotional Intelligence and Resistance to Change on Organizational Learning Capacity

This research study is based on the investigation and validation of Organizational Learning Capacity with regards to leaders in educational sector as source of Competitive Advantage to the Higher Education Institutions and testing the impact of leader’s Emotional Intelligence (EI) and Resistance to Change to enhance Organizational Learning Capacity (OLC). This study can be the hallmark for the HEI’s for gaining competitive advantage through their human capital.The results shows that EI has significant contribution towards the OLC and when measured together with the trait of resistance to change the results significantly transformed which suggest that RTC negatively and significantly affect the relationship between EI and OLC therefore, to control and reduce the aspect of resistance to change by enhancing and investing in the trait of Emotional Intelligence of the individuals and leaders is suggested to gain the benefits of OLC to become superior in the industry.