Miracle-dz/UniFAR-FTUs huggingface dataset:
Controller parameter optimization of direct-drive permanent magnet synchronous generator based on SGA-SVM classification

In this paper, a simulation model of direct-drive wind power generation system (WPGS) is established. The training samples of subsynchronous oscillation (SSO) are generated by changing parameters of the grid-side controller during time domain simulation. These samples are classified to be stable and unstable using nonlinear support vector machine (SVM) classification. The simple genetic algorithm (SGA) is used to optimize the hyperparameters of SVM, which improves the accuracy of prediction. Simulation analysis shows that the proposed model provides the possibility to predict the stability of system accurately with the parameter settings of grid-side controller. When the system is predicted as unstable, the grid-side controller parameters can be changed to recommended values to ensure the safety and stability of power system operation.
The Relationship Between Clinical Manifestation and Histopatological Result in Non- Hodgkin Lymphoma: RSUD Dr. Soetomo Surabaya

Non-Hodgkin's lymphoma is a major public health problem with over 14.1 million people are diagnosed with it (2012). In the same year there were 8.2 million deaths due to cancer. The purpose of this study was to determine the relationship between clinical manifestations and the degree of malignancy based on histopathological features in patients with Non-Hodgkin's Lymphoma. This study used a retrospective analytical method with a cross-sectional approach using the patient's medical record at RSUD Dr. Soetomo, Surabaya who was diagnosed with Non-Hodgkin Lymphoma from 1st January 2015 to 31st December 2017. In this study, there were 139 samples include those criteria, with a greater number of male samples (62.6%) compared to women (37.4%). This study showed that 49.3% of patients with non- Hodgkin's lymphoma in RSUD Dr. Soetomo with clinical manifestations without symptoms actually experience malignancy with a high degree, this showed that clinical manifestations without symptoms are not always associated with a low level of malignancy. Then, based on the Chi Square test results obtained p-value of 0.289 (>0.05), so there was no significant relationship between clinical manifestations and the degree of malignancy. Keywords: lymphoma; manifestation; histopatological
A step-by-step tutorial on active inference and its application to empirical data

The active inference framework, and in particular its recent formulation as a partially observable Markov decision process (POMDP), has gained increasing popularity in recent years as a useful approach for modeling neurocognitive processes. This framework is highly general and flexible in its ability to be customized to model any cognitive process, as well as simulate predicted neuronal responses based on its accompanying neural process theory. It also affords both simulation experiments for proof of principle and behavioral modeling for empirical studies. However, there are limited resources that explain how to build and run these models in practice, which limits their widespread use. Most introductions assume a technical background in programming, mathematics, and machine learning. In this paper we offer a step-by-step tutorial on how to build POMDPs, run simulations using standard MATLAB routines, and fit these models to empirical data. We assume a minimal background in programming and mathematics, thoroughly explain all equations, and provide exemplar scripts that can be customized for both theoretical and empirical studies. Our goal is to provide the reader with the requisite background knowledge and practical tools to apply active inference to their own research. We also provide optional technical sections and multiple appendices, which offer the interested reader additional technical details. This tutorial should provide the reader with all the tools necessary to use these models and to follow emerging advances in active inference research.
Clinical spectrum of diabetic retinopathy and its correlation with risk factors

Objectives: To study the clinical spectrum of diabetic retinopathy and its correlation with its risk factors.Material and Methods: A prospective, cross sectional and unmasked study was done in patients of diabetes mellitus presenting in OPD of RIO, PGIMS Rohtak. Total 60 patients were included in the study which were divided into 3 groups each of 20 patients, based on the duration of DM: Group A 10 years. Staging of retinopathy was done on the basis of International diabetic retinopathy and diabetic macular edema severity scale. Demographical factors like age and sex were correlated with duration of DM. Various clinical forms of diabetic retinopathy ranging from no apparent DR to severe NPDR to PDR were correlated to different laboratory parameters like degree of hyperglycemia, Hb, lipid profile, blood pressure, renal function tests to ascertain correlation between them.Results: Total prevalence of diabetic retinopathy in our study was 56.66%. Males were at higher risk in comparison to females. In our study, duration of disease was found to be strongly associated with diabetic retinopathy and macular edema. Hyperglycemia as measured by FBS and HbA1c is found to be an important risk factor in diabetic retinopathy. Hypertension, hypertriglyceridemia, deranged RFT and anaemia is statistically associated with diabetic retinopathy and its severity.Conclusion: Diabetic retinopathy is a leading cause of preventable blindness. Therefore early diagnosis, timely screening and control of diabetes mellitus is of utmost importance.
Breaking down the work from home mindset in a new normal way

With the shocking arrival of Covid, work from Home or Telework has become a buzzword now. The Global COVID-19 Pandemic has changed the world radically. The ridiculous appearance of the pandemic has brought about a spontaneous change in varying social statuses. New Complexities has been authored which were not there in the past, and therefore, business units are compelled to change the working styles. In the period of social separating and distant working, things have been changed a great deal. The business world in a state of uncertainty and fear are shifting to work from home, as it is the only way left for them in the quest for survival in the era of pandemic. Everyone is moving to telework, some are doing it in a planned manner with proper planning on the other hand others are doing it, because it seems the only left out in the situation of lockdown and isolation. Both the employers and the employees are trying to find out the optimum way out, but it will be too early to reach conclusion that telework is the optimum solution and it will be successful. Work from Home has its own intricacies. The perception of the employees and the employers may or may not change with time but one question is needed to be asked and that is, Is Work from is Home is better solution? Also, we need to know the perception of employees towards work from Home mode of performing Jobs. This Paper tries and evaluate the perception and point of view how the respondents felt about working from. The researcher also tried to have a clear understanding of supporting factors that have mad work from a more tolerable way of working and what works against working from home.
Bootstrapping inference of average treatment effect in completely randomized experiments with high-dimensional covariates

Investigators often use regression adjustment methods to analyze the results of randomized experiments when baseline covariates are available. Their aim is to improve the estimation efficiency of treatment effects by adjusting for imbalance of covariates. Under mild conditions, the regression-adjusted average treatment effect estimator is asymptotically normal with asymptotic variance no greater than that of the unadjusted estimator. The asymptotic variance can be estimated conservatively based on residual sum of squares. This article studies alternative inference methods based on the bootstrap and investigates their asymptotic properties under the Neyman–Rubin causal model and randomization-based inference framework. We show that the weighted, residual and paired bootstrap methods provide asymptotically conservative variance estimators that perform at least as good as the estimator based on residual sum of squares. We further provide counterexamples, where the original estimator is asymptotically normal, but the bootstrap counterpart is inconsistent for estimating its limiting distribution. Simulation studies indicate that the paired bootstrap method is preferable, in terms of preserving type I errors, for a small sample size. Finally, our methods analyze HER2+ breast cancer data from the NeOAdjuvant Herceptin trial to examine the effectiveness of trastuzumab in combination with neoadjuvant chemotherapy.
Overview and Prospects of High Power Amplifier Technology Trend for 5G and beyond 5G Base Stations

SUMMARY High power amplifier technologies for base transceiver stations (BTSs) for the 5th generation (5G) mobile communication systems and so-called beyond 5G (B5G) systems are reviewed. For sub-6, which is categorized into frequency range 1 (FR1) in 5G, wideband Doherty amplifiers are introduced, and a multi-band load modulation amplifier, an envelope tracking amplifier, and a digital power amplifier for B5G are explained. For millimeter wave 5G, which is categorized into frequency range 2 (FR2), GaAs and GaN MMICs operating at around 28GHz are introduced. Finally, future prospect for THz GaN devices is described.
FREQUENCY OF FUNGAL MYCOFLORA ON RICE

Introduction: Rice (Oryza sativa L.) is the second best essential cereal crop of the world as well as of Pakistan. There are various fungal pathogens that attack on rice and cause various rice diseases. Methods: Therefore, the present experiments were done to isolated different fungi associated with seeds, stems, and leaves of commercially grown rice varieties and of mainly isolated species. Results: Several fungi, namely, Magnaporthe oryzae, Curvularia lunata, Helminthosporium oryzae, Fusarium moniliforme, Alternaria alternata, Nigrospora oryzae, and Aspergillus niger were isolated from seeds, stems, and affected leaves of rice. Conclusion: In our research, the frequency of fungal mycoflora on rice has been calculated and the maximum isolates of Fusarium spp. have been found.
Modélisation des transferts de chaleur couplés pour la simulation multi-physique des chambres de combustion

Oxycombustion is seen as one mean to attain the wished goals in terms of efficiency optimisation and Greenhouse Effect Gases emissions reduction for industrial furnaces. The extreme operating conditions, high pressure and temperature, lead to a strong interaction between the different phenomena which take place inside the combustion chambe r: Combustion, turbulence and heat transfer. To better design these futur oxyfuel processes, a mean to study the related physics with a reasonable computational cost and return time. Such studies require the use of high-fidelity numerical resolution tools, and in order to model the multi-physics interaction in a cost efficient way, code coupling. The operating conditions being extreme : High pressure and temperature, a strong interaction exists between the different phenomena occuring inside the chamber. To better understand the physics inside oxycombustion chambers,a multiphysics high-fidelity simulation methodology is developped.
Transcriptome-wide responses of aggregates of the diatom Odontella aurita to oil

Diatom aggregates can play an important role in the formation of marine oil snow and the transport of oil from the sea surface to the benthos, yet their molecular response to oil has not been characterized. Here we use RNA-seq to analyze the transcriptome-wide responses of aggregates of the common Gulf of Mexico diatom Odontella aurita exposed to the water accommodated fraction of 2 different types of oil, Macondo surrogate and Refugio Beach oil. We identify a common set of 353 genes that are differentially expressed in response to both Macondo and Refugio oil exposure, relative to controls. Genes related to photosynthesis, and nuclear and ribosomal processes were all down-regulated in oil treatments, while genes related to repairing membrane damage, cellular stress, and the production of exopolymeric substances were up-regulated. Differential expression of genes was often greater in magnitude in the Refugio than the Macondo oil treatment, which may be due to differences in oil concentration in the treatments or to the physiochemical characteristics of the oils. Exposure to Refugio oil induced more severe nucleolar stress and more damage to chloroplasts than the lighter Macondo oil, which triggered an up-regulation of a more diverse suite of stress-response genes.
Structural Metaphors of Headlines of Financial and Economic Articles and Their Translation Strategy: Based on The Economist

In the new era of information and technology, an increasing number of people can get easier access to original western magazines and news reports, thus attaining first-hand information. The Economist , a journal in UK that is read worldwide, includes various sections like World News, Politics, Business, and Finance and Economics. It is necessary to conduct relevant researches on headlines, which provide central ideas of full texts to readers. Structural metaphor, one of three major kinds of conceptual metaphor, is a development of traditional metaphor theories under a cognitive perspective. Due to the wide use of structural metaphors in headlines of articles about business and economics, and the importance of their translation for Chinese readers, the present author intends to discuss the types of structural metaphors and their corresponding translation strategies by taking some of the headlines from “Business” and “Finance and Economics” columns of The Economist as a case study.
Quality and safety evaluation of new tomato cultivars

Tomato (Solanum lycopersicum) is a dietary source of bioactive compounds and breeding programs continuously create new cultivars with different nutritional and organoleptic characteristics. The aim of this work is to provide a quality and safety assessment of new tomato cultivars: Bamano, Dulcemiel and Sugarland. Eight biogenic amines, total phenolic content and antioxidant activity (DPPH and ABTS assays) have been determined. Tyramine was not detected in any samples. Sugarland was characterized by a high content of serotonin (266.87 ± 11.16 mg/kg) and phenolic compounds (303.15 ± 21.62 mgGAE/kg). Moreover, multivariate statistical analyses were applied to the data matrix.
Covariate Selection for Mortgage Default Analysis Using Survival Models

The mortgage sector plays a pivotal role in the financial services industry, and the U.S. economy in general, with the Federal Reserve, St. Louis, reporting Households and Nonprofit Organizations for One-to-Four-Family Residential Mortgages Liability Level at $10.8T in Q3 2020. It has been in the interest of banks to know which factors are the most influential predicting mortgage default, and the implementation of survival models can utilize data from defaulted obligors as well as non-default obligors who are still making payments as of the sampling period cutoff date. Besides the Cox proportional hazard model and the accelerated failure time model, this paper investigates two machine learning-based models, a random survival forest model, and a Cox proportional hazard neural network model DeepSurv. We compare the accuracy of covariate selection for the Cox model, AFT model, random survival forest model, and DeepSurv model, and this investigation is the first research using machine learning based survival models for mortgage default prediction. The result shows that Random survival forest can achieve the most accurate, and stable, covariate selection, while DeepSurv can achieve the highest accuracy of default prediction, and finally, the covariates selected by the models can be meaningful for mortgage programs throughout the banking industry.
Three Pillars of Physical Distancing: Anxiety, Prosociality, and Rule Compliance During the COVID-19 Pandemic

The outbreak of a global pandemic such as COVID-19 poses a challenge for societies across the world. Lacking both vaccination and medical treatment, the only way to combat the spread of a virus in its early stages are behavioral measures, particularly physical distancing behavior. The present work proposes three pillars of individuals’ engagement in physical distancing: anxiety, prosociality, and rule compliance. In a large (N = 1,504), pre-registered study among German adults, we studied both situation-specific tendencies and stable personality traits that are theoretically associated with these pillars in relation to self-reported physical distancing behavior and underlying motives. Results supported the importance of each of the proposed pillars for physical distancing behavior. That is, for each pillar, we found (some) relations of the corresponding tendencies and personality traits with physical distancing (motives) as expected. Overall, the project provides a comprehensive picture of physical distancing behavior during the COVID-19 pandemic.
Measuring the community value in online social networks

Communities in social networks form with different purposes and play a significant role in interpersonal interactions. Analysis of virtual communities indicates a more precise understanding of the behaviours and desires of individuals in social networks. In this paper, new measures have been proposed for analyzing implicit and explicit communities in Online Social Networks (OSNs). The measures of “potential value of the community members” and “value of the community messages”, which are used for calculating the measure of “community value” are among the most important measures introduced in this paper. Another measure introduced is “user influence rate” in a community, which represents the contribution of a person in creating value in a community. To provide a sound dataset, we collected the information from several real implicit communities in Twitter based on different hashtags. Finally, the suggested measures have been analyzed and compared statistically and behaviourally across different communities. The results of this research well indicate the importance and practicality of the measures introduced in Community analysis of Twitter.
Evaluation of the green synthesis, characterization and antibacterial activity of silver nanoparticles from corm extract of Crocus sativus var. Haussknechtii

Purpose: To prevent chemical toxicity, biosynthesis of silver nanoparticles has been proposed as a cost-effective and environmentally friendly reducing agent option by corm extract of Crocus sativus var. Haussknechtii and as well as, evaluate their effects against Staphylococcus aureus and Escherichia coli. Research method: Silver nanoparticles were produced in the presence of secondary metabolites of this plant. The nanoparticles were then identified using the technique ultraviolet-visible spectroscopy (UV–Vis), fourier transform infrared (FTIR), field emission scanning electron microscope (FESEM). The antibacterial properties were used against two microorganisms, S. aureus (Gram-positive) and E. coli (Gram-negative), using the agar well propagation method. Findings: The observation of the peak at 450 nm in the UV-Vis spectra for corm synthesized silver nanoparticles reveals the reduction of silver metal ions into silver nanoparticles. The optimum pH and AgNO3 concentrations were 9 and 4 mM, respectively. FESEM images detected the spherical Ag-NPs shape with diverse sizes ranged within 70.70–192.02 nm. Additionally, based on the antibacterial test that has been done for nanoparticles, the mean diameter of the inhibition zone after exposure to S. aureus and E. coli was 22.67±0.58 and 20.00±00 mm, respectively. Limitations: There was no significant limitation to report. Originality/Value: The corm extract of C. haussknechtii is a promising agent for the biosynthesis of almost spherical silver nanoparticles. The synthesized nanoparticles show good Inhibition activity in different concentrations. The AgNPs synthesized by corm extract in high concentrations are found to be high antibacterial activity against two bacterial organisms. This indicates that the increase in nanoparticle diameter is directly related to antimicrobial properties.
Contaminants of Emerging Concern Detected By Comprehensive Target Analysis in a Sediment Core Collected From Osaka Bay, Japan

The contaminants of emerging concern (CECs) have been one of the major concerns in recent. The objective of this study was to find potential CECs using an analytical screening method “comprehensive target analysis with an automated identification and quantification system (CTA-AIQS)” which uses gas and liquid chromatography combined with mass spectrometry (GC-MS and LC-MS). CTA-AIQS was applied to analyze sediment core samples collected from Osaka Bay, Japan, in 2019. Among the 74 chemicals were detected in sediment core samples, six sterols, PPCPs (fexofenadine, diphenhydramine, and clarithromycin), and tris(2-isopropylphenyl)phosphate isomers (TIPPPs) showed increasing profile from bottom to surface layer of the sediment core. Through the screening with chemical properties (persistence, bioaccumulation potential, and toxicity) of those chemicals, TIPPPs were categorized as potential CECs in the marine environment in Japan.
Normative Principles for Decision-Making in Natural Environments.

The decisions we make are shaped by a lifetime of learning. Past experience guides the way that we encode information in neural systems for perception and valuation, and determines the information we retrieve when making decisions. Distinct literatures have discussed how lifelong learning and local context shape decisions made about sensory signals, propositional information, or economic prospects. Here, we build bridges between these literatures, arguing for common principles of adaptive rationality in perception, cognition, and economic choice. We discuss how a single common framework, based on normative principles of efficient coding and Bayesian inference, can help us understand a myriad of human decision biases, including sensory illusions, adaptive aftereffects, choice history biases, central tendency effects, anchoring effects, contrast effects, framing effects, congruency effects, reference-dependent valuation, nonlinear utility functions, and discretization heuristics. We describe a simple computational framework for explaining these phenomena. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.