Miracle-dz/UniFAR-FTUs huggingface dataset:
Soft-sensor modeling for lysine fermentation processes based on PSO-SVM inversion

Lysine fermentation process,which is characterized by nonlinear,large time-delay,multivariable dynamic coupling,is very difficult to realize crucial biochemical parameters on-line measurement.A soft-sensing modeling method was proposed based on PSO-SVM inversion theory.Firstly,the reversibility of the system was analyzed and the inverse extension model was built by introducing the feature information and abandoning the secondary information,and the inverse extension model was built offline with SVM which had good fitting capability and was adjusted online by PSO based on the error information between actual fermentation process input and model output.Finally,the inverse extension model was applied to the fed-batch L-lysine fermentation process and prediction with on-line soft-sensor of the directly immeasurable crucial biochemical parameters was realized.The simulations showed that the crucial biochemical parameters could be predicted.The results also showed that the proposed method was more accurate compared with the alternative modeling methods.
Design of On-line Monitoring System for Tunnel Boring Machine's Disc Cutter Wear

In order to obtain the real time disc cutter wear monitoring data during the excavating of Tunnel Boring achine( TBM),a TBM on-line cutter wear monitoring system was designed based on the eddy current sensor,which converted the changes of distance between the sensor and the cutter ring before and after it was worn to the voltage signal. After it was conditioned,the signal was transmitted to K60 MCU,then the signal underwent the analog-digital convention and calculation to get the actual cutter ring wear data. In the transport course,the ZigBee Dual-link wireless communication system of the link was chosen by the quality of the link and the remaining power of the node,sending the test data out of the closed cutterhead excavation face to the rear to avoid the interruption of the data communication. Utilizing 17 inch 1∶ 2 similar disc cutters of different wear degrees as the research object,the on-line cutter wear monitoring experiment was made on the cutter performance test platform. The test proves the system can accurately measure the wear extent of the cutter ring,which contributes to the realization of measuring the disc cutter wear on-line.
A Personalized Recommender System with Correlation Estimation

Recommender systems aim to predict users’ ratings on items and suggest certain items to users that they are most likely to be interested in. Recent years there has been a lot of interest in developing recommender systems, especially personalized recommender systems to efficiently provide personalized services and increase conversion rates in commerce. Personalized recommender systems identify every individual’s preferences through analyzing users’ behavior, and sometimes also analyzing user and item feature information. Existing recommender system methods typically ignore the correlations between ratings given by a user. However, based on our observation the correlations can be strong. We propose a new personalized recommender system method that takes into account the correlation structure of ratings by a user. General precision matrices are estimated for the ratings of each user and clustered among users by supervised clustering. Moreover, in the proposed model we utilize user and item feature information, such as the demographic information of users and genres of movies. Individual preferences are estimated and grouped over users and items to find similar individuals that are close in nature. Computationally, we designed an algorithm applying the difference of convex method and the alternating direction method of multipliers to deal with the nonconvexity of the loss function and the fusion type penalty respectively. Theoretical rate of convergence is investigated for our new method. We also show theoretically that incorporating the correlation structure gives higher asymptotic efficiency of the estimators compared to ignoring it. Both simulation studies and Movielens data indicate that our method outperforms existing competitive recommender system methods.
Measurements of time-dependent $CP$ violation in charmless B meson decays

We report measurements of time-dependent $CP$ violation in charmless B meson decays that are sensitive to physics beyond the Standard model or Kobayashi-Maskawa $CP$ violation angle $\phi_{2}$ in b to s penguin or b to u tree transitions, respectively. The analyses use Belle final entire data sample collected at the Upsilon(4S) resonance containing 772 mullion B meson pairs.
Word Bank Document

hile e orts are currently in place to collect data on the economic consequences of the COVID-19 pandemic, this note looks at the experience of the 2008 nancial crisis to gain insights on possible di erential e ects of crises on female and male entrepreneurs. Speci cally, the note uses rm-level data collected immediately after the 2008 nancial crisis in six countries in Eastern Europe and Central Asia (Bulgaria, Hungary, Latvia, Lithuania, Romania, and Turkey) to look at two aspects of the di erential e ect of the crisis. First, whether there is a di erence in the exit rate for rms with male vs. female top managers; and second, whether, among rms that stayed in business, female-managed rms are a ected disproportionally. Results show that rms run by female top managers are more likely to exit the market. Secondly, when able to stay in business, male and female-managed rms su ered a similar impact in the short term; however, female-managed rms su ered more than male-managed rms in the longer term. W
Structured representation of temporal document collections by diachronic linguistic periodization

Language is our main communication tool. Deep understanding of its evolution is imperative for many related research areas including history, humanities, social sciences, etc. as well as for effective temporal information retrieval. To this end, we are interested in the task of segmenting long-term document corpora into naturally coherent periods based on the embodied evolving word semantics. There are many benefits of such segmentation including better representation of content in long-term document collections and support for modeling and understanding semantic drift. We propose a two-step framework for learning time-aware word semantics and periodizing document archive. The effectiveness of our model is demonstrated on the New York Times corpus spanning from 1990 to 2016.
Synchronous Distributed Key Generation without Broadcasts

Distributed key generation (DKG) is a key building block in developing many efficient threshold cryptosystems. This work initiates the study of communication complexity and latency of DKG protocols over a point-to-point (bounded) synchronous network. Our key result is the first synchronous DKG protocol for discrete log-based cryptosystems with O ( κn 3 ) communication complexity ( κ denotes a security parameter) that tolerates any t < n/ 2 Byzantine faults among n parties. We present two variants of the protocol: a deterministic protocol with O ( t ∆) latency and randomized protocol with O (∆) latency in expectation where ∆ denotes the bounded synchronous delay. In the process of achieving our results, we design (1) a novel weak gradecast protocol with optimal communication complexity of O ( κn 2 ) for linear-sized inputs and latency of O (∆), (2) a primitive called “recoverable set of shares” for ensuring recovery of shared secrets, (3) an oblivious leader election protocol with O ( κn 3 ) communication and O (∆) latency, and (4) a multi-valued validated Byzantine agreement (MVBA) protocol with O ( κn 3 ) communication complexity for linear-sized inputs and O (∆) latency in expectation. Each of these primitives is of independent interest.
Development, validation, and evaluation of prediction models to identify individuals at high risk of lung cancer for screening in the English primary care population using the QResearch database: research protocol and statistical analysis plan

Background and research aim: Lung cancer is a research priority in the UK. Early diagnosis of lung cancer can improve patients' survival outcomes. The DART-QResearch project is part of a larger academic-industrial collaborative initiative, using big data and artificial intelligence to improve patient outcomes with thoracic diseases. There are two general research aims in the DART-QResearch project: (1) to understand the natural history of lung cancer, (2) to develop, validate, and evaluate risk prediction models to select patients at high risk for lung cancer screening. Methods: This population-based cohort study uses the QResearch database (version 45) and includes patients aged between 25 and 84 years old and without a diagnosis of lung cancer at cohort entry (study period: 1 January 2005 to 31 December 2020). The team conducted a literature review (with additional clinical input) to inform the inclusion of variables for data extraction from the QResearch database. The following statistical techniques will be used for different research objectives, including descriptive statistics, multi-level modelling, multiple imputation for missing data, fractional polynomials to explore non-linear relationships between continuous variables and the outcome, and Cox regression for the prediction model. We will update our QCancer (lung, 10-year risk) algorithm, and compare it with the other two mainstream models (LLP and PLCOM2012) for lung cancer screening using the same dataset. We will evaluate the discrimination, calibration, and clinical usefulness of the prediction models, and recommend the best one for lung cancer screening for the English primary care population. Discussion: The DART-QResearch project focuses on both symptomatic presentation and asymptomatic patients in the lung cancer care pathway. A better understanding of the patterns, trajectories, and phenotypes of symptomatic presentation may help GPs consider lung cancer earlier. Screening asymptomatic patients at high risk is another route to achieve earlier diagnosis of lung cancer. The strengths of this study include using large-scale representative population-based clinical data, robust methodology, and a transparent research process. This project has great potential to contribute to the national cancer strategic plan and yields substantial public and societal benefits through earlier diagnosis of lung cancer.
Massive Molecular Gas as a Fuel Tank for Active Galactic Nuclei Feedback In Central Cluster Galaxies

Massive molecular gas has been discovered in giant elliptical galaxies at the centers of galaxy clusters. To reveal its role in active galactic nucleus (AGN) feedback in those galaxies, we construct a semianalytical model of gas circulation. This model especially focuses on the massive molecular gas (interstellar cold gas on a scale of ∼10 kpc) and the circumnuclear disk (≲0.5 kpc). We consider the destruction of the interstellar cold gas by star formation and the gravitational instability for the circumnuclear disk. Our model can reproduce the basic properties of the interstellar cold gas and the circumnuclear disk, such as their masses. We also find that the circumnuclear disk tends to stay at the boundary between stable and unstable states. This works as an “adjusting valve” that regulates mass accretion toward the supermassive black hole. On the other hand, the interstellar cold gas serves as a “fuel tank” in the AGN feedback. Even if the cooling of the galactic hot gas is prevented, the interstellar cold gas can sustain the AGN activity for ≳0.5 Gyr. We also confirm that the small entropy of hot gas (≲30 keV cm2) or the short cooling time (≲1 Gyr) is a critical condition for the existence of massive amounts of molecular gas in the galaxy. The dissipation time of the interstellar cold gas may be related to the critical cooling time. The galaxy behavior is described by a simple relation among the disk stability, the cloud dissipation time, and the gas cooling rate.
Evapotranspiration estimation of Urmia Lake Basin using GCOM-C thermal imagery

Basin-scale monthly and annual evapotranspiration (ET) is estimated for Urmia Lake Basin by applying the Global Change Observation Mission for Climate (GCOM-C) global ETindex estimation algorithm to thermal imagery observed by the GCOM-C satellite. In total, 297 satellite images acquired during 2018-2019 were used in this study. ET estimation accuracy was examined for an area dominated by apple fields using traditional surface irrigation. The estimated ET was 15% lower than the standard crop ET, which was computed using a procedure suggested by the Food and Agriculture Organization of the United Nations on a monthly timescale, and was 8% lower on an annual timescale. Comparison of estimated ET with a satellite-based ET map derived by using the Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC) model showed a similar difference. The 8%–15% differences among the different sources of ET were small, given that a similar or wider range of uncertainty is frequently available even in ground-based ET measurements. Comparison between the estimated ET and the MODIS ET Product (MOD16) revealed a greater difference in the evaluated area of the apple fields. Given the climatic ET demands and the irrigation practices of the area, ET estimation accuracy is more likely to be higher using the dataset derived from this study than using MOD16. The GCOM-C satellite started routine surface observations in January 2018. Its contribution to agricultural water management, such as by estimating ET as presented in this study, will increase as the amount of historical data stored continues to accumulate.
Structural and physical evaluation of a reinforced beam using strain gauges

In this research project,the deformations in the longitudinal and transverse reinforcing steel of a reinforced concrete beam with 2ϕ½” were estimated.Additionally,the displacements in thecenter of thespanweremeasuredtogether with theloads, which generatedthecrackingof the beam of dimensions180 mm×240 mm×3100 mm.Displacement were performed using a linear variable displacement transducer, and strain gauges were used to measure deformations. Finally, the applied load’s measurements were obtained with a load cell Pinzuar/Model-100/20T equipment.The physical-mechanical properties of the concreteused were determined through compressive strength tests at 28 days and modulus of elasticity.For this purpose, a load-bearing frame was used to support the beam at three points for load application.The data was collected directly on the Quantum/X2 equipment and analyzed with the help of the Catman/AP software.The maximum deformations found in the bending test at three point since there inforced concrete beam we are not greater than 8483µm/mm,presenting a failure in the center of the beam due to the creep of the tensile steel for a maximum load of 3115 Kg.Finally,the physical behavior of there inforced concrete beam with applying aload allows evaluating and optimizing this kind of systems.
A comparison between lumped and distributed hydrological models for daily rainfall-runoff simulation

In Uruguay, the Santa Lucía Chico watershed has been studied in several hydrologic/hydraulic works due to its economic and social importance. However, few studies have been focused on water balance computation in this watershed. In this work, two daily rainfall-runoff models, a distributed (SWAT) and a lumped one (GR4J), were implemented at two subbasins of the Santa Lucía Chico watershed, with the aim of providing a thorough comparison for simulating daily hydrographs and identify possible scenarios in which each approach is more suitable than the other. Results showed that a distributed and complex model like SWAT performs better in watersheds characterized by anthropic interventions such as dams, which can be explicitly represented. On the other hand, for watersheds with no significant reservoirs, the use of a complex model may not be justified due to the higher effort required in modeling design, implementation, and computational cost, which is not reflected in a significant improvement of model performance.
Canonical EEG microstates transitions reflect switching among BOLD resting state networks and predict fMRI signal

Objective. Electroencephalography (EEG) microstates (MSs), which reflect a large topographical representation of coherent electrophysiological brain activity, are widely adopted to study cognitive processes mechanisms and aberrant alterations in brain disorders. MS topographies are quasi-stable lasting between 60–120 ms. Some evidence suggests that MS are the electrophysiological signature of resting-state networks (RSNs). However, the spatial and functional interpretation of MS and their association with functional magnetic resonance imaging (fMRI) remains unclear. Approach. In a cohort of healthy subjects (n = 52), we conducted several statistical and machine learning (ML) approaches analyses on the association among MS spatio-temporal dynamics and the blood-oxygenation-level dependent (BOLD) simultaneous EEG-fMRI data using statistical and ML approaches. Main results. Our results using a generalized linear model showed that MS transitions were largely and negatively associated with BOLD signals in the somatomotor, visual, dorsal attention, and ventral attention fMRI networks with limited association within the default mode network. Additionally, a novel recurrent neural network (RNN) confirmed the association between MS transitioning and fMRI signal while revealing that MS dynamics can model BOLD signals and vice versa. Significance. Results suggest that MS transitions may represent the deactivation of fMRI RSNs and provide evidence that both modalities measure common aspects of undergoing brain neuronal activities. These results may help to better understand the electrophysiological interpretation of MS.
An Effect of CSR Engagement on Brand Image in the Food Service Industry*

†Symbiotic Life Tech Research Institute (Research center for food, nutrition & food service management), Yonsei University ** Dept. of Global Tourism, Baewha Women's University *** Corresponding Author Corporate Social Responsibility(CSR) can be broadly defined as a concept whereby companies integrate social and environmental concerns in their business operations and in their interaction with their stakeholders on a voluntary basis[1], something like working with employees, their An Effect of CSR Engagement on Brand Image in the Food Service Industry*
Low phosphorylation of estrogen receptor a (ERa) serine 118 and high phosphorylation of ERa serine 167 improve survival in ER-positive breast cancer

Endocrine therapy has become the most important treatment option for women with estrogen receptor (ER)-positive breast cancer.Urgently neededareprognostic assays that can identify those who need additional adjuvant therapy, such as signal transduction inhibitors or chemotherapy, for ER-positive early breast cancer. We examined phosphorylation of ERa serine (Ser) 118, ERa Ser167, p44/42mitogen-activated protein kinase (MAPK), andAkt and expression of progesterone receptor, amplified in breast cancer 1 (AIB1), human epidermal growth factor receptor 2 (HER2), p53, and Ki67 in ER-positive breast cancers by immunohistochemistry, and analyzed their significance for prognosis. Phosphorylation levels of ERa Ser118, ERa Ser167, MAPK, and Akt were positively correlated. AIB1 expression was significantly associated with phosphorylation of ERaSer118, MAPK, and Akt, and HER2 expression. Low phosphorylation of ERaSer118 and high phosphorylation of ERa Ser167 were associated with significantly improved disease-free (PZ0.0003 and PZ0.0002 respectively) and overall survival (PZ0.0007 and PZ0.0016 respectively) in multivariate analyses. Our data suggest that phosphorylation of ERa Ser118 and ERa Ser167 affects survival in ER-positive breast cancer and could be helpful in distinguishing patients who are likely to benefit from endocrine therapy alone from those who are not. Endocrine-Related Cancer (2008) 15 755–763
MAINTAIN AGENT CONSISTENCY IN SURAKARTA CHESS USING DUELING DEEP NETWORK WITH INCREASING BATCH

Deep reinforcement learning usage in creating intelligent agents for various tasks has shown outstanding performance, particularly the Q-Learning algorithm. Deep QNetwork (DQN) is a reinforcement learning algorithm that combines the Q-Learning algorithm and deep neural networks as an approximator function. In the single-agent environment, the DQN model successfully surpasses human ability several times over. Still, when there are other agents in the environment, DQN may experience decreased performance. This research evaluated a DQN agent to play in the two-player traditional board game of Surakarta Chess. One of the drawbacks that we found when using DQN in two-player games is its consistency. The agent will experience performance degradation when facing different opponents. This research shows Dueling Deep Q-Network usage with increasing batch size can improve the agent's performance consistency. Our agent trained against a rule-based agent that acts based on the Surakarta Chess positional properties and was then evaluated using different rule-based agents. The best agent used Dueling DQN architecture with increasing batch size that produced a 57% average win rate against ten different agents after training for a short period. ABSTRAK: Pembelajaran Peneguhan Mendalam adalah terbaik apabila digunakan bagi mewujudkan ejen pintar dalam menyelesaikan pelbagai tugasan, terutama jika ia melibatkan algoritma Pembelajaran-Q. Algoritma Rangkaian-Q Mendalam (DQN) adalah Pembelajaran Peneguhan berasaskan gabungan algoritma Pembelajaran-Q dan rangkaian neural sebagai fungsi penghampiran. Melalui persekitaran ejen tunggal, model DQN telah beberapa kali berjaya mengatasi kemampuan manusia. Namun, ketika ejen lain berada dalam persekitaran ini, DQN mungkin kurang berjaya. Kajian ini melibatkan ejen DQN bermain papan tradisional iaitu Catur Surakarta dengan dua pemain. Salah satu kekurangan yang dijumpai adalah konsistensi. Ejen ini akan kurang bagus ketika berhadapan lawan berbeza. Kajian menunjukkan dengan penggunaan Rangkaian-Q Dwipertarungan Mendalam bersama peningkatan saiz kumpulan dapat meningkatkan konsistensi prestasi ejen. Ejen ini telah dilatih untuk melawan ejen lain berasaskan peraturan dan sifat kedudukan Catur Surakarta. Kemudian, ejen ini diuji berpandukan peraturan berbeza. Ejen terbaik adalah yang menggunakan rekaan DQN Dwipertarungan bersama peningkatan saiz kumpulan. Ianya berhasil memenangi permainan dengan purata 57% berbanding sepuluh agen lain melalui latihan jangka masa pendek.
ANALYSIS CLASSIFICATION SENTIMENT OF THE LARGE PRIEST OF FPI’S RETURN USING SVM CLASSIFICATION WITH OVERSAMPLING METHOD

At the end of 2020, Habib Rizieq's return to Indonesia drew criticism from the public for causing crowds during the Covid-19 pandemic. News and opinions about Habib Rizieq fill internet platforms, including Twitter. The researcher wants to classify the opinion text data of Habib Rizieq's return from Twitter into positive and negative sentiments using the Support Vector Machine method. Opinion data comes from Twitter, so the data is analyzed by text mining through the preprocessing stage. The SVM classification of unbalanced data between positive and negative classes resulted in 95.06% accuracy with a negative class precision value of 84% and better than 72% recall, in the positive class the precision value was 96% less than 2% of recall 98%. While the SVM classification with the oversampling method gets 100% accuracy, precision, and recall. The results of positive sentiments are known that the public will always support and want freedom for Rizieq, for negative sentiments it is known that many people are disappointed with Rizieq regarding the lies of his swab test results.