Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Nanoparticle in the Veterinary Medicine

Nanotechnology is a modern and developed technology, which have great importance in many fields of medicine (diagnosis and treatment). Also, it used to prevent and solve many problems related to animal production and health. The Nanosystems are including metallic nanoparticles, liposomes, polymeric Nanospheres, polymeric micelles, carbon nanotubes, functionalized fullerenes, polymer-coated Nanocrystals, dendrimers and Nanoshells. Our review showed a details classification of nanoparticles and their uses. Nanoparticles have several features depended on the size, colossal surface. The development of antibiotics nanoparticle is very important and has an excellent impact in treating bacterial infections wherever the antibiotics nanoparticle gives high therapeutic effect without negative side effects. Our review showed some aspects of the nanoparticles' classification and their uses in general form and veterinary medicine, focusing the light on nanoparticle applications in the nutrient, Biocides, meat and egg quality, milk, animal treatment diseases, and reproduction the animals.
Class-Incremental Learning for Tissue Classiﬁcation

Deep Neural Networks (DNNs) have been successfully applied to many problems in the medical domain. However, the common assumption of having access to the complete training data may not always hold true. Instead, data could be provided by different sources at different points in time. Especially in the medical ﬁeld, privacy regulations can exacerbate this problem by prohibiting storage of old data. In such scenarios, proper incremental training for DNNs becomes essential to not forget previously learned knowledge. To this end, a recently published method called Deep Model Consolidation (DMC) proposes knowledge distillation with auxiliary data. We use this method as a benchmark and propose improvements on top of it. Furthermore, we present a novel approach to transform auxiliary images to look more like images from old datasets. With experiments on known benchmark datasets as well as a dataset for tissue type classiﬁcation, we show that DMC and our image transformer-based method can reduce forgetting of previously acquired knowledge.
SOCIAL CONSERVATION MODELS IN ANCIENT JAVANESE MANUSCRIPTS FROM THE 19 CENTURY

The specific purpose of this research is to discover the social conservation models contained in the ancient Javanese manuscript, Serat Centhini (1814 AD). This is a qualitative research study which utilizes a philological and social humanities approach. The data collected from the manuscript were reduced, displayed, and verified based on the research problem. The results of the research indicate that there are three values of social conservation in the Centhini manuscript, namely fundamental, instrumental, and practical values. The essence of the social teachings in the Centhini manuscript is divine behavior, morals, truth, beauty, humanity, and life. The implementation of the social cultural teachings in the Centhini manuscript is harmony, kinship, friendship, and mutual cooperation. All of these values are extremely important for the actualization of better norms in modern-day life.
PREDICTING ANTICANCER PEPTIDES AND PROTEIN FUNCTION WITH DEEP LEARNING by Nathaniel

Anticancer peptides (ACPs) are a promising alternative to traditional chemotherapy. To aid wet-lab and clinical research, there is a growing interest in using machine learning techniques to help identify good ACP candidates computationally. In this work, we develop DeepACPpred, a novel deep learning model for predicting ACPs using their amino acid sequences. Using several gold-standard ACP datasets, we demonstrate that DeepACPpred is highly effective compared to state-of-the-art ACP prediction models. Furthermore, we adapt the above neural network model for predicting protein function and report our experience with participating in a community-wide large-scale assessment of protein functional annotation tools.
ANOMALY MACHINE DETECTION ALGORITHM BASED ON SEMI VARIATIONAL AUTO-ENCODER OF MEL SPECTROGRAM Technical

This report proposes a solution for Task 2 of IEEE DCASE data challenge 2020, which attempts to detect anomaly machines according to acoustic data. The proposed solution uses a semi variational auto-encoder. The term “semi” indicates that the resulting variational auto-encoder may not successfully reconstruct the input as the key task of the task is to distinguish the outlier samples according to a specific feature rather than reconstruct the input precisely. As a result, there are a few minor changes introduced by the provided baseline system, which set up a different training stop criteria and a different anomaly scoring system. By the proposed method, the use of different stop training criteria for an variational auto-encoder may help different objectives.
Personal Credit Risk Forecast Model Based on BO-XGBoost

With the development of financial business in our country, the consumption mode of personal credit economy is generally accepted in daily life. Personal credit economy brings great convenience, but also brings huge personal credit risk. The accuracy of personal credit risk prediction model is particularly important. The logical regression of the traditional financial risk forecasting model can not solve the nonlinear problem and it is difficult to fit the real distribution of data, resulting in low prediction accuracy. In order to solve these two problems, this paper uses XGBoost (eXtremeGradientBoosting) model, which can solve not only nonlinear problems but also difficult fitting problems, but the prediction accuracy is still not high and there is room for further improvement. In this paper, the super-parameters of XGBoost model are optimized by Bayesian algorithm to form BO-XGBoost (Bayesian Optimization XGBoost) model, and the prediction results are analyzed and compared with traditional logical regression model and unoptimized XGBoost model. The results show that the prediction accuracy of the optimized XGBoost model is higher than that of the traditional logical regression model and the unoptimized BO-XGBoost model.
Total and Abdominal Adiposity are Lower in Overweight and Obese Children with High Cardiorespiratory Fitness

The study goal was to determine the impact of cardiorespiratory fitness on the total and abdominal adiposity in a sample of children aged 6 to 10. It was hypothesised that high cardiorespiratory fitness would result in lower total and abdominal obesity. The research was conducted on a sample of 1432 respondents where 717 (50.1%) are boys, and 715 (49.9%) are girls aged 6 to 10. The average age of the respondents was 8.72±1.4 years. Three anthropometric measures (body height, body weight and waist circumference) were considered and the body composition was determined by BIA. Body mass index sex - and age-specific cut-off points were used for overweight and obesity definition and children were placed in two groups: overweight/obese and non overweight. Cardiorespiratory fitness (CRF) was assessed with the endurance shuttle-run test. Participants were grouped into high and low CRF based on age and sex distributions. The results were statistically analyzed by using t-tests and a χ2 test, analysis of covariance, Pearson correlation and hierarchical linear regression. On the basis of the obtained results, one can conclude that children who are overweight or obese and have a high level of cardiorespiratory fitness, have lower body mass index values, lower waist circumference, lower body fat percentage and a higher muscle mass percentage compared to children who are classified in the same BMI category, but have low values of cardiorespiratory fitness. The results of this study suggest that an appropriate level of cardiorespiratory fitness can reduce the threats of obesity among the pediatric population.
THE IMPACT OF THE TAX CUTS AND JOBS ACT ON RESIDENTIAL HOUSING MARKETS: EVIDENCE FROM NEW JERSEY

The US tax code contains provisions that significantly reduce homeownership costs. These benefits were reduced under the Tax Cuts and Jobs Act of 2017, raising the real cost of property taxes and mortgages for a subset of taxpayers. This paper examines how individual homebuyers and residential housing markets responded to these changes. Using a unique constructed dataset of home loan records matched to deeds, this paper shows that homebuyers responded by purchasing smaller homes with lower property tax burdens, with the level of response indicating that the price elasticity of housing demand is approximately unit elastic. Homebuyers also reduced the size of their home loans (relative to sale price) by the equivalent of the response to a two percentage point increase in interest rates. Additionally, home prices in high-property tax areas fell by an amount corresponding to 60 percent of the increase in property tax liabilities.
Anomalous Sounds Detection Using A New Type of Autoencoder based on Residual Connection Technical Report

This report describes our submission for task2 (Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring) of the DCASE 2020. In this report we propose networks of fully connected autoencoder based on residual connections, which can increase the accuracy of anomaly sound detection. As for data pre-processing, we use data augmentation methods to generate more data from existing data. Our feature extraction is still carried out with log mel spectrogram. Finally, our method has achieved average AUC of 0.7912 and average pAUC of 0.6105 on the development dataset.
Firms’ Rollover Risk and Macroeconomic Dynamics

This paper analyzes the macroeconomic implications of firms’ rollover risk. I develop a heterogeneous-firms macroeconomic model with rollover crises emerging from coordination failures among creditors. Rollover crises are events in which a firm defaults because creditors fail to roll over its debt, but would have repaid otherwise. I assess the quantitative relevance of rollover crises by employing a modelbased identification strategy which argues that their incidence is informed by the observed distribution of firms’ bankruptcy outcomes, and find that roughly half of corporate bankruptcy events are due to rollover crises. I then use the model to assess the aggregate implications of rollover risk for the U.S. economy and find that rollover risk can significantly amplify the impact of recessions. Lastly, I show that imperfectly targeted credit policies can mitigate rollover crises but can exacerbate firms’ future debt overhang. ∗I am highly indebted to Diego Perez and Pablo Ottonello for their invaluable and continuous guidance and support, and to Jaroslav Borovička, Mark Gertler and Ricardo Lagos for their generous advice. I am also grateful to Jess Benhabib, Simon Gilchrist, Federico Kochen and Sydney Ludvigson for several useful discussions and suggestions. I would like to thank Marina Azzimonti, Yan Bai, Javier Bianchi, Lucia Casal, Michele Cavallo, Gaston Chaumont, Alessandro Dovis, Thomas Drechsel, Juan Dubra, Max Huber, Juan Martin Morelli, Dino Palazzo, John Shea, Alp Simsek, Luminita Stevens, Francisco Vazquez-Grande, and participants in seminars at Bank of Mexico, Central Bank of Uruguay (BCU), EIEF, Federal Reserve Board, Minneapolis Fed, NYU, PUC-Rio, Rutgers University, SED-Wisconsin 2022, T2M-Kings’ College London 2022, Universidad de Chile, University of Maryland, and University of Rochester for very helpful comments and suggestions. The paper benefited greatly from several discussions with members of the Research Divisions at the Federal Reserve Board in the context of the Ph.D. Dissertation Fellowship program. In previous versions, the paper circulated as ”Quantifying Firm Runs. ” Email: lguntinw@ur.rochester. edu 1
Optimization of Operating Parameters on Wear Behavior of Manganese Phosphate Coated Die Steel

The aim of Manganese Phosphate coating is to convert the metallic surface completely into a non metallic surface. A Phosphating is a chemical conversion treatment which produces a porous surface layer of crystalline phosphate. High carbon high chromium steels are commonly used in applications requiring excellent wear resistance in tool and die making industries. The wear characteristic of the coated steel was evaluated through pin on disc test as per ASTM G99 standards using a sliding velocity of 3.0m/s under constant loads. Taguchi’s design of L09 orthogonal array is followed selecting three parameters namely load, surface roughness, sliding distance. Optimal conditions are arrived by Signal-Noise ratio method with respect to coefficient of friction. The results are validated by analysis of variance (ANOVA) and the percentage of contribution of load, surface roughness and sliding distance for better coefficient of friction are determined.
OPTIMIZATION OF STAGGERED GRID FINITE- DIFFERENCE COEFFICIENTS BASED ON CONJUGATE GRADIENT METHOD

Jin, K. , Huang, J.P., Zou, Q. , Wang, Z. , Tong, S. , Liu, B. and Hu, Z. , 2021. Optimization of staggered grid finite-difference coefficients based on conjugate gradient method. Journal of Seismic Exploration, 31: 33-52. The implementation of difference coefficients optimization strategy can effectively suppress numerical dispersion and improve the modeling accuracy. The conventional difference coefficients calculation method based on Taylor-series Expansion exists serious numerical dispersion. In this paper, we derive a new dispersion error function from the dispersion relation, and the optimal difference coefficients are obtained iteratively by using the conjugate gradient method, thus a staggered-grid difference coefficients optimization method based on the conjugate gradient is developed. We compare dispersion curves, snapshots and single shot records using low-velocity model, high-velocity model and Marmousi model, the results show that the new method can effectively reduce the numerical dispersion compared with the difference coefficients of the conventional Taylor-series Expansion method. The 8th-order optimized difference operators can achieve the modeling precision of 12th-order Taylor-series Expansion difference operators, which can effectively save calculation time and internal storage. The optimization method performs well for both simple model and complex model forward modeling.
Qatari Money Reader: A smart mobile app to support persons with visual impairment – Supported by Mada Innovation Program

In 2018, Innovation Factory Limited developed a unique smartphone application called “Qatari Money Reader” fully funded through the direct grant stream under the Mada Innovation Program (MIP, 2021). The app can scan and identify Qatari currency by just using the camera of the smartphone. The solution was effective for people with visual impairment & the elderly while retaining their privacy by identifying the value of the notes when they are in public places or when they need to count their money. In Addition to that, the app includes other features like reading the Qatari Riyal currency in real-time and works entirely offline without the need to have internet connectivity. Once the currency is detected, the app will inform the user about the value of money in Arabic and English. With the update in the Qatari Bills, the Mada Innovation program supported the development of Money Reader App V2 with added features like new bill detection, Counting notes, Exchange notes and Experiment on fake note detection. Mada hosted focus groups and user testing sessions to evaluate the application before the official dissemination.
Trends of Progress in Setting up Biorefineries in Developing Countries: A Review of Bioethanol Exploration in Nigeria

In recent times, limitations and adverse effects of fossil fuels have significantly attracted researchers' attention to green fuels worldwide, especially in developed nations. As a way of assessing this actualization of biorefineries establishment in developing nations, this report surveys the works done by various researches towards this great course in terms of promoting and gaining the attention of both government and private investors about the technical and economic feasibility of embracing the use of biofuels, a case of bioethanol in Nigeria. Different classes of feedstocks were reviewed for the laboratory-scale, process scale-up, pilot plant, and techno-economic studies regarding ascertaining the technical and economic feasibility of local setup of a functional biorefinery in Nigeria, which would be beneficial environmentally and economically. The literature survey unveiled that the Bioethanol yield obtained from sugarcane-juice (72.7 %), banana-stems (84.0 %), and cassava (92.0 %) were found to be of highest potential amongst other sugar-based, lignocellulosic, and starchbased feedstock, respectively. The survey further unveils that the volume of process scale-up and economic feasibility studies does not correlate well with the laboratory-scale studies. The bulk of the research works on bioethanol has given preferential attention to laboratory studies. Only a few studies have looked into the commercialization (i.e., scale-up) of the laboratory findings and the economic implications. Presently, only sugarcane and a few cassavas are reported in the literature so far. It is, therefore, necessary for further studies to give attention to the investigation of the commercializing locally developed technologies and the exploration of their economic benefits. https://doi.org/10.30501/jree.2021. 278037.1197
An in- vitro comparative assessment of the sealing ability of AH ‎Plus sealer with different root canal nano sealers ‎

Objectives: The aim of the present study was to evaluate the sealing ability of experimental nano sealers (nano calcium hydroxide and nano bioactive glass) and to compare it with the commercial AH plus sealer using a dye penetration method. Materials and methods: Sixty single-rooted mandibular premolars were selected. The coronal portion of each tooth was removed to standardize root length at 16 mm and the root canals were prepared with NiTi rotary files (ProTaper Next) up to X4 (40/0.06). Teeth were randomly assigned and divided according to the sealer used for obturation into three groups of 20 each. Group I: AH plus, Group 2: Nano calcium hydroxide and Group 3: Nano-bioactive glass. All root canals were obturated using single cone gutta-percha(#40/.06) and one of the tested sealers. Root canal sealing was assessed by a dye penetration test. The data were statistically analyzed by ANOVA test followed by post hoc analysis (P < 0.05). Results: Significant improvement shown by the presented study suggests that nano calcium hydroxide sealer showed significantly less dye leakage than nano bioactive glass sealers and Ah plus sealer. Conclusion: The results of this study showed that the synthesized nano-powder sealers are suitable for use in root canal therapy to prevent leakage. The root canal can be sealed better by using smaller nano-powder particle sizes. In addition, the two groups exhibited significant differences in leakage in comparison with commonly used AH plus sealer.
Opinion Formation on Social Media Platform

In this paper, we formulate a hierarchical Bayesian learning model to investigate the dynamics of individual opinion formation on a social media platform where individuals are exposed to two sources of social influence, preceding peer posts in a microblog thread and comments from the general public, which are often endogenously generated and thus correlated. The focus of this study is to separate these two distinct sources and measure their respective effects. We take the advantage of a unique natural experiment contained in the data from a leading microblogging website in China to help model identification. Further, we adapt the Consensus Markov Chain Monte Carlo algorithm, a parallel computing approach, to our Bayesian estimation procedure to effectively handle the big data in this study. We find that preceding peer posts in a microblog thread influence an individual to converge to the opinions of her peers. It is observed that more comments from the general public make an individual less certain about the topic discussed in the thread, whereas preceding microblogs from closely connected peers reinforce her opinion. Simulation studies are conducted to show that restricting number of comments in the early stage of a social media campaign helps to build a consumer base that is more certain about their opinions, and identifying more influential users with favorable opinions to participate early positively shifts the average opinion.
Mask-net: Detection of Correct Use of Masks Through Computer Vision

This paper focuses on creating a system for recognizing the correct use of a mask through computer vision techniques. Research was carried out with aims of establishing the criteria for the creation of custom datasets, which were used to train, validate and test a pair of deep learning models, Mask-net and I-Mask-net. Both were designed with similar architectures, making use of Transfer Learning Techniques. The results given by training showed that the ﬁne tuning carried out was adequate, while the tests carried out showed that the models have an acceptable level of accuracy, reaching 85.47% for Mask-net and 85.96% for I-Mask-net, additionally supported by the obtained precision, recall and F1-Score calculations.
Local Weighted Generalized Multigranulation Neighborhood Rough Set

Local neighborhood rough set is an emerging rough set model, which provides more efficient knowledge representation for weakly labeled data with real-valued attributes in big data. It mainly studies knowledge representation under single granulation, but it cannot effectively and comprehensively represent knowledge of multiple granulations. In this paper, having considered the importance of different granulations for knowledge representation and decisions, we propose a Local Weighted Generalized Multigranulation Neighborhood Rough Set (LWGMNRS) model by using a novel weighted granulation method for knowledge representation of weakly labeled data with multiple granulations. Meanwhile, we discuss the relationships among LWGMNRS and the existing rough set models in detail, as well as its advantages and significances. Finally, theoretical analysis and case study show that the sig-nificance of LWGMNRS in knowledge representation.