Miracle-dz/UniFAR-FTUs huggingface dataset:
The Empirical Research of Relationship between Consumption and Income for Chinese Urban Residents

This paper studied the clustering analysis of panel data, the specification test of panel data model and its parameter estimation. By carrying out clustering analysis on panel data, we finally decided to study the relationship of Chinese urban residents’ eight income levels between consumption and income from 2007 to 2012. Based on analysis of covariance in panel data model, we built the variable coefficient panel data model and then estimated the model parameters. In this work, we can identify the relationship between consumption and income in recent years. According to the estimation results, we drew the conclusion that income disparities have important influence on urban residents’ consumption behavior.
PERBAIKAN KUALITAS CITRA UNTUK KLASIFIKASI DAUN MENGGUNAKAN METODE FUZZY K-NEAREST NEIGHBOR

Abstrak Tumbuhan memiliki banyak manfaat bagi kehidupan manusia seperti makanan, obat-obatan, industri, melindungi lingkungan, bahkan penyedia oksigen bagi organisme lainnya. Untuk mengetahui jenis-jenis tumbuhan maka diperlukan klasifikasi. Klasifikasi tumbuhan dapat dilakukan dengan mengidentifikasi fitur daun dalam tumbuhan tersebut. Dalam menentukan bagus dan tidaknya proses identifikasi citra maka diperlukan suatu proses perbaikan kualitas citra. Perbaikan kualitas citra digunakan untuk menyiapkan citra dalam kondisi yang ideal agar tidak menimbulkan masalah dan mempengaruhi hasil interpelasi serta mempengaruhi analisis dan perencanaan yang akan dilakukan. Pada penelitian ini metode klasifikasi yang digunakan adalah metode Fuzzy K-Nearest Neighbor (FKNN). Metode Fuzzy K-Nearest Neighbor (FKNN) merupakan metode yang paling objektif. Berdasarkan hasil uji coba yang dilakukan, penggunaan metode Fuzzy K-Nearest Neighbor (FKNN) didapatkan nilai akurasi sebesar 93%. Kata kunci: Perbaikan kualitas citra, Fuzzy K-Nearest Neighbor (FKNN)
Found in translation

We describe translation effects that have been studied in the automated text classification literature. We expand on a point within this research space, quality effects, with our own work in this area. We present an efficient method for evaluating text quality on the basis of reference texts. The method, which is general to text classification problems more widely construed, is related to the background literature and argued to be effective on the strength of the fact that it enables quality checking of amounts of text that exceed what is humanly feasible to verify. The method partially automates the process: in processing the entirety of a translated corpus being probed, it ranks items for stylistic conformity with a reference corpus, and the least conforming ranks are indicated as the items most likely to require human intervention.
Choosing Children ’ s Environmental Risk w

A model of endogenous risk provides a foundation to study a parent’s child care decisions when the child could be exposed to an environmental hazard (e.g., toxic substance, foodborne pathogen). The parent invests in childcare quality and quantity to reduce the likelihood of a hazard exposure occurring and to reduce its severity if the exposure is realized. We supply conditions to sign unambiguously the effects on a child’s hazard exposure of an increased probability a parent fails to access or have command over a technique of exposure prevention or that a technique is ineffective in preventing exposure. Also, we consider these effects when the parent is unsure what a technique can do to reduce the child’s probability of exposure. We conclude public policies designed to encourage use of a particular childcare technique, if childcare quality and quantity are stochastic substitutes, can reduce parental use of other techniques. The net impact of the policy could increase the chance the child suffers.
Detection of Moving Objects with Fuzzy Color Coherence Vector

Background subtraction is a popular method for detecting foreground that is widely adopted as the fundamental processing for advanced applications such as tracking and surveillance. Color coherence vector (CCV) includes both the color distribution information (histogram) and the local spatial relationship information of colors. So it overcomes the weakness of the conventional color histogram for the representation of an object. In this paper, we introduce a fuzzy color coherence vector (FCCV) based background subtraction method. After applying the fuzzy c-means clustering to color coherence subvectors and color incoherence subvectors, we develop a region-based fuzzy statistical feature for each pixel based on the fuzzy membership matrices. The features are extracted from consecutive frames to build the background model and detect the moving objects. The experimental results demonstrate the effectiveness of the proposed approach.
Geographically Weighted Poisson Regression (GWPR) for Analyzing The Malnutrition Data (Case Study: Java Island in 2008

DIDIN SAEPUDIN. Geographically Weighted Poisson Regression (GWPR) for Analyzing The Malnutrition Data (Case Study: Java Island in 2008). Advised by ASEP SAEFUDDIN and DIAN KUSUMANINGRUM. Poisson regression, namely global model is a statistical method used to analyze the relationship between the dependent variable and the explanatory variables, where the dependent variable is a counted data and has a Poisson distribution. The result of its parameter estimation is homogeneous for all of the observations. However, especially in spatial data, its estimation will produce biased estimation. The parameter estimates in each location will vary among regions as it is influenced by territorial or geographical factors, which is known as spatial variability or spatial non-stationarity. Therefore the appropriate analysis for this data is Geographically Weighted Poisson Regression (GWPR) model. GWPR parameter estimation used a weighting matrix which depends on the proximity between the locations. Fisher scoring iteration is used for solving the iteratively parameter estimation. In this research, GWPR will be used in malnutrition data because malnutrition is counted data which is assumed to have a Poisson distribution and the indirect factors of differences in the number of malnourished patients in every region is possible due to spatial factors. The results showed that GWPR model has better performance than global model based on AICc difference. Poverty aspect was the most influencing factor to the number of malnourished patients in a region compared to health, education, and food aspect. The spatial variability map is created for eight variables used in selected global model where every map showed the variability of local parameter estimates. There were five groups of the local parameter estimates in each map based on percentiles grouping which showed the low until hight relation of the parameter estimates groups to the number of malnourished patients. This research also created a significant variables map which detects the variables that were significant in each region.
Oxygenic photosynthesis: translation to solar fuel technologies

Mitigation of man-made climate change, rapid depletion of readily available fossil fuel reserves and facing the growing energy demand that faces mankind in the near future drive the rapid development of economically viable, renewable energy production technologies. It is very likely that greenhouse gas emissions will lead to the significant climate change over the next fifty years. World energy consumption has doubled over the last twenty-five years, and is expected to double again in the next quarter of the 21st century. Our biosphere is at the verge of a severe energy crisis that can no longer be overlooked. Solar radiation represents the most abundant source of clean, renewable energy that is readily available for conversion to solar fuels. Developing clean technologies that utilize practically inexhaustible solar energy that reaches our planet and convert it into the high energy density solar fuels provides an attractive solution to resolving the global energy crisis that mankind faces in the not too distant future. Nature’s oxygenic photosynthesis is the most fundamental process that has sustained life on Earth for more than 3.5 billion years through conversion of solar energy into energy of chemical bonds captured in biomass, food and fossil fuels. It is this process that has led to evolution of various forms of life as we know them today. Recent advances in imitating the natural process of photosynthesis by developing biohybrid and synthetic “artificial leaves” capable of solar energy conversion into clean fuels and other high value products, as well as advances in the mechanistic and structural aspects of the natural solar energy converters, photosystem I and photosystem II, allow to address the main challenges: how to maximize solar-to-fuel conversion efficiency, and most importantly: how to store the energy efficiently and use it without significant losses. Last but not least, the question of how to make the process of solar energy conversion into fuel not only efficient but also cost effective, therefore attractive to the consumer, should be properly addressed.
How different creators of wine brands affect dimensions of brand personality: An empirical study in the German wine industry

Purpose: Brands are one of the most important assets owned by many producer companies. This is even more important in the wine industry which is characterized by almost unlimited different brands leading to the fact that wine is seen as an extraordinary “difficult” product by the average consumer with a lot of information that cannot be processed in advance of their purchases. However, how brands emerge is not fully understood, yet. Recent research suggests different brand creators besides the producer (for example consumers as brand creators). Nevertheless, empirical research on this issue is scarce, even more in the wine industry. Methodology: Against this background we analyzed how the perception of a brand in the wine industry and corresponding dimensions of brand-personality will be changed using different brand creators in an experimental setting with n=957 participants. Findings: We found significant differences in the evaluation of brand-personality-dimensions. Therefore, we suggest that knowledge about the role of different groups in the brand creating process will have important implications for communication strategies within contemporary brand management and successful positioning in the wine industry.
Effects of Socialization Methods and Peer Attachment on Character Strength of School-Aged Children

This study aimed to analyze the influences of socialization methods and peer attachment on character strength of scholl-aged in rural families. This study employed cross sectional approach using survey and interview. A hundred of families with children at grades 4 and 5 in two public elementary schools at Pamijahan Sub District, Bogor Regency participated in this study. This found only age and peer attachment that had significant influence on character strengths of school-aged children in rural families.
LEAF RUST RESPONSIVE EXPRESSION PROFILING OF GRAS TRANSCRIPTION FACTOR FAMILY IN WHEAT (TRITICUM AESTIVUM L)

Objective: Rusts are among the most important fungal diseases of wheat all over the world responsible for losses in yield ranging from 25% to 90%. Bread wheat ( Triticum aestivum L . ) is one of the major staple food crops all over the world but is greatly affected by leaf rust. GRAS is a plant-specific stress responsive transcription factor gene family. The objective of the present study is to carry out expression profiling of GRAS TFs during leaf rust pathogenesis. Methods: SOLiD SAGE library preparation. GRAS TFs were mapped to the four libraries using the CLC genomics workbench to study their expression profiles. A Co-expression network of these TFs has been constructed using WGCNA (weighted gene co-expression network analysis). Results: The four libraries have been prepared: S-M, S-PI, R-M R-PI. GRAS TFs were mapped to these libraries, giving different expression profiles of the 63 GRAS TFs. Pearson correlation coefficients were 0.56, 0.34 and 0.24 for R-M vs. R-PI, S-M vs. S-PI and S-PI vs. R-PI respectively. Highest difference in expression of TaGRAS genes was between two libraries S-PI vs. R-PI. TaGRAS genes have been clustered into seven (blue, turquoise, red, green, black, maroon and yellow) different modules in signed correlation. Conclusion: TaGRAS genes which are upregulated during leaf rust might be plays important roles to provide resistance to the plants. The difference in Pearson correlation coefficient indicates that susceptible and resistant-NILs utilize a different set of TaGRAS genes to counter leaf rust pathogenesis. The genes which are clustered together in coexpression network might be expressed together during leaf rust pathogenesis to provide resistance to the plant. Keywords : Leaf rust, Wheat. GRAS, Transcription Factors, SAGE, WGCNA
Application of Three Different Calibration-Uncertainty Analysis Methods in a Semi-Distributed Rainfall-Runoff Model Application

Often parameters in hydrologic models cannot be measured directly and can only be inferred by a calibration process. This work addresses the application and comparison of three parameter uncertainty methods and their effects on the prediction of streamflow in Qezel Ozan watershed (43,000 km ) located in 2 northwestern Iran. Methods of Partcle Swarm Optimization (PSO), DiffeRential Evolution Adaptive Metropolis (DREAM) and Sequential Uncertainty Fitting ver. 2 (SUFI2) were used in this study to calibrate a rainfall-runoff model created using the Soil and Water Assessment Tool (SWAT). The calibration and validation results indicated statistically insignificant differences among the three algorithms. The main difference was in their number of runs, where DREAM converged after 36000 runs, PSO after 6000 runs and SUFI2 after 1500 runs. SUFI2 proved to be a very efficient optimization algorithm, while PSO had the largest NS for calibration (0.59) and validation (0.74) periods.
Determinants of Farm-Level Adoption of Cultural Practices for Banana Xanthomonas Wilt Control in Uganda

Understanding the factors influencing farmers’ adoption decisions of the disseminated Banana Xanthomonas wilt (BXW) control package is critical for successful management of the disease. This paper analysed the determinants of farmers’ decisions to adopt the control package by smallholder banana farmers in Uganda. A binomial Logit model is applied to household survey data collected from 350 households. Results from the study show that household labour availability, technology package attributes such as labour demand and perceived effectiveness of the practices in managing the disease; and agro-ecological location and banana production system significantly influence adoption decisions. These results suggest the need for adaptation of the technology package to better suit the needs and socio-economic conditions of smallholder farmers through a farmer participatory technology development approach that takes into account research findings, farmers’ indigenous knowledge and resource constraints to enhance adoption.
Predicting cognitive and behavioral functions in patients with dementia: Practical prognostic models of logarithmic and linear regression

Aims: This study provides data on predicting changes in cognitive functions, behavioral independences and disturbances in dementia patients by differential modeling with logarithmic and linear regression. Methods: This longitudinal study included two data analysis groups. Group one: 24 dementia patients for identification of cognitive and behavioral changes over time in group data; group two: 15 dementia patients to ensure correlation of the group data applied to prediction of each individual’s degree of cognitive and behavioral changes. Group one mini-mental state examination, functional independence measure and dementia behavior disturbance scale scores were assessed initially and 3 and 6 months thereafter during hospitalization and were regressed on the logarithm and linear of time. In group two, calculations of the scores were made for the first two scorings after admission to tailor logarithmic and linear regression formulae to fit an individual’s degree of changes at 9 and 12 months. Results: Changes in data Aki Watanabe1, Makoto Suzuki1, Harumi Kotaki2, Hironori Sasaki2, Takayuki Kawaguchi1, Hideki Tanaka1, Michinari Fukuda1 Affiliations: 1Department of Rehabilitation, Kitasato University School of Allied Health Sciences, Kanagawa, Japan; 2Department of Rehabilitation Medicine, Hatsutomi Hoken Hospital, Chiba, Japan. Corresponding Author: Aki Watanabe, 1-15-1 Kitasato, Minami-ku, Sagamihara City, Kanagawa 252-0373, Japan; Email address: aki.wtnb@kitasato-u.ac. jp Received: 23 August 2016 Accepted: 01 November 2016 Published: 22 November 2016 over time resembled both logarithmic and linear functions. However, the scores sampled at two baseline points based on logarithmic regression modeling estimated prediction of cognitive and behavioral changes more accurately than did linear regression modeling. Conclusion: This simple-to-use logarithmic modeling accurately predicted changes in cognitive functions, behavioral independence and disturbances in patients with dementia.
Proliferation and Apoptosis of the Bitch Ovary during the Different Stages of the Oestrous Cycle

In order to maintain homeostasis of the ovary, proliferation and apoptosis occur simultaneously in different ovarian cell types. It was shown that steroid hormone which changed during the oestrous cycle, may regulate these mechanisms in normal mammal ovarian tissues. Therefore, the present study was aimed to study cell proliferation and apoptosis during different stages of the oestrous cycle in the bitch ovary by using immunohistochemistry and TUNEL assay. The bitch ovaries were collected at 4 different stages of the oestrous cycle, which were prooestrus, oestrus, dioestrus and anoestrus. Immunohistochemistry and TUNEL assay were applied to detect Ki-67 protein and apoptosis respectively in different cells of the bitch ovary. The results showed that the most prominent staining for both Ki-67 and apoptosis was found in granulosa cells of the ovarian follicles. During the oestrous cycle, high proliferation was observed at prooestrus and oestrus in almost all ovarian cell types while no proliferating cell was found in the bitch corpus luteum. For apoptosis, high apoptotic rate was found at prooestrus in all ovarian cells except for luteal cells, in which apoptosis was significantly higher during dioestrus compared to the other stages. These results indicated that proliferation and apoptosis varied not only among different ovarian cells but also during the stages of the oestrous cycle, which may partly involve with the levels of ovarian steroid hormones. Moreover, the granulosa cells of the ovarian follicles were the most dynamic cell types which undergo proliferation and apoptosis during the oestrous cycle from the present study.
A deep understanding from a single image

As the number of photos being taken increases exponentially, there is also an exponentially increasing need for technologies for automatic image analysis. The applications include organizing personal photo collections, processing surveillance imagery, and industrial analysis. Despite the rise of deep learning in recent years, which brought computer vision to a level of maturity, there is still a long way to go until performance reaches the human level of image understanding: if you look at a photo, you will be immediately able to (a) sharpen the edges of the image in your mind even though it is slightly blurry, (b) recognize all objects in the image, (c) guess the age, gender, and attractiveness of the people, and (d) make a good guess about the occasion on which it was taken. Can a computer make similar inferences? In this thesis we aim at providing answers to this question. We propose various techniques, including image processing, object detection, and finegrained classification to infer as much as possible from just a single image. As a first contribution, we propose an efficient novel artifact reduction algorithm based on an anchored regression model which doubles performance when compared to state-of-the-art methods while being orders of magnitude faster. The second contribution is a novel formulation of non-maximum suppression (NMS) as a post processing step for object detection for a single image. Our method is based on the recent Affinity Propagation Clustering algorithm and contrary to the standard greedy approach solved globally with its parameters being learned automatically. The experiments show for object class and generic object detection that it provides a promising solution to the shortcomings of the greedy NMS. The third contribution is a deep learning solution to age estimation from a single face image without the use of facial landmarks and the release of the IMDB-WIKI dataset, the largest public dataset of face images with age and gender labels. Our method
The Antimicrobial Effects of Boswellia Carterii, Glycyrrhiza Glabra and Rosmarinus Officinalis some Pathogenic Microorganisms

Evaluate the antimicrobial activity of Boswellia carterii (frankincense), Glycyrrhiza glabra (Licorice) and Rosmarinus officinalis (Rosemary) against Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa , Bacillus subtilis and klebsiella pneumoniae . The extracts were prepared using soxhlet apparatus for 8 hours. Evaluation of antimicrobial activity was carried out using disc diffusion method and Minimum inhibition concentration method (MIC) at different concentrations for (200mg/ml -0.39mg/ml) for all tested bacteria . The results of this study showed that the D.W, ethanol and chloroform extracts of rosemary showed the lowest minimum inhibitory concentration against S. aureus and all extracts of licorice against K. pneumonia . However, extracts of frankincense showed a broad change in values from (12.5mg/ml) to (50mg/ml). These results illustrate the importance of tested the plant extracts against pathogenic organisms as the first step in the path to further encourage the use of natural extracts as alternative method to chemical treatment.
Vertical and horizontal migrations by the jumbo squid Dosidicus gigas revealed by electronic tagging

Dosidicus gigas is a large and powerful oceanic squid that is economically valuable and eco- logically important in the eastern Pacific Ocean. We employed electronic tagging methods to provide the first long-term monitoring of the natural behaviors of D. gigas in its mesopelagic habitat. Seven pop-up satellite tags logged depth and temperature for a total of 842 h, and a conventional archival tag yielded 780 h of continuous time-series data. Horizontal movements of nearly 100 km over 3 d were observed, and these were temporally associated with an established trans- Gulf migration. Squid consistently spent most daylight hours at depths >250 m, the approximate upper boundary of a midwater hypoxic zone termed the oxy- gen minimum layer (OML). A diel migration brought squid to near-surface waters at dusk, but a highly vari- able amount of diving back into the OML occurred throughout the night. Rhythmic vertical movements within the OML often occurred, and sojourns of up to 6 h in this hypoxic zone below 300 m were observed. Laboratory experiments revealed a high resting rate of oxygen consumption under normal conditions, but this rate decreased drastically under hypoxic conditions such as would be associated with the OML in nature. These findings suggest that D. gigas has physiological adaptations that permit constant foraging in both oxygenated near-surface waters and within the OML.