Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Clustering Technique in Marketing Sector

: Cluster analysis divides data into meaningful or useful groups (clusters). One of the most important problems in modern finance is finding efficient ways to summarize and visualize the stock market data to give individuals or institutions useful information about the market behaviour for investment decisions. The enormous amount of valuable data generated by the stock market has attracted researchers to explore this problem domain using different methodologies. Potential significant benefits of solving these problems motivated extensive research for years. We proposed clustering techniques that are being used in Data Mining is presented. Data mining adds to clustering the complications of very large datasets with very many attributes of different types. This imposes unique computational requirements on relevant clustering algorithms with k-means method is one of the clustering techniques. Data mining facilitates marketing sector by classifying customer demographic that can be used to predict which customer will respond to a mailing or buy a particular product and it is very much helpful in growth of business. If meaningful clusters are the goal, then the resulting clusters should capture the “natural” structure of the data. For example, cluster analysis has been used to group related documents for browsing, to find genes. and accumulated at an unprecedented pace. Data mining techniques will be used to uncover hidden patterns and predict future trends and behaviours in financial markets. The competitive advantages achieved by data mining include increased revenue, reduced cost, and much improved marketplace responsiveness and awareness. We described the process of clustering from the data mining point of view. We gave the properties of a “good” clustering technique and the methods used to find meaningful partitioning. These techniques are being used in marketing sector. This study concludes that clustering techniques and algorithms become a highly active research area in data mining research.
Patterns of water and heat flux across a biome gradient from tropical forest to savanna in Brazil

We investigated the seasonal patterns of water vapor and sensible heat flux along a tropical biome gradient from forest to savanna. We analyzed data from a network of flux towers in Brazil that were operated within the Large‐Scale Biosphere‐Atmosphere Experiment in Amazonia (LBA). These tower sites included tropical humid and semideciduous forest, transitional forest, floodplain (with physiognomies of cerrado), and cerrado sensu stricto. The mean annual sensible heat flux at all sites ranged from 20 to 38 Wm−2, and was generally reduced in the wet season and increased in the late dry season, coincident with seasonal variations of net radiation and soil moisture. The sites were easily divisible into two functional groups based on the seasonality of evaporation: tropical forest and savanna. At sites with an annual precipitation above 1900 mm and a dry season length less than 4 months (Manaus, Santarem and Rondonia), evaporation rates increased in the dry season, coincident with increased radiation. Evaporation rates were as high as 4.0 mm d−1 in these evergreen or semidecidous forests. In contrast, ecosystems with precipitation less than 1700 mm and a longer dry season (Mato Grosso, Tocantins and São Paulo) showed clear evidence of reduced evaporation in the dry season. Evaporation rates were as low as 2.5 mm d−1 in the transitional forests and 1 mm d−1 in the cerrado. The controls on evapotranspiration seasonality changed along the biome gradient, with evaporative demand (especially net radiation) playing a more important role in the wetter forests, and soil moisture playing a more important role in the drier savannah sites.
Racial/Ethnic Differences in High Return Investment Ownership: A Decomposition Analysis

The racial/ethnic disparities of risky asset ownership were investigated. In the 2004 and 2007 Survey of Consumer Finances datasets, 30% of Hispanic, 36% of Black, and 65% of White households had high return investments such as stocks, investment real estate, or private business assets. Logistic analysis shows that Black and Hispanic households are much less likely to have high return assets, even after controlling for other factors such as education. However, Blinder-Oaxaca decomposition analyses show that if Black households had the same characteristics, including risk tolerance, as White households, they would have the same ownership rates for high return investments, and the gap between Hispanic and White households is much smaller than implied by standard logistic regression.
Effect of Administration of Metformin on Lipid Profile in Patients with Polycystic Ovary Syndrome after Six Months of Treatment

Objective: Women with polycystic ovary syndrome (PCOS) exhibit risk factors for cardiovascular diseases such as abdominal obesity, insulin resistance and dyslipidemia. Insulin sensitizers, especially metformin, have been shown to improve these metabolic disturbances, but there are only a few studies on their effects on serum lipids in polycystic ovary syndrome. The aim of the present study was to evaluate the effects of metformin on lipid profile changes in patients affected by PCOS. Methods: In this study, 60 women aged 16-40 years with PCOS were selected. Diagnostic criteria were based on the diagnostic criteria of PCO syndrome in Rotterdam ESHRE (European Society for Human Reproduction)/ ASRM(American Society of Reproductive Medicine) Sponsored PCOS Consensus Workshop Group (2004). Samples of fasting peripheral blood were taken from all patients to test serum Cholesterol, Low density lipoprotein (LDL), Triglycerides (TG) and High density lipoprotein (HDL) at the start of the treatment and were repeated after 6 months of metformin treatment in a dosage of 1500 mg in divided doses. Also, BMI was compared before and after the treatment. Results: The mean BMI before treatment was 24.6  2.9 kg/m 2 and after treatment was 23.8  2.8 kg/m 2 (p<0.0001). The mean cholesterol level before treatment was 192.9  30.2 mg/dl and after treatment was 174.0  21.7 mg/dl (p<0.0001). The mean Triglyceride value before treatment was 148.143.2 mg/dl and after treatment was 129.9  29.6 mg/dl (p<0.0001). The mean LDL level before treatment was 127.5  31.7 mg/dl and after treatment was 106.8  21.6 mg/dl (p<0.0001). The mean HDL level before treatment was 36.6  7.9 mg/dl and after treatment was 41.1  6.4 mg/dl (p<0.0001). Conclusion: In women with PCOS, metformin treatment had beneficial effects on lipid profile and therefore it could be useful in the prevention of cardiovascular complications in these women.
Testing redMaPPer centring probabilities using galaxy clustering and galaxy–galaxy lensing

Galaxy cluster centring is a key issue for precision cosmology studies using galaxy surveys. Mis-identification of central galaxies causes systematics in various studies such as cluster lensing, satellite kinematics, and galaxy clustering. The red-sequence Matched-filter Probabilistic Percolation (redMaPPer) estimates the probability that each member galaxy is central from photometric information rather than specifying one central galaxy. The redMaPPer estimates can be used for calibrating the off-centring effect, however, the centring algorithm has not previously been well-tested. We test the centring probabilities of redMaPPer cluster catalog using the projected cross correlation between redMaPPer clusters with photometric red galaxies and galaxy-galaxy lensing. We focus on the subsample of redMaPPer clusters in which the redMaPPer central galaxies (RMCGs) are not the brightest member galaxies (BMEM) and both of them have spectroscopic redshift. This subsample represents nearly 10% of the whole cluster sample. We find a clear difference in the cross-correlation measurements between RMCGs and BMEMs, and the estimated centring probability is 74$\pm$10% for RMCGs and 13$\pm$4% for BMEMs in the Gaussian offset model and 78$\pm$9% for RMCGs and 5$\pm$5% for BMEMs in the NFW offset model. These values are in agreement with the centring probability values reported by redMaPPer (75% for RMCG and 10% for BMEMs) within 1$\sigma$. Our analysis provides a strong consistency test of the redMaPPer centring probabilities. Our results suggest that redMaPPer centring probabilities are reliably estimated. We confirm that the brightest galaxy in the cluster is not always the central galaxy as has been shown in previous works.
Copula Dependence Structure on Stock Market with Application to Risk

Traditionally, dependency between stock returns has been expressed by linear correlation and been measured whether they follow the same trend of movement under the assumption of joint elliptical distribution. However, this method cannot fully describe the dependencies between two stock returns, such as lower tail, upper tail or center dependency. In this thesis, I make use of the copula analysis method to model these dependencies. One objective of this thesis is to analyze the stock returns in the real stock market, then apply the copula technique to compile the real data and investigate the intrinsic link and relevant structure between them. Further, I will study rare events under extreme value theories. Together with the use of copula technique that models default correlation, I will construct peaks-over-threshold model to analyze the dependence structure between different stock returns and simulate the real world data. Firstly, the copula model is estimated based on the data in two dimension. Then, the application is extended to three dimension, and the three-dimensional copula model is constructed by the similar method.
Some Selected Physical Fitness Parameters of Intellectual Disability Individuals: A Review

The benefits of physical activities are universal for all including those with intellectual disability. Individuals with intellectual disability are mostly neglected and hardly exercised. Hence, the opportunities for improved health and wellness are limited. Paucity of data that could guide ameliorative measures also presents another challenge. Therefore, this paper reviews some selected physical fitness parameters of intellectually disables individuals. Key words: Intellectual disability, Physical Fitness.
The alteration in intestinal secretory immunoglobulin A and its secreting cells during ischemia/reperfusion injury

Objective To investigate the change in intestinal secretion immunoglobulin A (sIgA) level and IgA-secreting cells during ischemia/reperfusion (I/R) injury. Methods Forty-eight BALB/c mice were randomly divided into 6 experimental groups in accordance with different reperfusion times (R2h, R6h, R12h, R24h, and R72h group), and one sham group (n＝8). Bacterial translocation to distant organs (lung, spleen, and mesenteric lymph nodes) was observed. The sIgA level of the intestinal tract was measured by enzyme-linked immunosorbent assay (ELISA). The B cell subgroup in the lymphocytes related to the intestinal tract was measured by flow cytometry. Results The bacterial translocation occurred during I/R injury, and the intestinal sIgA level decreased, and they showed an obvious negative correlation (r2＝0.729). With the increase in intestinal I/R injury, the ratio of IgM+B220+ cells in the gut-associated lymphoid tissue increased, whereas the proportion of IgA+B220+ cells decreased. The most significant change was found in R12h group (P < 0.01). Conclusions The proportion of IgM+ B cells in the gut-associated lymphoid tissue increased, whereas that of IgA+ B cells reduced during I/R injury. These phenomena may cause sIgA level to reduce and bacterial translocation of the distant organs to occur.
Pan-cancer single cell RNA-seq uncovers recurring programs of cellular heterogeneity

Cultured cell lines are the workhorse of cancer research, but it is unclear to what extent they recapitulate the cellular heterogeneity observed among malignant cells in tumors, given the absence of a native tumor microenvironment. Here, we used multiplexed single cell RNA-seq to profile ~200 cancer cell lines. We uncovered expression programs that are recurrently heterogeneous within many cancer cell lines and are largely independent of observed genetic diversity. These programs of heterogeneity are associated with diverse biological processes, including cell cycle, senescence, stress and interferon responses, epithelial-to-mesenchymal transition, and protein maturation and degradation. Notably, some of these recurrent programs recapitulate those seen in human tumors, suggesting a prominent role of intrinsic plasticity in generating intra-tumoral heterogeneity. Moreover, the data allowed us to prioritize specific cell lines as model systems of cellular plasticity. We used two such models to demonstrate the dynamics, regulation and drug sensitivities associated with a cancer senescence program also observed in human tumors. Our work describes the landscape of cellular heterogeneity in diverse cancer cell lines, and identifies recurrent patterns of expression heterogeneity that are shared between tumors and specific cell lines and can thus be further explored in follow up studies.
Measuring residential property values in Hong Kong

The elements of sales comparison for residential property depend on a package of inherent attributes that are valued by consumers. These attributes can be classified into the following categories: structural, physical, neighbourhood and environmental. A model that incorporates adjustments for floor area, age, views and amenities such as availability of recreational facilities is presented and discussed. A multiple regression analysis with transactions‐based data, using weighted least square to determine the discrete estate‐type induced price effect in the Hong Kong housing market, is also presented. The paper demonstrates how the view of negative housing attributes is capitalised into house prices. Specifically, it is shown that the residential property values are higher for estate‐type housing properties, and lower for dwelling units with a cemetery view.
Using neural networks for service strategies in an abandonment queue

To determine an optimal strategy for a multi-class server, with impatient customers, can be difficult. There are heuristics, such as the Whittle index, that perform well under restrictive conditions. We discuss a few of these heuristics and their merits and drawbacks. To overcome these drawbacks, we experiment with a data-based approach, by generating data using value iteration and predicting a strategy with a neural network. Furthermore, we explore the effect of different parameters on the performance of the neural network, for a particular case. Moreover, we look into more extensive methods such as combining the prediction of multiple neural networks and using neural networks to choose the training data. Acknowledgments First of all, I would like to thank Dr. F. M. Spieksma and M. J. H. van den Bergh for their supervision and guidance. They provided warm support and excellent advice during our many meetings. They gave me the freedom to explore what I found interesting, which is in my opinion the basis for good research. I would like to thank my fellow students and especially Josefien Verschuure for making the bachelor and the master more enjoyable. Finally, I would like to thank Nicky, my family and friends for their love and support.
An Improved Multi-Classifiers Classification Methodology using Local Contextual Information for Remote Sensing Images

Combining multiple classifiers has been regarded as an effective solution applied in remote sensing image classification. However, generally by adopting common voting principle, it faces a dilemma when setting voting threshold to balance the validity of the classified pixels and the proportion of unclassified pixels. In this paper, a multi-classifiers classification methodology considering local contextual knowledge was proposed to solve the dilemma. The whole work consists of two stages: primary classification and post classification. In the primary classification, a conservative (or near-conservative) voting principle was adopted to ensure a strong validity of the classified pixels. In post classification, a new adjusted spatial and spectral minimum distance (ASSMD) was used, integrating local contextual information of classified pixels to resolve the rest unclassified pixels. Effectiveness of this methodology was demonstrated by experiments compared with several classic classifiers. The overall accuracy increased by 14% and Kappa statistic increased by 0.19 at most. Furthermore, a sensitivity analysis of ASSMD to two factors: mask size and percentage of contextual information, has been carried out to explore the best configuration.
Deadlock-Free Typestate-Oriented Programming Luca Padovani

Context. TypeState-Oriented Programming (TSOP) is a paradigm intended to help developers in the implementation and use of mutable objects whose public interface depends on their private state. Under this paradigm, well-typed programs are guaranteed to conform with the protocol of the objects they use. Inquiry. Previous works have investigated TSOP for both sequential and concurrent objects. However, an important difference between the two settings still remains. In a sequential setting, a well-typed program either progresses indefinitely or terminates eventually. In a concurrent setting, protocol conformance is no longer enough to avoid deadlocks, a situation in which the execution of the program halts because two or more objects are involved in mutual dependencies that prevent any further progress. Approach. In this work, we put forward a refinement of TSOP for concurrent objects guaranteeing that welltyped programs not only conform with the protocol of the objects they use, but are also deadlock free. The key ingredients of the type system are behavioral types, used to specify and enforce object protocols, and dependency relations, used to represent abstract descriptions of the dependencies between objects and detect circularities that might cause deadlocks. Knowledge. The proposed approach stands out for two features. First, the approach is fully compositional and therefore scalable: the objects of a large program can be type checked in isolation; deadlock freedom of an object composition solely depends on the types of the objects being composed; any modification/refactoring of an object that does not affect its public interface does not affect other objects either. Second, we provide the first deadlock analysis technique for join patterns, a concurrency abstraction with which programmers can express complex synchronizations in a succint and declarative form. Grounding. We detail the proposed typing discipline for a core programming language blending concurrent objects, asynchronous message passing and join patterns. We prove that the type system is sound and give non-trivial examples of programs that can be successfully analyzed. A Haskell implementation of the type system that demonstrates the feasibility of the approach is publicly available. Importance. The static analysis technique described in this work can be used to certify programs written in a core language for concurrent TSOP with proven correctness guarantees. This is an essential first step towards the application and integration of the technique in a real-world developer toolchain, making programming of such systems more productive and less frustrating.
NEURAL AND STATISTICAL TECHNIQUES FOR REMOTE SENSING IMAGE CLASSIFICATION

This paper examines different approaches to remote sensing images classification. Included in the study are statistical approach, in particular Gaussian maximum likelihood classifier, and two different neural networks paradigms: multilayer perceptron trained with EDBD algorithm, and ARTMAP neural network. These classification methods are compared on data acquired from Landsat-7 satellite. Experimental results showed that to achieve better performance of classifiers modular neural networks and committee machines should be applied.
PERIMENOPAUSAL DEPRESSION AND ITS MANAGEMENT THROUGH UNANI APPROACH: A REVIEW

Perimenopausal women in particular are at risk for new onset and recurrence of major depressive episodes. Women with previous histories of PMS or p ost artum depression are at increased risk. The symptoms may present with features of melanchol ia, agitation, somatic symptoms, or sleep disturbances. The increase in major depressive epis odes occurring at this time has been found to be linked to hormonal changes of the menopausal tra nsi ion, namely increased FSH levels, rather than to social or environmental triggers, although changes in valued lifestyle factors associated with, for example, motherhood, family, fertility, o r physical rigor and attractiveness, may precipitate depressive mood changes in predisposed r vulnerable women. Women who worry about others are at increased risk for developing c li ical depression. Other women may value the new-found independence that these lifestyle changes incur.Untreated depression may exacerbate heart disease, diabetes, and osteoporosis, as well as contributing to an increased risk for suicide and to a more debilitating course of the depression that is more refractory to intervention.Although antidepressant medication is the mainstay of treatment, adjunctive therapy, especially with estrogen replacement, may be indicated in refractory cases, and may speed the onset of antidepressant action. Many, but not all, studies, report that progesterone antagonizes the beneficial effects of estrogen. Alt hough some antidepressants improve vasomotor symptoms, in general they are not as effe ctive as estrogen alone for relieving these symptoms. Estrogen alone, however, does not general ly result in remission of major depression in most (but not all) studies, but may provide bene fit to some women with less severe symptoms if administered in therapeutic ranges. Phytoestrogens are naturally occurring plant compounds that are similar in some ways to estradio l, the most potent naturally occurring estrogen. Isoflavones are phyto-estrogens similar to women's strogens and are bound to cellular estrogen receptors in various organs. In addition to attention to general health, hormon al status, and antidepressant treatment, the optimal management of perimenopausal depression also requires attention to the individual woman’s psychosocial an d spiritual well being.
Constraints on the initial mass function of the first stars

Motivated by theoretical predictions that the first stars were predominantly very massive, we investigate the physics of the transition from an early epoch dominated by massive Pop III stars to a later epoch dominated by familiar low-mass Pop II/I stars by means of a numerically generated catalogue of dark matter haloes coupled with a self-consistent treatment of chemical and radiative feedback. Depending on the strength of the chemical feedback, Pop III stars can contribute a substantial fraction (several per cent) of the cosmic star formation activity even at moderate redshifts, z ≈ 5. We find that the three z ≈ 10 sources tentatively detected in Near Infrared Camera and Multi-Object Spectrometer (NICMOS) Ultra Deep Fields (UDFs) should be powered by Pop III stars, if these are massive; however, this scenario fails to reproduce the derived Wilkinson Microwave Anisotropy Probe (WMAP) electron scattering optical depth. Instead, both the UDFs and WMAP constraints can be fulfilled if stars at any time form with a more standard, slightly top-heavy, Larson initial mass function.