Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness of Planned Health Education on Ideal Breastfeeding among Primigravidae in a Selected Hospital at Mangalore, South Karnataka

3. Breast feeding is an unequalled way of providing ideal food for the healthy growth and development of infants. It is also integral part of reproductive process with implications for the health of mother. Review of evidence has shown that on a population bases exclusive breast feeding for 6 months is the optimal way of feeding infants 4 . Nature has designed the provision that infants to be fed upon their mother’s milk. They find their food and mother at the same time. Every child born in the country has a right to lead a healthy life which is directly related to breast feeding. Milk contains immunizing agents which protects babies against various diseases and is rich in vitamins, enzymes and antibodies and it provides babies for the establishment of personalities and learning readiness. It is no doubt that no other food can replace mother’s milk 5 . Hence the advantage of breast feeding can’t be overemphasized in the developing countries like India which have high neonatal and infant mortality rates. Therefore, it has to be fed on exclusive basis to boost the fulfillment of needs of the babies and it is the right of every child. Exclusive breastfeeding (EBF) reduces infant deaths caused by common childhood illness such as diarrhea and pneumonia, fastens recovery during illness and helps space births 6 . Studies have showed that breast fed babies are more intelligent, improves their academic performance and they become smarter than formula fed babies 7 . Globally, more than ten million children die before their 5th birthday due to preventable causes and malnutrition is associated with over half of all childhood death and over a million babies born in the country die within their first month of life. India has the unfortunate distinction of claiming more than a quarter of the total newborn deaths in the world. It is estimated that out of 3.9 million neonatal deaths that occur worldwide, almost 30 percent occur in India 8 . The major cause of infant death is lack of exclusive breastfeeding leading to malnutrition, diarrhea and infections. The infant mortality rate is increasing primarily due to infectious diseases and malnutrition. Failure to offer breast milk, on practicing of exclusive breast feeding, on-compliance with immunization on account of illiteracy, ignorance, cultural and social beliefs pose the greatest risk of death in the infant. Many studies have found that mothers in India have inadequate knowledge and ill practices about breast feeding and EBF. Findings of a study conducted in Pondicherry, India revealed that the knowledge of the mothers was inadequate in areas of time of initiation of breastfeeding (92%), colostrum feeding (56%), duration of exclusive breastfeeding (38%), knowledge on expressed breast milk (51%) and continuation of breastfeeding while baby is sick 9
Unemployment incidence of immigrant men in Canada.

The unemployment incidence of immigrant and non-immigrant men in Canada is compared using 11 cross-sectional surveys spanning the years from 1982 to 1993. Recent immigrants are found to have higher unemployment probabilities than nonimmigrants with the difference being larger in recession years. Subsequently, measures of unemployment assimilation of immigrants are found to be sensitive to the macroeconomic conditions of the survey years. The main implication of the results for policy is that recent immigrants would benefit most from labour market programs that facilitate the transition of unemployed immigrants back to employment during recessions.
Multi-sensor Analysis of Vegetation Indices in a Semiarid Environment

Multi-sensor comparisons are sometimes used due to limited image availability and temporal coverage by a single sensor. However, multi-sensor comparability is not well documented. Factors affecting direct comparability such as atmospheric conditions, landscape heterogeneity, landscape changes, and sensor characteristics are difficult to quantify. This study compared several vegetation indices (VIs) from multisensor data to determine if VIs are comparable across scales and sensors. Within-sensor comparisons demonstrate that VIs are consistent across spatial resolutions indicating a direct multi-scale comparability. However, among-sensor comparisons indicate that VIs calculated from different sensors are not comparable with one another regardless of spatial resolution. Sensor-specific characteristics appear to offer the best explanation for the observed results.
Performance enhancement for microcell planning using simple genetic algorithm

Microcell planning is quite different from the cell planning used for macrocells. For achieving efficient cell planning, it is necessary to understand the propagation characteristics of microcell systems. In this paper, we present the propagation measurement results at the campus area located at urban Taipei city. We verify that the Walfisch-Ikegami model is an accurate model for predicting path loss in microcell systems. Based on the Walfisch-Ikegami model and digitized building information, cell planning using a simple genetic algorithm is presented. The optimum solution for base station numbers, locations, antenna heights, and transmitting power can be achieved. On other hand, due to capacity demand and coverage requirement, the existing system must be enhanced. We also present the simulation results for system enhancement using the simple genetic algorithm with the help of digitized building information and the Walfisch-Ikegami model.
A mathematical model for GHG from SB-MBR: calibration by an innovative protocol

A mathematical model to quantify greenhouse gases (GHG) (carbon dioxide, CO2 and nitrous oxide, N2O) for a membrane bioreactor (MBR) is presented. The model has been applied to a pilot plant with a pre-denitrification MBR scheme. The pilot plant was cyclically filled with real saline wastewater according to the fill-draw-batch operation. The model was calibrated by adopting a specific protocol based on extensive field dataset. Standardised Regression Coefficient (SRC) method was adopted to select the most influential model factors to be calibrated. Results related to SRC method show that among the important model factors a key role is played by the half saturation coefficients related with the nitrogen removal processes (kN2O, kNO) and by the model factors affecting the oxygen transfer rate in the aerobic and MBR tank (k2,2 and k2,3). In terms of uncertainty, it was found that for the gaseous model outputs (SGHG,N2O,1 and SGHG,N2O,2) only the 7% and the 12% of the measured data lays outside the bands showing an accurate model prediction in case a wide data set is available.
Weapon Detection and Classification Based on Time-Frequency Analysis of Electromagnetic Transient Images

Terrorist groups, hijackers, and people hiding guns and knives are a constant and increasing threat. Concealed weapon detection has become one of the greatest challenges facing the law enforcement community today. The fact that most weapons are made from metallic materials makes electromagnetic detection methods the most prominent and preferred approach for concealed weapon detection. Each weapon has a unique electromagnetic fingerprint, determined by its size, shape and physical composition. A new detection system developed at Newcastle University that uses a walk-through metal detector with a Giant Magneto-Resistive sensor array has been utilized in this study. The system enables a two-dimensional image to be constructed from measured signals and used in later image processing. This paper addresses weapon detection using time and frequency feature extraction techniques based on this new system. The study also employs and compares two classification techniques for potential automated classification. Experimental results using guns and non-gun objects in controlled and non-controlled environments have demonstrated the potential and efficiency of the new system. The classification capabilities of the system could be developed to the point that individuals could pass through the system without the need to take off other metallic objects. The proposed techniques have the potential to produce major improvements in automatic weapon detection and classification. Keywords-sensor array; electromagnetic imaging; weapon detection; feature extraction; airport security.
Asymmetric Three-Phase Cascading Trinary-DC Source Multilevel Inverter Topologies for Variable Frequency PWM

Asymmetric three-phase cascading Trinary-DC source Multilevel Inverter which can achieve reduced harmonics and superior root mean square (RMS) values of the output voltage is proposed. This topology can achieve cascaded full bridge inverter operation with dissimilar (unequal) DC Source and it is fired by using variable frequency pulse with modulation technique as a switching strategy. This pulse width modulation switching strategy has a newly adopted multicarrier single reference technique. The performance parameter factors like Form Factor (FF), Crest Factor (CF), Total Harmonic Distortion (THD) and fundamental RMS output voltage (VRMS) are estimated by using proposed asymmetrical three-phase cascading multilevel inverter for several modulation indices (0.8 - 1). The research study carries with MATLAB/SIMULINK based simulation and experimental results obtained using appropriate prototype (test board) to prove the viability of the proposed concept.
Optimum Monte-Carlo sampling using Markov chains

SUMMARY The sampling method proposed by Metropolis et al. (1953) requires the simulation of a Markov chain with a specified 7i as its stationary distribution. Hastings (1970) outlined a general procedure for constructing and simulating such a Markov chain. The matrix P of transition probabilities is constructed using a defined symmetric function sij and an arbitrary transition matrix Q. Here, for a given Q, the relative merits of the two simple choices for sij suggested by Hastings (1970) are discussed. The optimum choice for sij is shown to be one of these. For the other choice, those Q are given which are known to make the sampling method based on P asymptotically less precise than independent sampling.
Powering Up With Space-Time Wind Forecasting

The technology to harvest electricity from wind energy is now advanced enough to make entire cities powered by it a reality. High-quality, short-term forecasts of wind speed are vital to making this a more reliable energy source. Gneiting et al. (2006) have introduced a model for the average wind speed two hours ahead based on both spatial and temporal information. The forecasts produced by this model are accurate, and subject to accuracy, the predictive distribution is sharp, that is, highly concentrated around its center. However, this model is split into nonunique regimes based on the wind direction at an offsite location. This paper both generalizes and improves upon this model by treating wind direction as a circular variable and including it in the model. It is robust in many experiments, such as predicting wind at other locations. We compare this with the more common approach of modeling wind speeds and directions in the Cartesian space and use a skew-t distribution for the errors. The quality of the predictions from all of these models can be more realistically assessed with a loss measure that depends upon the power curve relating wind speed to power output. This proposed loss measure yields more insight into the true value of each model’s predictions.
Research on the Method of Quickly Finding the Pedestrian Area of Interest

In pedestrian detection based on car video, it is necessary to quickly and accurately detect pedestrians. However, in the past the method that people used is exhaustive search. The method needs to detect all the areas in the picture, which is a waste of time. Based on the purpose of reducing the area to be tested, we use the edge detection and the principle of camera imaging, and run the test in MATLAB to get the method of finding the region of interest quickly. This method can reduce the retrieval area and shorten the retrieval time. The method can meet the requirements of real-time in pedestrian detection. Compared with the exhaustive search method, the number of windows that the method requires is 1/33 of the number of windows that the exhaustive search method requires. The detection speed of this method is several times higher than that of the exhaustive search method. It can be seen that the method is effective.
Genetic Assessment of Three Colour Variants of African Yam Bean(Sphenostylis Stenocarpa) Commonly Grown in the Midwestern Region of Nigeria

Three varieties of African yam bean (Sphenostylis stenocarpa) were collected from six different locations in Edo State, Nigeria (Ekpoma, Benin City, Auchi, Igueben, Igbanke and Sabongida Ora). These seeds were then characterized based on seed colour into black, brown, and light grey. The seeds were screened in the field for agronomic and yield asso- ciated characters as well as chemical composition of the seeds. Considerable variations were observed in both agronomic and yield associated characters like shoot height, leaf area, grain yield and total ash content of the seeds. The black colour variant was significantly (p<0.05) highest in grain yield per hectare (1542.28kg/ha) compared to both brown variant (1304.23kg/ha) and the light grey type (1259.97kg/ha).
DEM Working Paper Series Bayesian operational risk models

Operational risk is hard to quantify, for the presence of heavy tailed loss distributions. Extreme value distributions, used in this context, are very sensitive to the data, and this is a problem in the presence of rare loss data. Self risk assessment questionnaires, if properly modelled, may provide the missing piece of information that is necessary to adequately estimate operational risks. In this paper we propose to embody self risk assessment data into suitable prior distributions, and to follow a Bayesian approach to merge self assessment with loss data. We derive operational loss posterior distributions, from which appropriate measures of risk, such as the Value at Risk, or the Expected Shortfall, can be derived. We test our proposed models on a real database, made up of internal loss data and self risk assessment questionnaires of an anonymous commercial bank. Our results show that the proposed Bayesian models performs better with respect to classical extreme value models, leading to a smaller quantification of the Value at Risk required to cover unexpected losses.
Coalescence, structure and reticulation analysis of genetic diversity in Prunus scoparia populations

Almond is an important food resource plant in Iran and over 30 Amygdalus (Prunus) species, subspecies or ecotypes have been reported in the country. Wild almond species can provide a gene pool of valuable characteristics for breeding the cultivated almond. The present study deals with a genetic diversity analysis of three P. scoparia populations by using nuclear ISSR markers, and tries to reveal genetic distinctness versus admixture of these populations. The three studied populations differed significantly in their molecular characteristics, allele polymorphism and expected heterozygosity and also showed isolation by distance. AMOVA test revealed significant genetic differences among the three populations (p<0.01). The Mantel test showed that genetic distance between the populations correlates with their geographical distance. Furthermore, Bayesian model-based clustering manifested a good separation of the populations at k = 3. However, according to the reticulation analysis, these populations were not completely isolated and some degree of gene exchange has occurred among them. The coalescence analysis also revealed horizontal gene transfer and gene exchange among these populations.
Evidentiality as a Causal Relation : A Case Study from Japanese youda

The use of youda is infelicitous for assertions where the speaker has direct evidence (2a) or which are made on the basis of general background knowledge (2b). The use of youda seems to require that the assertion be made on the basis of indirect evidence (2c). In this paper, we investigate how exactly youda marks a proposition as being based on ‘indirect evidence’. We argue for two main conclusions: (i) youda does not mark any kind of epistemic commitment on the part of the speaker, and (ii) youda marks indirect evidence by indicating a causal relation between its propositional complement and a contextually salient evidence source. Before providing evidence for these two conclusions, we rst give a brief overview of previous accounts of youda in section 2, focusing particularly on the account of McCready and Ogata (2007). Following Waldie (2012), we argue that evidentials in general encode a tripartite relationship between an agent a, an evidence source e , and target proposition p. We discuss the kind of relations that previous accounts posit between these three elements, before going on to discuss each one in detail, on the basis of both intuitionistic and experimental evidence. In section 3, we provide experimental support for the basic paradigm in (1). This paradigm establishes that youda requires a speci c evidence source, rather than mere background knowledge on the part of the speaker. It furthermore establishes that the relationship between the evidence
ALUMINIUM ALLOY ADDITION EFFECTS ON THE BEHAVIOUR OF SOFT MAGNETIC MATERIALS AT LOW FREQUENCIES

The present paper focused on the effects of aluminium alloy addition on the behaviour of soft magnetic materials at low frequencies. The microstructure investigation reveals that for materials with high aluminium contents, the pores are oriented near or surrounding the aluminium particles. The microstructure investigation reveals that for materials with high aluminium contents, the pores are oriented near or surrounding the aluminium particles, as well as after heat treatment shows coarse-grained structure with a minimum number of inclusions within the grains and at the grain boundaries. Results show that the magnetic properties are dependent on the structural state of the investigated material. Magnetic properties increased with decreasing density due to the enhanced densification by means of applied pressing pressure and promote porosity reduction during heat treatment.
Application of Rough Classification of Multi-objective Extension Group Decision-making under Uncertainty

On account of the problem of incomplete information system in classification of extension group decision-making, this paper studies attribution reduction with decision-making function based on the group interaction and individual preferences assembly for achieving the goal of rough classification of multi-objective extension group decision-making under uncertainty. Then, this paper describes the idea and operating processes of multi-objective extension classification model in order to provide decision-makers with more practical, easy to operate and objective classification. Finally, an example concerning practical problem is given to demonstrate the classification process. Combining by extension association and rough reduction, this method not only takes the advantages of dynamic classification in extension decision-making, but also achieves the elimination of redundant attributes, conducive to the promotion on the accuracy and the reliability of the classification results in multi-objective extension group decision-making. Keywords: extension group decision-making; matter-element analysis; extension association; rough set; attribution reduction
GROUP-BASED EXERCISE IMPROVES FITNESS PARAMETERS IN PREVIOUSLY SEDENTARY INDIVIDUALS WITH CANCER

INTRODUCTION : Exercise rehabilitation has previously been reported effective in attenuating numerous cancer treatment-related toxicities and enhancing the quality of life (QOL) of patients. However, approximately 85% of the patients we see at Maple Tree Cancer Alliance were previously sedentary. These individuals report experiencing much anxiety about starting an exercise program for the first time, and compliance with such programs has been an issue. Therefore, we created a group-based exercise class as an introduction to exercise. Our goal in doing so was to see the impact of this class on fitness parameters, QOL, and exercise compliance after the program ended. METHODS :82 individuals who were currently undergoing cancer treatment participated in this group-based exercise class. Their results were compared to 200 different individuals who completed one-on-one exercise training. Each group underwent a comprehensive fitness assessment and completed McGill QOL questionnaires at the start of their exercise training, and after 12-weeks of training.Compliance data was also measured for each group. Data was analyzed at the 0.05 level of significance using descriptive statistics RESULTS :Exercise has a positive impact on fitness parameters for both groups. On average, the one-on-one exercise group experienced greater improvements in all measured parameters. Likewise, QOL improved for both groups, but to a greater extent in the group exercisers. Finally, the group-based exercise had the highest compliance rate (70% vs. 32% in the one-on-one exercise group). (p<0.001). CONCLUSIONS :Based on these data, it appears as though exercise can improve fitness parameters during cancer treatment. The individualized, one-on-one approach is the most effective at improving fitness. However, compliance and QOL was higher for those who exercised in a group-based setting. Therefore, this form of exercise may be appropriate to help individuals who are new to exercise get started on a program in a relaxed, supportive atmosphere.
Statistical Issues in Studies of the Long-Term Effects of Air Pollution: The Southern California Children’s Health Study

In this article we discuss statistical techniques for modeling data from cohort studies that examine long-term effects of air pollution on children’s health by comparing data from multiple communities with a diverse pollution profile. Under a general multilevel modeling paradigm, we discuss models for different outcome types along with their connections to the generalized mixed effects models methodology. The model specifications include linear and flexible models for continuous lung function data, logistic and/or time-to-event models for symptoms data that account for misspecifications via hidden Markov models and Poisson models for school absence counts. The main aim of the modeling scheme is to be able to estimate effects at various levels (e.g., within subjects across time, within communities across subjects and between communities). We also discuss in detail various recurring issues such as ecologic bias, exposure measurement error, multicollinearity in multipollutant models, interrelationships between major endpoints and choice of appropriate exposure metrics. The key conceptual issues and recent methodologic advances are reviewed, with illustrative results from the Southern California Children’s Health Study, a 10-year study of the effects of air pollution on children’s respiratory health.
Dynamic credit models

contract is initiated, the borrower (or issuer) receives the initial notional of the bond from the lender; during the life of the bond, he pays to the lender appropriate coupons which represent the cost of using the loan, and on termination he returns the bond notional to the lender. We emphasize that the lender receives coupons and the notional only if the borrower honors his obligation; in the opposite case, the lender loses outstanding coupon payments and part of the notional. The possibility of the borrower’s default is reflected in the spread measured as the difference between the rate of return on this bond as required by the lender and the rate of return on US treasury bonds, which are considered to be default-free securities. The spread charged on the borrower’s bonds increases with the perceived probability of the default of this borrower.