O presente trabalho investiga se a inclusão financeira contribui para o desenvolvimento econômico no Brasil. A pesquisa foi desenvolvida através da análise estatística descritiva, além de regressão e correlação estatística, utilizando dados secundários coletados a partir do Banco Central do Brasil (BACEN), Programa de Desenvolvimento das Nações Unidas (PNUD), Fundo Monetário Internacional (FMI). O período de estudo do projeto compreendeu os anos de 2004 a 2017.

The study is an assessment of light, used in a restaurant, based on observation and interviews. The restaurant name is “Premier Family Dining”, the available lighting features are observed and interviews conducted with restaurateur, restaurant manager and a number of regular dinners to analyses that how they feel about restaurant lighting when stay there. The researcher’s assessment concluded that the lighting of the restaurant is used in excess for the aesthetical purpose such as lighting coming from the ceiling, walls, pillars, floor but with no control of voltage or intensity of light. Whether all the lighting features are used to attract customers but excessive use increase the expenses on electricity and create sharp beams of light to enter in the eyes of presences. The light is well used but needs a few alterations to bring the restaurant up to date and more functional than it is, such as the intensity of lights must be lowered down to create more effective ambience. The tables must be spot lit to avail the light for proper brightness on food. These alterations not require much of finance but they would bring a blooming change in the interiors of the restaurant.

Epigallocatechin gallate (EGCG), a natural polyphenolic phytomolecule obtained from green tea (Camellia sinensis L.). Due to its highly auto-oxidative nature, it shows poor physicochemical properties impeding its therapeutic applications. EGCG-β-CD complexes were formulated by a spray-drying method. Spray dried EGCG-β-CD were characterized for various physicochemical properties, stability and anti-oxidant potential using griess reaction. Spray dried microparticles showed a formation amorphous EGCG-β-CD complexes with significant reduction in mean particle size and improved stability. Further, spray dried EGCG- β-CD complexes showed NO reduction at effective lower concentration of 125 μg/ml as compared to EGCG preventing its auto-oxidative nature. The developed amorphous spray dried β-CD complexes can be commercialized with improved therapeutic properties.

Early diagnosis of leaf ailments is the most necessary and prominent way to increase agriculture production. In this paper, a computer-aided approach for classifying the ailments in plant leaves is proposed. Leaf disease classification is similar to other classification problems but varies significantly in the features that contribute to classification. According to this observation, a new feature selection technique is proposed which selects the set of features which can effectively characterize the leaf dataset based on Neutrosophic Cognitive Maps (NCM). Eight feature selection techniques are used for making performance comparison in order to show the ability of the proposed method’s performance. The proposed feature selection method provides a classification accuracy of 99.8 % when applied for apple leaf disease detection.

Background: A patient safety culture is something that absolutely must be applied and is the prominent foundation for patient safety, therefore, it could reduce the patient safety incidents at the hospital. The study aims to analyse the effect of patient safety culture toward patient safety incidents at the highest reference hospital in East Kalimantan.\nMethods: Analytical study with the cross-sectional design is applied. There were 119 nurses in 17 rooms as sample. The sample selection was conducted by proportional random sampling. The patient safety culture with 12 dimensions was an independent variable while the dependent variable was a safety incident. Data collection using a questionnaire refers to the AHRQ at the Hospital Survey on Patient Safety Culture in 2017. The patient safety incident questionnaire was prepared based on the incident data that often occurs in the Hospital. Data were analyzed by multiple linear regressions.\nResults: The average nurse\'s perception of the patient safety culture was 2.97 (SD ± 0.22). The lowest mean perception on the dimension of non-punitive response to errors was 2.65 (SD ± 0.60), while the mean of the highest perceptions in the dimensions of collaboration in units was 3.43 (SD ± 0.43). The most common incident was phlebitis (8.4%). There was an influence of management support (p = 0.038, R = 5,633), nurses\' perceptions (p = 0.015, R = 5,609) and cooperation between units (p = 0.006, R = 6,646) toward the incidents of patient safety.\nConclusion: Management support, nurse perceptions, and collaboration between units affect Patient Safety Incidents. These three dimensions of patient safety culture need to be improved, consequently, the incidence of patient safety at the hospital could decrease.

Purpose\nThis paper assessed cowpea farmers’ vulnerability to climate change and as well ascertained the adaptation strategies used in Iddo local government area of Oyo state in Nigeria. For effective adaptation to climate change impacts and sustainable cowpea supply security in Nigeria\nMethod\n Following a survey of 108 randomly selected cowpea farmers, descriptive and infrential statistical tools were used to analysed data collected at 5 percent level of significant. \nResults \nFarmers (85.1%) in the study area were highly susceptible to climate change impacts. significant correlation existed between farm size (r = 0.2), intensity of vulnerability to climate change (r=0.1) and climate change adaptation technology used. Their proactive solutions include combining various forms of adaptation strategies according to farm sizes and the intensity of their vulnerability. \nConclusion\nFor effective climate change or variability resilience, adaptation policy for smallholder cowpea farming should require inputs from the local experts with understanding of local climatic variability and change of the farming community.

Due to the shortcomings of low utilization of high frequency bands, fixed waveform parameter configuration, and high synchronization requirements, OFDM technology is no longer suitable for new scenarios in future communications. The F-OFDM new waveform technology has the advantage of flexible parameter configuration, and can better adapt to new communication needs in the future. The system uses a low-complexity window function method to design the filter, and the frequency domain filtering method is selected by comparing the frequency domain filtering with the time domain filtering. At the same time, considering the system\'s anti-interference ability and spectral efficiency in a compromise, a scheme for setting guard subcarriers in the passband and transition band of the filter is proposed. Finally, the feasibility of this solution is proved by simulation and analysis of parameters such as the position and number of guard subcarriers, moving speed, and system throughput, and, it was found that the F-OFDM system has better throughput and better system performance than the OFDM system.

Indian music is broadly classified into North Indian Hindustani music and South Indian Carnatic music. Both these systems of music are rich in their own style. But Carnatic music is more complex in the way the notes are arranged and rendered, compared to any other type of music. Raga is the backbone of Indian classical music. In Carnatic music there are 72 melakarta ragas where each raga is denoted by predefined arrangement of musical notes. In context of computational musicology of Indian music, automatic raga recognition is a significant step and has numerous applications like indexing, automatic note transcription, comparing, classifying tunes to mention a few. The chromagram also known as Harmonic Pitch Class Profile (HPCP) is an important tonal feature of music, showing the distribution of energy along the pitches or pitch classes. This paper focuses on classifying the Melakarta ragas using chromagram.

Mobile robot is a future. Robots can do work of 10, 100 or 1000 humans at a time. In previous era, there used be some specified tasks which were assigned to robot and hence it has to do the required task, the computation of request response model becomes quite simple, as there is a fixed inputs and outputs. In recent year, there was a considerable progress in mobile robot. As robot has to perform different tasks, they are present in the different shape with different purpose. This robot can help human in their everyday life and routine. These are not an indoor robot which was designed to perform desired task, but they can adapt to our environment by themselves and to learn from their own experiences. In this research we focus on high degree of autonomy, which is a must for social robots. For training purpose autonomous exploration and unknown environments is used along with proper algorithm so that robot can adapt to unknown environments. For testing purpose, simulation is carried with sensor fusion method, so that real world noise can be reduced and accuracy can be increased. This dissertation focuses on the intelligent robot control in autonomous navigation tasks and investigates the robot learning in following aspects. This method is based on human instinct of imitation. In this standard real time data set is provided to the robot for training purpose, it gets train from these data and generalize over all unseen potential situations and environments. Convolutional Neural Network is used to determine the probability and based on that robot can act. After acceptable number of demonstrations, robot can predict output with high accuracy and hence can acquire the independent navigation skills. State-of-the-art reinforcement learning techniques is used to train the robot via interaction with the robots. Convolutional Neural Network is also incorporated for fast generalization. Robot is train based on all past state-action pairs collected during interaction. This training model can predict output which helps robot for autonomous navigation.