Miracle-dz/UniFAR-FTUs huggingface dataset:
Ownership concentration and bank pro fi tability

We investigate whether ownership concentration influences bank profitability in a developing country context. We focus on bank ownership concentration measured as the amount of direct equity held by a majority shareholder categorised into: high ownership concentration, moderate ownership concentration and disperse ownership. We find that banks with high ownership concentration have higher return on assets, higher net interest margin and higher recurring earning power while banks with dispersed ownership have lower return on assets but have higher return on equity. Also, higher cost efficiency improves the return on assets of widely-held banks and the return on equity of banks with moderate ownership. The findings have implications. & 2017 Faculty of Commerce and Business Administration, Future University. Production and Hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). JEL: Code: G3; G34; G31
Statistical Modeling for Analyzing Growth Using Weight and Height of Thai Children Aged 0-8.5 Years in the Prospective Cohort Study in the South of Thailand

This study aimed to investigate the patterns of children’s growth and to fit an appropriate statistical model for children’s growth in South of Thailand. The study included 1,061 children from at birth in the prospective cohort study conducted in Thepa district in Songkhla province, Thailand. Linear regression model by taking reciprocal weight gain was used to analyze the weight gain at the first year of life base on feeding types at aged 21 days, 3 months, 6 months and 12 months as breast, mixed and bottle feeding. Feeding type was significant associated with weight gain in the first years of life. Weight gain in both boys and girls fed with formula feeding at age 21 days, 6 and 12 months were significantly higher than weight gain in breast milk and mixed method. Weight gain fed with mixed methods at aged 3 months was significantly higher weight gain of breast milk and formula feeding for both boys and girls. There was statistical association between type of feeding for one year with weight and height gain at aged 1, 3, 5 and 8.5 years. The height gain of boys at aged 1, 3 and 5 years and girls at aged 3.5, and 8.5 years fed by breast milk for one year were significantly lower than height gain by breast feeding and mixed methods. Infant feeding type is significant factor for determining weight gain at first year of life. Implementation the knowledge of nutrition in infant feeding methods for mothers should be emphasized.
Combined Occurrence of Wind, Snow Loading and Soil Frost with Implications for Risks to Forestry in Finland under the Current and Changing Climatic Conditions

This work focuses on the combined occurrence of wind, snow loading and soil frost with implications for risks to forestry in Finland under the current and changing climatic conditions. For this purpose, we employ meteorological datasets, available for the period of 1971–2009 and global climate model (GCM) simulations for the current climate 1971–2000, and periods 2046–65 and 2081–2100 applying the A1B-climate change scenario. Based on our results, the wind and snow induced risks to Finnish forests are projected to increase in the future although the change in the occurrence of strong winds is small. This is because soil frost depths that support tree anchorage from late autumn to early spring in Finland are projected to nearly disappear in the southern and central parts of the country. Heavy snow loads >30 kg m–2 are becoming more common in southern and eastern Finland despite that the average cumulative 5-day snow loads decrease in these areas by 18 to 50%, respectively. As a result of the changes in the combined occurrence of wind, snow loading and soil frost, the risk of climatic conditions making conifers liable to uprooting are projected to increase in southern, central and eastern Finland. In the north, the risk of stem breakage is becoming more pronounced under snow loading >20 kg m–2. Despite some uncertainties related to this work, we assume that the findings can serve as valuable support for the risk assessment of wind and snow induced damages to Finnish forests and for forestry, in general.
Gravitational radiation from neutron stars deformed by crustal Hall drift

Aprecondition for the radio emission of pulsars is the existence of strong, small-scalemagnetic field structures (‘magnetic spots’) in the polar cap region. Their creation can proceed via crustal Hall drift out of two qualitatively and quantitatively different initial magnetic field configurations: a field confined completely to the crust and another which penetrates the whole star. The aim of this study is to explore whether these magnetic structures in the crust can deform the star sufficiently to make it an observable source of gravitational waves. We model the evolution of these field configurations, which can develop, within ∼104–105 yr, magnetic spotswith local surface field strengths∼1014 Gmaintained over�106 yr. Deformations caused by the magnetic forces are calculated.We show that, under favourable initial conditions, a star undergoing crustal Hall drift can have ellipticity � ∼ 10−6, even with sub-magnetar polar field strengths, after ∼105 yr. A pulsar rotating at ∼102 Hz with such � is a promising gravitational wave source candidate. Since such large deformations can be caused only by a particular magnetic field configuration that penetrates the whole star and whose maximum magnetic energy is concentrated in the outer core region, gravitational wave emission observed from radio pulsars can thus inform us about the internal field structures of young neutron stars.
Studies on Preparation of Squash from Sweet Orange

in production of sweet orange with annual production of 4266.9 million tonnes. In Maharashtra the citrus is grown in district of Ahmednagar, Nasik, Pune. The nucellar mosambi, mukhed seedless and raja poyi are the major cultivars of sweet orange cultivated in Maharashtra. In Parbhani district, nucellar mosambi and rajapimpari are the major cultivars of sweet orange. The chemical composition of sweet orange shows that it contains water (86-92%), sugar (5-8%), pectin (1-2%), glycosides (0.1-1.5%), pentosans (0.8-1.2%), citric acid (0.4 to 1.5%), fibre (0.6-0.9%), proteins (0.6-0.8%), fat (0.2-0.5%), minerals (0.5-0.9%) and essential oils (0.2-0.5%) [1]. The sweet orange fruit is processed commercially in to various forms mainly juice, frozen concentrates, squash, RTS drinks, nectar, dry mixes, canned segments, juice blends, marmalades and other value added products like pectin and essential oil from peel, natural colors, candied peel, feed yeast etc. Fresh juice of sweet orange is an important nutritious product providing 45 kcal, moderate quantity of vitamin C, potassium, bioflavonoid and folic acid and essential items of breakfast. It is refreshing, thirst quenching and energizing drink of breakfast. It is refreshing, thirst quenching and energizing drink that improves health and nutritional requirements.
Inclusion of Some Raw Legume Grains as Broiler Chicks Concentrates

A total of 200 broiler chicks were used to evaluate the effect of feeding plant concentrate substitute the imported concentrate on the performance and blood composition of broilers. Four dietary treatments containing 0%, 5%, 10% and 15% plant concentrate were formulated to meet the nutrients requirements as outlined by NRC (1994). The experiment was in a Completely Randomize Design (CRD) comprised broiler chick fed iso caloric and iso nitrogenous diet. Each treatment was replicated five times with 10 birds/replicate. The study lasted for six weeks. Parameters measured was feed intake, body weight gain, Feed Conversion Ratio (FCR), pre-slaughter weight, dressing percentage, protein efficiency, some blood parameters (glucose, cholesterol, triglyceride, total lipid, total protein, calcium and phosphorus) and profitability. Result revealed that increasing dietary level of plant concentrate had significantly (p 0.05) difference in blood serum glucose, total protein, calcium and phosphorus. However, birds fed on diets containing 5%, 10% and 15% plant concentrate significantly (p<0.05) recorded lower serum cholesterol than control group.
A Study of the Process of Commissioning New Music for the Concert Band

This study examined the process of commissioning a new composition for the concert band. Composer Brian Balmages was commissioned to write a piece for concert band that had standard band instrumentation and was of Grade IV difficulty. The Temple of the Murals was given its world premiere performance on October 30, 2010 on the campus of Garden City Community College in Garden City, Kansas by an honor band (N=41) comprised of high school and college musicians from the Southwest Kansas region. The Ensemble Members Attitude Survey (EMAS) was given to the participants as a pre-survey at the beginning of the clinic sessions and again as a post-survey before the premiere performance. At the same time, a qualitative study was employed to examine the rationales that wind band conductors and composers may have for commissioning new music for the concert band. Participants in the qualitative study (N=4) were interviewed three separate times using similar interview questions. The researcher then transcribed the interviews and formulated themes from the data. An external reviewer was employed to review the transcripts of the interviews. Results largely indicated no significant differences between pre and post administrations of the survey. Post-administration outcomes demonstrated participants' positive orientation towards the commissioned work and the composer, specific musical concepts, and new music in general. Themes to emerge from the qualitative study included the notions of connectivity with students, a desire to improve and expand the repertoire of the wind band, and providing students with a unique musical experience. Connections were found between the survey and qualitative aspects of the study.
Numerical solution of three-dimensional rectangular submerged jets with the evidence of the undisturbed region of flow

ABSTRACT The evolution of turbulent rectangular submerged free jets has been investigated numerically with a two-dimensional (2D) approach by the present authors and, by using the large eddy simulations (LES) at several Reynolds numbers. The average numerical results confirmed the presence of the undisturbed region of flow (URF) located between the slot exit and the beginning of the potential core region (PCR) previously observed experimentally at the University of Rome “Tor Vergata” by Gori and coworkers. The 2D study of the present authors carried out under the conditions previously investigated in the literature, showed that the URF has a self-similar behavior, and proposed a new law for the evolution of the momentum. The present paper extends the LES to three-dimensional (3D) rectangular submerged free jets, in the range from Re = 5,000 to Re = 40,000, showing that the self-similar behavior of URF is also present in the 3D numerical simulations, as well as in the PCR and in the fully developed region (FDR).
Ebola virus disease outbreak in Nigeria: lessons to learn

International air travel has already spread Ebola virus disease (EVD) to major cities as part of the unprecedented epidemic that started in Guinea in December 2013. An infected airline passenger arrived in Nigeria on July 20, 2014 and caused an outbreak in Lagos and then Port Harcourt. After a total of 20 reported cases, including 8 deaths, Nigeria was declared EVD free on October 20, 2014. We quantified the impact of early control measures in preventing further spread of EVD in Nigeria and calculated the risk that a single undetected case will cause a new outbreak. We fitted an EVD transmission model to data from the outbreak in Nigeria and estimated the basic reproduction number R0 = 9.0 (95% confidence interval [CI]: 5.2‐15.6). We also found that the net reproduction number Rt fell below unity 15 days (95% CI: 11‐21 days) after the arrival of the index case. Using the estimated value of R0 in Nigeria, we calculated that the risk of an outbreak from a single undetected case was 89% (95% CI: 81‐94%). Even though R0 in Nigeria was high, EVD outbreaks caused by infected air travelers can be successfully contained if control measures are rapidly implemented.
The effectiveness of seminars in educating adults in financial literacy

In Australia, financial literacy is not given prominence within the education system, and it is a general view that financial literacy is gained through ‘hands on’ experience in earning and spending money; further, financial education seems to occur only when people take a loan or experience financial difficulties (Hajaj, 2002). This is not sufficient if people need to make informed decisions about their investments and, because of a number of social factors, it has become necessary to educate the majority of the adult population in Australia in Financial Literacy over a relatively short period of time. Given the large numbers requiring such an education, the seminar approach is being widely used, however is it effective? Drawing on the adult education literature, this study examined the effectiveness of the seminar approach by surveying adult participants in a Financial Literacy seminar. The survey found that while a majority of participants (50%) expressed none or weak financial knowledge prior to attending the seminar and 45% expressed that they only had a moderate rate of financial knowledge, a majority of 63% strongly agreed or agreed to the seminar improving their knowledge of the need for retirement savings. Furthermore, 58% of participants were either confident or very confident of being able to apply what they learned in the seminar to achieving their retirement savings goal. These findings suggest that the seminar approach was effective in educating adults and improved their level of financial literacy. Future research could investigate whether the level of financial knowledge gained during the seminar is retained over a considerable period of time.
Study of the inhibitory effect of the media culture parameters and cell population to increase the biomass production of Dunaliella tertiolecta Progress in Biological Sciences

Microalgal growth curve, after the exponential phase, shows a stationary phase where algal biomass production is inhibited and remains constant by some factors such as nutrient depletion and intrinsic behavior. The present study is concerned with evaluating effects of the culture medium condition and intrinsic behavior on biomass production by Dunaliella tertiolecta. To this end, effect of pH, nutrient concentration, CO 2 (NaHCO 3 ) concentration, possible secreted substances, and cell density on the biomass production by D. tertiolecta were investigated. The results showed that biomass yield can be significantly affected by pH (p<0.01) and nutrient (p<0.05). In a combination of pH and nutrient, the biomass was found to be more influenced by the pH. The results showed a significant interaction between nutrient and NaHCO 3 (p<0.05), suggesting that CO 2 concentration may limit biomass production only when sufficient nutrients are available. Nutrients concentration and biomass production showed a direct correlation (p<0.05). The rate of reaching the maximum biomass was showed to be increased in higher nutrients concentration; however, the maximum point could not to be affected. The existence of secreted compounds with inhibitory effect on the growth was not observed. The inhibitory effect of the cell density on biomass production can not be confirmed.
Neural Collaborative Autoencoder

In recent years, deep neural networks have yielded state-of-the-art performance on several tasks. Although some recent works have focused on combining deep learning with recommendation, we highlight three issues of existing models. First, these models cannot work on both explicit and implicit feedback, since the network structures are specially designed for one particular case. Second, due to the difficulty on training deep neural networks, existing explicit models do not fully exploit the expressive potential of deep learning. Third, neural network models are easier to overfit on the implicit setting than shallow models. To tackle these issues, we present a generic recommender framework called Neural Collaborative Autoencoder (NCAE) to perform collaborative filtering, which works well for both explicit feedback and implicit feedback. NCAE can effectively capture the subtle hidden relationships between interactions via a non-linear matrix factorization process. To optimize the deep architecture of NCAE, we develop a three-stage pre-training mechanism that combines supervised and unsupervised feature learning. Moreover, to prevent overfitting on the implicit setting, we propose an error reweighting module and a sparsity-aware data-augmentation strategy. Extensive experiments on three real-world datasets demonstrate that NCAE can significantly advance the state-of-the-art.
Training on the test set? An analysis of Spampinato et al. [31]

A recent paper [31] claims to classify brain processing evoked in subjects watching ImageNet stimuli as measured with EEG and to use a representation derived from this processing to create a novel object classifier. That paper, together with a series of subsequent papers [8, 15, 17, 20, 21, 30, 35], claims to revolutionize the field by achieving extremely successful results on several computer-vision tasks, including object classification, transfer learning, and generation of images depicting human perception and thought using brain-derived representations measured through EEG. Our novel experiments and analyses demonstrate that their results crucially depend on the block design that they use, where all stimuli of a given class are presented together, and fail with a rapid-event design, where stimuli of different classes are randomly intermixed. The block design leads to classification of arbitrary brain states based on block-level temporal correlations that tend to exist in all EEG data, rather than stimulus-related activity. Because every trial in their test sets comes from the same block as many trials in the corresponding training sets, their block design thus leads to surreptitiously training on the test set. This invalidates all subsequent analyses performed on this data in multiple published papers and calls into question all of the purported results. We further show that a novel object classifier constructed with a random codebook performs as well as or better than a novel object classifier constructed with the representation extracted from EEG data, suggesting that the performance of their classifier constructed with a representation extracted from EEG data does not benefit at all from the brain-derived representation. Our results calibrate the underlying difficulty of the tasks involved and caution against sensational and overly optimistic, but false, claims to the contrary.
A Reinforcement Learning Hyper-Heuristic in Multi-Objective Single Point Search with Application to Structural Fault Identification

Multi-objective optimizations are frequently encountered in engineering practices. The solution techniques and parametric selections however are usually problem-specific. In this study we formulate a reinforcement learning hyper-heuristic scheme, and propose four low-level heuristics which can work coherently with the single point search algorithm MOSA/R (Multi-Objective Simulated Annealing Algorithm based on Re-seed) towards multi-objective optimization problems of general applications. Making use of the domination amount, crowding distance and hypervolume calculations, the proposed hyper-heuristic scheme can meet various optimization requirements adaptively and autonomously. The approach developed not only exhibits improved and more robust performance compared to AMOSA, NSGA-II and MOEA/D when applied to benchmark test cases, but also shows promising results when applied to a generic structural fault identification problem. The outcome of this research can be extended to a variety of design and manufacturing optimization applications.
RNNFast

Recurrent Neural Networks (RNNs) are an important class of neural networks designed to retain and incorporate context into current decisions. RNNs are particularly well suited for machine learning problems in which context is important, such as speech recognition and language translation. This work presents RNNFast, a hardware accelerator for RNNs that leverages an emerging class of non-volatile memory called domain-wall memory (DWM). We show that DWM is very well suited for RNN acceleration due to its very high density and low read/write energy. At the same time, the sequential nature of input/weight processing of RNNs mitigates one of the downsides of DWM, which is the linear (rather than constant) data access time. RNNFast is very efficient and highly scalable, with flexible mapping of logical neurons to RNN hardware blocks. The basic hardware primitive, the RNN processing element (PE), includes custom DWM-based multiplication, sigmoid and tanh units for high density and low energy. The accelerator is designed to minimize data movement by closely interleaving DWM storage and computation. We compare our design with a state-of-the-art GPGPU and find 21.8× higher performance with 70× lower energy.
Mining Interpretable AOG Representations From Convolutional Networks via Active Question Answering

In this paper, we present a method to mine object-part patterns from conv-layers of a pre-trained convolutional neural network (CNN). The mined object-part patterns are organized by an And-Or graph (AOG). This interpretable AOG representation consists of a four-layer semantic hierarchy, i.e., semantic parts, part templates, latent patterns, and neural units. The AOG associates each object part with certain neural units in feature maps of conv-layers. The AOG is constructed with very few annotations (e.g., 3–20) of object parts. We develop a question-answering (QA) method that uses active human-computer communications to mine patterns from a pre-trained CNN, in order to explain features in conv-layers incrementally. During the learning process, our QA method uses the current AOG for part localization. The QA method actively identifies objects, whose feature maps cannot be explained by the AOG. Then, our method asks people to annotate parts on the unexplained objects, and uses answers to discover CNN patterns corresponding to newly labeled parts. In this way, our method gradually grows new branches and refines existing branches on the AOG to semanticize CNN representations. In experiments, our method exhibited a high learning efficiency. Our method used about <inline-formula><tex-math notation="LaTeX">$1/6$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:mn>6</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="zhang-ieq1-2993147.gif"/></alternatives></inline-formula>–<inline-formula><tex-math notation="LaTeX">$1/3$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="zhang-ieq2-2993147.gif"/></alternatives></inline-formula> of the part annotations for training, but achieved similar or better part-localization performance than fast-RCNN methods.