Miracle-dz/UniFAR-FTUs huggingface dataset:
AEROBIC COMPOSTING OF CASSAVA PEELS USING COWDUNG, SEWAGE SLUDGE AND POULTRY MANURE AS SUPPLEMENTS

Cassava peels, poultry manure, cowdung, sewage sludge and grass clippings are common wastes with resourceful fertilizer value but often indiscriminately discarded, thereby constituting environmental pollution. This paper studied the use of poultry manure, cowdung and sewage sludge as supplements for composting of cassava peels. In this study, these wastes were used to prepare different composts. Compost pile A was prepared by mixing cassava peels with sewage sludge and grass clipping in a ratio of 1:1:1 by weight. The second pile B was prepared by combining cassava peels, poultry manure and grass clippings also in 1:1:1 ratio by weight. The third pile C had a blending of cassava peels, cowdung and grass clippings in a ratio of 1:1.5:1 by weight. The fourth pile a control D was prepared by mixing cassava peels with grass clippings in ratio of 1:1 by weight. In all the piles, Total Organic Carbon (TOC) losses had significant effect on N, P,K, Na, Ca, and Mg content. There existed significant negative correlations between TOC losses and other nutrients. The composted products containing the various supplements had higher contents of micro nutrients such as N, P, K than pile D (control) without supplement. The Ca, Na and Mg contents were higher in composts with supplements with the exception of pile C. The E. coli values for all piles fall within the USEPA limits of less than 3 x 10 4 (cfu/g). Pile B slightly got to maturity state before piles A and C, while pile D (control) took much longer period. Cassava peels, sewage sludge, cowdung and poultry manure are potential materials for a good compost but poultry manure induced the early maturity of the composting than other supplements and contributed to the nutritional content of the compost.
"ENERGY" DRINKS: HELP, HARM OR HYPE?

SUMMARY Athletes will always be attracted to products that claim to have performance-enhancing effects. Energy drinks are not adequate substitutes for the time, training, rest, recovery, and fueling required for sports. Athletes must take the responsibility for what goes into their bodies, which includes being informed as well as cautious about dietary supplements. Educating athletes about these products is critical for their health, safety, and sport performance. REFERENCES American Dietetic Association, Dietitians of Canada, and the American College of Sports Medicine (2000). Position of the American Dietetic Association, Dietitians of Canada, and the American College of Sports Medicine: Nutrition and Athletic Performance. J. Am. Diet. Assoc . 100:1543-1556. Bergeron, M.F. (1996). Heat cramps during tennis: a case report. Int. J. Sport Nutr. 6:62-68. Brass, E.P. (2000). Supplemental carnitine and exercise. Am. J. Clin. Nutr . 72(2 Suppl):618S-623S. Carrithers, J.A. , D.L. Williamson, P.M. Gallagher, M.P. Godard, K.E. Schulze, and S.W. Trappe (2000). Effects of postexercise carbohydrate-protein feedings on muscle glycogen restoration. J. Appl. Physiol . 88:1976-1982. Casa D.J., L.E. Armstrong, S.K. Hillman, S.J. Montain, R.V. Reiff, B.S.E. Rich, W.O. Roberts, and J.A. Stone (2000). National Athletic Trainers' Association Position Statement: Fluid Replacement for Athletes. J. Athl. Train . 35:212-224. Cheuvront, S.N. , R.J. Moffatt, K.D. Biggerstaff, S. Bearden, and P. McDonough (1999). Effect of ENDUROX
The Localization of Netrin 4 in Vivo

Background: Ntn4 derived from vascular endothelial cells inhibits differentiation of osteoclasts and Ntn4 administration prevents receptor activator of nuclear factor-κB ligand-induced osteoporosis in mice. In this study, the localization of Ntn4-expressing vascular endothelial cells located near osteoclasts and the level of Ntn4 in ovariectomized mice were determined. Materials and methods: We performed drill-hole injury experiments and TRAP staining followed by immunostaining for PECAM-1 at day 14. We also investigated whether Ntn4 expression was changed in the serum of ovariectomized osteoporotic mice. Results: TRAP-positive cells were also found in immediate vicinity of Ntn4-positive vascular endothelial cells during bone healing. We found that serum Ntn4 levels were significantly decreased in ovariectomized osteoporotic mice. Conclusion: Taken together, Ntn4 may have a critical role for bone metabolism with characteristic localization.
Effect of Feeding Bypass Fat on Feed Intake, Milk Production and Body Condition of Holstein Friesian Cows

Bypass fat, produced from rapeseed acid oil (a by-product of oil refining process having 80 per cent degree of saponification), was incorporated in the ration of Holstein Friesian (HF) cows @500g/head/d for a period of six months. Feeding bypass fat significantly (P<0.05) improved milk yield 1 and 4% fat corrected milk (FCM) without affecting total DM intake. The initial body condition score of 1.0 of the experimental cows was changed to 0.5 in control and 1.5 in experimental cows at the end of trial period, indicating that cows in control group lost more body weight. Average period for conception after calving was 92 d in experimental cows compared to 118 d in control.
Perspectives on hope of Hospice and Palliative Care nurses

Fostering hope for clients experiencing end of life is an essential component of Hospice and Palliative Care nursing practice. There is a limited repertoire of literature that describes the perspectives on hope held by Hospice and Palliative Care nurses. The purpose of this doctoral research study was to describe the perspectives on hope of Hospice and Palliative Care nurses who work in one Hospice and Palliative Care organization in the northeast. The specific research questions that guided this inquiry included: How do Hospice and Palliative Care nurses describe the concept of hope? In what ways do the nurses think that hope is helpful to clients? What nursing interventions do nurses utilize to facilitate clients' hopes? A qualitative methodology utilizing a self-report questionnaire on hope was used to collect data from Hospice and Palliative Care nurses who work in inpatient and home care settings. Manifest content analysis was the research methodology utilized for analysis of the data from nine open-ended surveys returned by the nurses. The perspectives on hope of the Hospice and Palliative Care nurses illuminated two potentially new critical attributes of hope to include spirituality and interconnectedness. The nurses described hope as being helpful to clients by allowing them to stay positive, stay in control, feel safe and find meaning in their circumstances. Descriptions of nursing strategies that foster hope did not consistently demonstrate integration of the elements of connectedness and spirituality into their interventions. Organizational issues, such as decrease in Hospice client lengths of stays, with a resulting increase in client acuity and conflicting end-of life-care plans may have been factors that impacted on these nurses’ descriptions. It is not known what individual factors may have influenced the Hospice and Palliative Care nurses’ perspectives on hope. Further research is required to understand the circumstances that may impact on Hospice and Palliative Care nurses’ perspectives on hope.
How to tell Real From Fake? Understanding how to classify human-authored and machine-generated text

Natural Language Generation (NLG) using Generative Adversarial Networks (GANs) has been an active field of research as it alleviates restrictions in conventional Language Modelling based text generators e.g. Long-Short Term Memory (LSTM) networks. The adequacy of a GAN-based text generator depends on its capacity to classify human-written (real) and machine-generated (synthetic) text. However, traditional evaluation metrics used by these generators cannot effectively capture classification features in NLG tasks, such as creative writing. We prove this by using an LSTM network to almost perfectly classify sentences generated by a LeakGAN, a state-of-the-art GAN for long text generation. This thesis attempts a rare approach to understand real and synthetic sentences using meaningful and interpretable features of long sentences (with at least 20 words). We analyse novelty and diversity features of real and synthetic sentences, generate by a LeakGAN, using three meaningful text dissimilarity functions: Jaccard Distance (JD), Normalised Levenshtein Distance (NLD) and Word Mover’s Distance (WMD). In particular, these functions focus on (1) the number of common words, (2) the order of these words, and (3) the semantic similarity in both sentence types, making them interpretable. We provide a comprehensive investigation to identify the effectiveness of novelty and diversity, in classifying real and synthetic sentences, by training two different classification algorithms of varying complexities. Our evaluations show that sentence diversities, using JD and NLD, are the most effective features for classification of human-authored and machine-generated sentences.
A Personalized Sentiment Model with Textual and Contextual Information

In this paper, we look beyond the traditional population-level sentiment modeling and consider the individuality in a person’s expressions by discovering both textual and contextual information. In particular, we construct a hierarchical neural network that leverages valuable information from a person’s past expressions, and offer a better understanding of the sentiment from the expresser’s perspective. Additionally, we investigate how a person’s sentiment changes over time so that recent incidents or opinions may have more effect on the person’s current sentiment than the old ones. Psychological studies have also shown that individual variation exists in how easily people change their sentiments. In order to model such traits, we develop a modified attention mechanism with Hawkes process applied on top of a recurrent network for a user-specific design. Implemented with automatically labeled Twitter data, the proposed model has shown positive results employing different input formulations for representing the concerned information.
2 2 O ct 2 01 4 Spacetime Average Density ( SAD ) Cosmological Measures ∗

The measure problem of cosmology is how to obtain normalized probabilities of observations from the quantum state of the universe. This is particularly a problem when eternal inflation leads to a universe of unbounded size so that there are apparently infinitely many realizations or occurrences of observations of each of many different kinds or types, making the ratios ambiguous. There is also the danger of domination by Boltzmann Brains. Here two new Spacetime Average Density (SAD) measures are proposed, Maximal Average Density (MAD) and Biased Average Density (BAD), for getting a finite number of observation occurrences by using properties of the Spacetime Average Density (SAD) of observation occurrences to restrict to finite regions of spacetimes that have a preferred beginning or bounce hypersurface. These measures avoid Boltzmann brain domination and appear to give results consistent with other observations that are problematic for other widely used measures, such as the observation of a positive cosmological constant. Alberta-Thy-10-14, arXiv:1406.0504 [hep-th] Internet address: profdonpage@gmail.com
Performance Mapping Of Optical Mark Recognition (Omr) Machines In The Division Of Batangas City: Basis For Program Enhancement

This study aimed to determine the performance mapping of OMR machines in terms of usage, advantages and disadvantages and map the results of the seven machines, compare the results done by the teacher manually in order to recommend a program on the utilization of the OMR to uplift the performance of the students different schools of the division of Batangas City. The study used qualitative design using descriptive-thematic synthesis and quantitative using correlation of statistics results. Purposive sampling was utilized in choosing seven (7 )participants from seven (7) schools with OMR machines in the Division of Batangas City. A total of seven (7) OMR machines were used to map the performance, seven (7) teacheroperators were interviewed in gathering data. The statistical tools used were frequency mean and t-test. The study revealed that OMR machine is useful in data scanning scannable answer sheets of students quarter examinations, it gives quick and accurate assessment results but enable to process thin paper, and improper shading, and sometimes experienced technical problem just like any other machines. Meanwhile, all the seven (7) machines tested generated exactly the same results for a specific examination sample in mathematics. Further results shows that there is no significant difference between the results taken from the OMR machines and teacher’s computed results. Thus, the proposed program maybe used to enhance the utilization of OMR machines in the division and uplift students achievement. The results of this study may remove the doubt of the users and the division personnel on the reliability and validity of the data scanned by the machines. Thus, the results may be used to plan and implement a more comprehensive program in order to raise the achievement of the learners within the division.
A cost-effectiveness analysis of introducing pneumococcal vaccine in the Swedish vaccination program

Background Streptococcus pneumoniae is a frequent cause of bacterial meningitis, septicaemia, pneumonia and acute otitis media. These diseases lead to substantial mortality, morbidity and costs. There is a 7-valent pneumococcal conjugate vaccine (PCV-7) developed, that has proved to be highly effective against invasive disease, but has also provided significant protection against pneumonia and acute otitis media. Objective The objective of this study was to evaluate the projected health benefits, costs and cost-effectiveness of vaccination with the 7-valent conjugated pneumococcal vaccine compared with no vaccination, in all infants in Sweden. Methods An economic model (Markov model) was used in order to perform a costutility and a cost-effectiveness analysis, comparing a universal pneumococcal vaccination programme with a no-vaccination programme. The main outcomes were measured by reduction in the disease burden, costs and net costs of vaccination (per individual and a whole birth cohort), incremental cost per Quality adjusted life years (QALY) and life year (LY) gained. Results In a cohort of 95,000 infants, vaccination could potentially prevent 9 cases of pneumococcal meningitis, 22 cases of pneumococcal septicaemia, 509 cases of pneumonia, 7812 cases of acute otitis media, and 2.7 fatalities, among children 04 years of age. As a result of herd immunity effect; a total of 6 episodes of pneumococcal meningitis and 167 cases of pneumococcal septicaemia among adults could potentially be avoided. The model predicted vaccination to cost MSEK 148 (three doses) or MSEK 196 (four doses). The potential cost saving as a result of reduced burden of disease was estimated to MSEK 158 (MSEK 87.4 in direct costs and MSEK 70.3 in indirect costs). The incremental cost per QALY and LY gained was estimated to be negative (cost-saving) for an implementation of a three doses vaccination programme or SEK 274,000 and SEK 482,000 in case of four doses of vaccine. When herd immunity was accounted for, the cost per QALY and LY gained was estimated to be cost-saving (with three doses) or SEK 51,500 and SEK 61,500 respectively (four doses).
Old Italian versus new violins: the soloists’ perspective

C. Fritza, J. Curtinb, J. Poitevineaua, H. Borsarelloc, I. Wollmana, F. -C. Taod and T. Ghasarossiane aLAM / IJLRA, 11 rue de Lourmel, 75015 Paris, France bJoseph Curtin Studios, 3493 W. Delhi Rd, Ann Arbor, MI MI48103, USA cMusica Prima, 208 rue Diderot, 94300 Vincennes, France d4 D’Addario & Company, 595 Smith Street, Farmingdale, NY NY 11735, USA eSAS Ghasarossian, 8 rue de Dublin, 67300 Schiltigheim, France fritz@lam.jussieu. fr ISMA 2014, Le Mans, France
Machine Learning-Based Models for Early Stage Detection of Autism Spectrum Disorders

Autism Spectrum Disorder (ASD) is a group of neurodevelopmental disabilities that are not curable but may be ameliorated by early interventions. We gathered early-detected ASD datasets relating to toddlers, children, adolescents and adults, and applied several feature transformation methods, including log, Z-score and sine functions to these datasets. Various classification techniques were then implemented with these transformed ASD datasets and assessed for their performance. We found SVM showed the best performance for the toddler dataset, while Adaboost gave the best results for the children dataset, Glmboost for the adolescent and Adaboost for the adult datasets. The feature transformations resulting in the best classifications was sine function for toddler and Z-score for children and adolescent datasets. After these analyses, several feature selection techniques were used with these Z-score-transformed datasets to identify the significant ASD risk factors for the toddler, child, adolescent and adult subjects. The results of these analytical approaches indicate that, when appropriately optimised, machine learning methods can provide good predictions of ASD status. This suggests that it may possible to apply these models for the detection of ASD in its early stages.
EEG-Based Emotion Recognition with Deep Convolution Neural Network

Emotions are closely related to people's work and life. Emotional analysis and recognition is not only an urgent need to solve certain mental illnesses, but also has broad application prospects in the fields of human-computer interaction, entertainment and medical care. Therefore, it is of great value to classify emotional EEG signals. This paper introduces CNN(Convolutional Neural Networks)into the process of emotional EEG recognition. The innovation of this method is to adjustthe convolution kernel of the CNN to adapt to the input of EEG signals. The classification accuracy of 0.8579 is achieved in the three-classification emotional EEG signal.
MEED: An Unsupervised Multi-Environment Event Detector for Non-Intrusive Load Monitoring

The accurate detection of transitions between appliance states in electrical signals is the fundamental step that numerous energy conserving applications, such as Non-Intrusive Load Monitoring, rely on. So far, domain experts define rules and patterns to detect changes of appliance states and to extract detailed consumption information of individual appliances subsequently. Such event detectors are specifically designed for certain environments and need to be tediously adapted for new ones, as they require in-depth expert knowledge of the environment. To overcome this limitation, we propose a new unsupervised, multi-environment event detector, called MEED, that is based on a bidirectional recurrent denoising autoencoder. The performance of MEED is evaluated by comparing it to two state-of-the-art algorithms on two publicly available datasets from different environments. The results show that MEED improves the current state of the art and outperforms the reference algorithms on a residential (BLUED) and an office environment (BLOND) dataset while being trained and used fully unsupervised in the heterogeneous environments.
ARU-Net: Research and Application for Wrist Reference Bone Segmentation

Due to the influence of the irregular shapes and the adjacent positions of the wrist reference bones, it is difficult for the expert to accurately estimate the mature indication of the wrist reference bones of the minor. How to precisely segment the reference bones of the minor is a challenge. For this problem, the ARU-Net for wrist reference bone segmentation is proposed. First, we extract the reference bone ROI by Faster R-CNN. Then, the pre-processed ROI is fed into ARU-Net for segmentation. On the basis of traditional U-Net, ARU-Net adds residual mapping and attention mechanism, which improves the utilization rate of features and the accuracy of reference bone segmentation. Finally, a post-processing method including the flood fill algorithm and the morphological operation is used to eliminate jagged edges and holes in the segmented result. The hamate is one of the most difficult reference bones to segment in the wrist. This paper takes it as an example to assess the performance of ARU-Net. Experiments show that compared with FCN, U-Net and ResUnet, the accuracy and F1 scores of ARU-Net are higher. The accuracy rate is 96.4%, and the F1 score is 0.953. The post-processing method can further improve the result. Finally, the accuracy rate reaches 96.5%, and the F1 score reaches 0.954. In order to verify the segmentation stability of ARU-Net, it is also applied to the segmentation of the radius and the capitate. RU-Net can precisely segment the reference bone, which facilitates the expert to assess its mature indication, so as to accurately evaluate the bone age.
Stable Single-Longitudinal-Mode Fiber Laser With Ultra-Narrow Linewidth Based on Convex-Shaped Fiber Ring and Sagnac Loop

In this investigation, a convex-shaped fiber ring (CSFR)-based single-longitudinal-mode (SLM) erbium-doped fiber laser (EDFL) is proposed and experimentally verified. The CSFR, composed of only two optical couplers and forming a double-ring architecture, is employed to serve as a high quality mode filter to eliminate the dense longitudinal modes of the EDFL. The unpumped erbium-doped fiber in the Sagnac loop is utilized to produce the ultra-narrowband auto-tracking filter effect as a saturable absorber to guarantee the SLM out. The experimental results show that the linewidth of the proposed EDFL is 840 Hz with a high optical signal to noise ratio of 63 dB. In addition, the stability of the EDFL with the maximum output power fluctuation of 0.01 dB and the maximum central wavelength variation of 0.01 nm, in both the short-term and the long-term observations. Hence, we obtained a SLM EDFL with extremely high stability, ultra-narrow linewidth and high optical signal to noise ratio (OSNR).
Learning-Based Time Delay Attack Characterization for Cyber-Physical Systems

The cyber-physical systems (CPSes) rely on computing and control techniques to achieve system safety and reliability. However, recent attacks show that these techniques are vulnerable once the cyber-attackers have bypassed air gaps. The attacks may cause service disruptions or even physical damages. This paper designs the built-in attack characterization scheme for one general type of cyber-attacks in CPS, which we call time delay attack, that delays the transmission of the system control commands. We use the recurrent neural networks in deep learning to estimate the delay values from the input trace. Specifically, to deal with the long time-sequence data, we design the deep learning model using stacked bidirectional long short-term memory (LSTM) units. The proposed approach is tested by using the data generated from a power plant control system. The results show that the LSTM-based deep learning approach can work well based on data traces from three sensor measurements, i.e., temperature, pressure, and power generation, in the power plant control system. Moreover, we show that the proposed approach outperforms the base approach based on k-nearest neighbors.