Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of the nitrogen content in poultry manure

Proper estimation of the nitrogen (N) content of poultry manure and proper manure handling are necessary to ensure that application rates minimize emissions from the manure and nitrate leaching into the cropland. Uric acid and undigested proteins are the two main N components in poultry manure that cause ammonia emissions and nitrate leaching in the ground water. The ammonia that is applied to cropland may be 50 to 90% of total N, depending upon the way the manure has been stored or treated. Ammonia and hydrogen sulphide contents have been proven to be useful alternative measures of odour reduction. The order of importance in influencing ammonia formation is : litter pH > temperature > moisture content. Total fixation of ammonia was achieved below pH 4 and temperatures down 10°C are necessary to have a negative effect on degradation and volatilisation. Adsorbants such as sawdust and straw enable the capture some of the readily available N and enable the microbial population to start immobilizing N. The organic fraction of poultry manure had a C/N ratio that varied from 1 to 27:1. Most of the N (approximately 60 – 70%) excreted in poultry manure is in the form of uric acid and urea. Total N, total Kjeldahl N (TKN), organic N, ammonium, nitrate and nitrite are significantly correlated with the amount of N mineralised as well as the fraction of organic N mineralised during incubation. Some useful equations are: Inorganic N (IN) = ammonium + nitrate + nitrite; Total N (TN) = TKN + nitrate + nitrite; Organic N = TKN – ammonium or TN – (ammonium + uric acid) or TN – IN; Available N (AN) = Inorganic N + 0.4 x organic N; Predicted available N (PAN) = 80% Inorganic N + 60% Organic N.
A Conceptual Framework to Predict Academic Performance of Students using Classification Algorithm

- Technological advancements have improved customer service and enhanced customer satisfaction to a large extent in the Industry and Service sectors. Education institutions across the globe are leveraging on the technology to produce high-quality graduates and improve the customer satisfaction level. Several researchers are striving hard to improvise the system through their innovative solutions. Education Data Mining (EDM) is an evolving field in Data mining due to its increasing demand in the higher education sector. Analyzing the students learning behavior and predicting their progression at the early stage will help the higher education institutions to produce quality graduates and to curb the student attrition. In this paper, we propose a conceptual framework that can act as a guide to develop a recommender system to predict the academic performance of students at the early stage by using classification algorithms. Various
Application of the theory of probability and dynamic systems in the evaluation of cardiac dynamics: Study of diagnostic agreement

In the present work a new methodology was applied, to evaluate the cardiac status in 16 hours, in continuous electrocardiographic records and Holter, this by means of the theory of probability, and the occupation of spaces from attractors in the general space of Box-Counting, of recent development. 20 normal and 80 pathological records were analyzed, according to the conventional diagnosis. A simulation of the totality of the dynamics was made, taking into account the number of beats, and their maximum and minimum frequencies per hour, during 21 hours. With these data the attractors were built in a phase space. The calculation of the fractal dimension of the attractors was made from the evaluation of the spatial occupation of each one of them and through probability of occupation in the Box-Counting space, establishing its diagnosis with previous mathematical results. Finally, taking the conventional diagnosis as Goldstandard, this and the mathematical result were compared. In the states of normality, the probability of occupation values of the attractors was between 0.157 and 0.286, while for the pathological states, it was 0.019 and 0.141, thereby differentiating the normal and pathological states.
DE OBSTRUÇÃO INTESTINAL EFFECT OF METRONIDAZOL ON THE INTESTINAL MORPHOLOGY AND OCCURRENCE OF BACTERIAL TRANSLOCATION IN RA TS IN THE PRESENCE OR ABSENCE OF INTESTINAL OBSTRUCTION

Background: Aerobic flora is associated with bacterial translocation (BT). The anaer obic flora may have a r ole in the occurence of this phenomenon. The aim of this study was to investigate the effect of metr onidazol on the intestinal morphology and on the occur rence of BT in the presence or absence of intestinal obstr uc ion. Methods: Eighty wistar rats wer e randomized to two gr oups: group metr onidazol (n=40) and gr oup saline (n=40). They received the cor respondent medication for 72h. Each gr oup comprised four subgr oups of 10 rats each as follows: without laparotomy, with laparotomy, ileal and sigmoideal obstr uction. After receiving the solutions for 48h, the rats under went different operative pr ocedures depending on the subgr oups except for subgr oup without lapar otomy in which no operation was car ried out. They wer killed after 72h and the mesenteric lymph nodes, liver spleen and blood samples wer e cultured. Biopsies of the jejunum, ileum and sigmoide wer e collected and sent to histomorphometric measur ements. Results: A significantly higher mor tality was obser ved in group saline in obstructed animals. Ther was no impor tant morphological differ ences between the gr oups. In metr onidazol group the incidence of BT (8/40; 20%) was significantly higher (p =0.028) than in contr ls (1/40; 2,5%) in nonoperated animals. In the subset of ileum obstr uction, the rate of BT was higher in contr ols than in metr onidazol group (27/40; 67,5% vs. 7/40; 17,5%; p<0.001). However , there was gr eater translocation to lymph nodes in metronidazol gr oup compar ed with contr ols in sigmoid obstr ucted rats. Conclusions: Metronidazol favors the occurrence of BT under basal conditions. Ther e is no impor tant morphological alteration induced by metr onidazol. Intestinal obstruction enhances BT and the site of obstruction influences the rate of translocation associated with metronidazol. However , mortality is lower when metr onidazol is used in obstr ucted animals.
Sparse Dissimilarity-constrained Coding for Glaucoma Screening

-Eye disease which harms the optic nerve that conveys data from the eye to the brain is Glaucoma. It is the second reason of visual deficiency, recognizing the disease in time is critical on the grounds that it can't be restored. Numerous methodologies are done in distinguishing Glaucoma; those are insufficient for population based glaucoma screening. Optic nerve head evaluation in retinal fundus image is both all the more encouraging and unrivaled. This paper proposes image preparing strategy for the early identification of glaucoma. Glaucoma is one of the significant causes which cause blindness yet it was difficult to analyze it in beginning times.
The potential of an all‐grass diet for the late‐winter calving dairy cow

In a trial lasting 3 years at Jealott's Hill and 2 years at Ravenscroft Hall, a total of 73 January- March-calving Friesian x Holstein cows and heifers were given a diet consisting almost exclusively of ensiled and grazed ryegrass for an entire lactation to establish the level of milk production which grass alone can support. From calving to turnout in April/May (average duration 87 days), the animals were given high digestibility (DOMD = 678g kg-1 DM) ryegrass silage ad libitum. After turnout, they grazed ryegrass swards until housing in autumn. Thereafter, the cattle were given ryegrass silage of lower digestibility (DOMD = 600–620 g kg-1 DM) for the remainder of the lactation and during the dry period. Mean silage dry matter intakes from calving to turnout were 13–2 kg day-1 at Jealott's Hill and 11·5 kg day-1 at Ravenscroft Hall. Average milk yields were 21-1 kg day-1 for cows and 16-1 kg day-1 for heifers with 39-2g fat kg-1, 29-6g protein kg-1 and 47–9 g lactose kg-1. Over the complete lactation, average milk yields were 4680 kg and 4006 kg for the cows and heifers, respectively, with 39-4 g fat kg-1, 31·4 g protein kg-1 and 46·9 g lactose kg-1. Animal health and fertility were satisfactory. The results demonstrated the value of high quality grazed and ensiled ryegrass and provided a measure of milk production from grass only.
Embedding Humanizing Cultures in Organizations through Moral Identity and Institutional Leadership: The ‘Strategic’ Role of HRM

Building on dissatisfaction with increasing emphasis on concepts and solutions such as ‘sustainability proceduralization’ or ‘creating shared value’, which entail a superficial conception of the firm’s moral agency and role in society, this paper is based on three general premises. First, it adheres to the Catholic Social Thought’s view of the enterprise as a community of work. Second, it draws some insights from recent developments in virtue ethics elaborating on MacIntyre’s work, with special regard to how firms can be able to develop an organizational moral character and promote virtue among their members. The third premise is about organizational culture, considered as shared values and commitments that have an influence on relationships and actions of persons in organizations; in particular, it is suggested that the moral character of the enterprise reflects the development of an organizational ‘humanizing culture’ revolving around the principles of human dignity and the common good. The paper claims that Human Resource Management (HRM) can provide essential support for the development of organizational humanizing cultures. Whereas this is partly in line with the emerging call for scholars and practitioners to treat HRM and the sphere of socially responsible behavior of enterprises as interdependent domains, the paper intends to delve deeper into the recurrent but often only sketchily explored conception that HR professionals have a key role to play in integrating ethically-driven principles within organizational cultures. It tries to do so by dwelling on and connecting two basic points. First, it elaborates on the idea that embedding ethical organizational cultures primarily involves an influence on the development of employees’ moral identity, so as to build virtuous behavior repertories. In particular, it is highlighted how HR managers may significantly contribute to generating this organizational ‘formative context’, by means of their established functions (including recruitment and selection, training, performance assessment and rewarding) and, more profoundly, by creating experiential, relational and symbolic venues and opportunities that favor the nurturing of a collective moral identity oriented toward the common good of the firm and of society. Secondly, it is suggested that, operating in that way, the HR manager can participate directly in the crucial processes of ‘institutional leadership’; i.e., taking inspiration from the perspective of the sociologist Philip Selznick, the capacity to act as a co-producer of an organization’s moral character. The paper concludes by stressing that the proposed framework brings to light a deeper sense in which companies committed to creating humanizing cultures can conceive of – and unleash – the strategic potential of HRM.
A Machine Learning Approach for Contrast Enhancement of Medical Images

— Image contrast enhancement is important in medical applications. This is due to the fact that visual examination of medical images is essential in the diagnosis of many diseases. The proposed method use the applicability of the Neural Network approach for image quality classification using a set of key features extracted from the images. If the image is of low quality then it is enhanced with appropriate contrast enhancement technique. The technique can prevent over-enhancement and enhance small details of an image. We focus on chest x-ray and mammogram images.
THE IMPORTANCE OF PATIENTS’ USER EXPERIENCE WITH A TELEHEALTH SYSTEM FOR THEIR EVALUATION OF ITS PSYCHOSOCIAL IMPACTS

Drawing on the unified theory of acceptance and use of technology model, this study presents the preliminary results of an intervention study that explored how the usability and acceptability – two dimensions of user experience (UX) – of a home telehealth system (THS) shape patients’ perceptions of its impact on their functional independence, well-being and quality of life. Fifty-five patients with at least one chronic condition (diabetes and/or hypertension) tested a home THS for five months. All participants in the non-probability sample were recruited in a Community Health Centre in Slovenia. A multilinear regression model was run on the data collected after a three-month trial period. The positive psychosocial impacts of the home THS (PIADS-10) utilisation were higher among the patients who reported higher values of usability (TUQ), regardless of their self-assessed physical and mental health (SF-12), control over health (PAM) and frequency of THS use. The results also showed no association between psychosocial impacts and technology acceptance (SUTAQ). Overall, the findings suggest that UX is an important aspect of patients’ engagement with a home THS.
Stochastic Multi-Player Bandit Learning from Player-Dependent Feedback

We investigate robust data aggregation in a multi-agent online learning setting. In reality, multiple online learning agents are often deployed to perform similar tasks and receive similar feedback. We study how agents can improve their collective performance by sharing information among each other. In this paper, we formulate the -multi-player multi-armed bandit problem, in which a set of M players that have similar reward distributions for each arm play concurrently. We develop an upper confidence bound-based algorithm that adaptively aggregates rewards collected by different players. To our best knowledge, we are the first to develop such a scheme in a multi-player bandit learning setting. We show that under the assumption that pairwise distances between the means of the player-dependent distributions for each arm are small, we improve the (collective) regret bound by nearly a factor of M , in comparison with a baseline algorithm in which the players learn individually using the UCB-1 algorithm (Auer et al., 2002). Our algorithm also exhibits a fallback guarantee, namely, if our task similarity assumption fails to hold, our algorithm still has a performance guarantee that cannot be worse than the baseline by a constant factor. Empirically, we validate our algorithm on synthetic data.
LAND COVER CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK WITH REMOTE SENSING DATA AND DIGITAL SURFACE MODEL

Abstract. Land cover map is widely used in urban planning, environmental monitoring and monitoring of the changing world. This paper proposes a framework with convolutional neural network (CNN), object-based voting and conditional random field (CRF) for land cover classification. Both very-high-resolution (VHR) remote sensing images and digital surface model (DSM) are inputs of this CNN model. To solve the “salt and pepper” effect caused by pixel-based classification, an object-based voting classification is performed. And to capture accurate boundary of ground objects, a CRF optimization using spectral information, DSM and deep features extracted through CNN is applied. Area one of Vaihingen datasets is used for experiment. The experimental results show that method proposed in this paper achieve an overall accuracy of 95.57%, which demonstrate the effectiveness of proposed method.
Quality assurance of intensity modulated radiation therapy treatment planning using head and neck phantom

Abstract Purpose The purpose of this study is the verification of intensity modulated radiation therapy (IMRT) head neck treatment planning with one-dimensional and two-dimensional (2D) dosimeters using imaging and radiation oncology core (IROC) Houston head & neck (H&N) phantom. Method The image of the H&N phantom was obtained by computed tomography scan which was then transferred to Pinnacle@3 treatment planning system (TPS) for treatment planning. The contouring of the target volumes and critical organ were done manually and dose constraints were set for each organ according to IROC prescription. The plan was optimised by adoptive convolution algorithm to meet the IROC criteria and collapse cone convolution algorithm calculated the delivered doses for treatment. Varian Clinac 2110 was used to deliver the treatment plan to the phantom, the process of irradiation and measurement were repeated three times for reproducibility and reliability. The treatment plan was verified by measuring the doses from thermoluminescent dosimeters (TLDs) and GafChromic external beam therapy 2 films. The agreement between the planned and delivered doses were checked by calculating the percentage dose differences, analysing their isodose line profiles and 2D gamma maps. Results The average percent dose difference of 1·8% was obtained between computed doses by TPS and measured doses from TLDs, however these differences were found to be higher for organ at risk. The film dose profile was well in agreement with the planned dose distribution with distance to agreement of 1·5 mm. The gamma analysis of the computed and recorded doses passed the criteria of 3%/3 mm with passing percentages of >96%, which shows successful authentication of delivered doses for IMRT. Conclusion IMRT pre-treatment validation can be done with IROC anthropomorphic phantoms, which is essential for the delivery of modulated radiotherapies. It was concluded that films and TLDs can be used as quality assurance tools for IMRT.
Biological control of soilborne plant pathogens in the rhizosphere with bacteria

Biological control of soilborne pathogens by introduced microorganisms has been studied for over 65 years (9, 49), but during most of that time it has not been considered commercially feasible. Since about 1 965, however, interest and research in this area have increased steadily (9), as reflected by the number of books (10, 47,49, 152) and reviews about it (11,26,30, 106, 143, 153, 173, 174, 183) that have appeared . Concurrently, there has been a shift to the opinion that biological control can have an important role in agriculture in the future, and it is encouraging that several companies now have programs to develop biocontrol agents as commercial products. This renewed interest in biocontrol is in part a response to public concern about hazards associated with chemical pesticides. Microorganisms that can grow in the rhizosphere are ideal for use as biocontrol agents, since the rhizosphere provides the front-line defense for roots against attack by pathogens. Pathogens encounter antagonism from rhizosphere microorganisms before and during primary infection and also during secondary spread on the root. In some soils described as microbiologi­ cally suppressive to pathogens (172), microbial antagonism of the pathogen is especially great, leading to substantial disease control. Although pathogen­ suppressive soils are rare, those identified are excellent examples of the full potential of biological control of soilborne pathogens.
Neural Architecture Search for Time Series Classification

Neural architecture search (NAS) has achieved great success in different computer vision tasks such as object detection and image recognition. Moreover, deep learning models have millions or billions of parameters and applying NAS methods when considering a small amount of data is not trivial. Unlike computer vision tasks, labeling time series data for supervised learning is a laborious and expensive task that often requires expertise. Therefore, this paper proposes a simple-yet-effective fine-tuning method based on repeated k-fold cross-validation in order to train deep residual networks using only a small amount of time series data. The main idea is that each model fitted during cross-validation will transfer its weights to the subsequent folds over the rounds. We conducted extensive experiments on 85 instances from the UCR archive for Time Series Classification (TSC) to investigate the performance of the proposed approach. The experimental results reveal that our proposed model called NAS-T reaches new state-of-the-art TSC accuracy, by designing a single classifier that is able to beat HIVE-COTE: an ensemble of 37 individual classifiers.
The effect of high-frequency oscillatory ventilation or airway pressure release ventilation on children with acute respiratory distress syndrome as a rescue therapy

Background To investigate the effects of high-frequency oscillatory ventilation (HFOV) or airway pressure release ventilation (APRV) as a rescue therapy on children with moderate and severe acute respiratory distress syndrome (ARDS). Methods We retrospectively enrolled 47 children with ARDS who were transitioned from synchronized intermittent mandatory ventilation (SIMV) to either HFOV or APRV for 48 h or longer after failure of SIMV. The parameters of demographic data, arterial blood gases, ventilator settings, oxygenation index (OI), and PaO2/FiO2 (PF) ratio during the first 48 h of HFOV and APRV were recorded. Results There was no significant difference between the HFOV and APRV groups with survival rates of 60% and 72.7%, respectively. Compared to pre-transition, the mean airway pressures at 2 and 48 h after transition were higher in both groups (P<0.01), and the PF ratio at 2 and 48 h in both modes was significantly improved (P<0.001). PF ratio and PaCO2 have significant differences at 48 h between two groups. The OI at 2 h after transition had no improvement in either group and was substantially lower at 48 h relative to the pre-transition level (P<0.001) in both groups. At 48 h after the transition to both HFOV and APRV, the survivors had lower mean airway pressures, higher PF ratios, and a lower OIs than non-survivors (P<0.01). Conclusions There was no significant difference on the survival rates of HFOV and APRV application as a rescue therapy for ARDS, but improved oxygenation at 48 h reliably discriminated survivors from non-survivors in both groups.
THE SJTU SUBMISSION FOR DCASE2020 TASK 6: A CRNN-GRU BASED REINFORCEMENT LEARNING APPROACH TO AUDIOCAPTION Technical Report

This paper proposes the SJTU AudioCaption system for the DCASE2020 Task 6 challenge. Our system consists of a powerful CRNN encoder combined with a GRU decoder. In addition to standard cross-entropy Audiocaption, reinforcement learning is also investigated. Our approach signiﬁcantly improves against the challenge baseline model on all shown metrics achieving a relative improvement of at least 34%. Our best submission achieves a BLEU4 of 0.146, Rouge-L of 0.352, CIDEr of 0.280, METEOR of 0.149, and SPICE of 0.099 on Clotho evaluation set.
College Loans and Post-Schooling Skill Accumulation∗

We develop and estimate a model of post-schooling dynamic decisions on consumption, savings, labor supply, and costly human capital investment, where heterogenous individuals are faced with borrowing constraints and the burden of college loan repayment. Our estimates suggest that individuals with larger college loans are more likely have higher efficiency in producing human capital and lower disutility of work. Accounting for these unobservables, we find that individuals indeed under-invest in human capital due to the burden of repaying college loans early in their career. Relative to the 10-year standard repayment plan, “Pay As You Earn” plans would lead to welfare improvement for college loan borrowers by over $200 worth of annual consumption on average and a gain of over $1,600 in government budget per borrower.