Miracle-dz/UniFAR-FTUs huggingface dataset:
Morphological and molecular characterization of ( ) associated with Post-Flowering Stalk Rot (PFSR) of Maize in Karnataka Fusarium verticillioides F. moniliforme

Maize ( L.) is the major staple cereal crop in the world and is the third-largest grown cereal crop in India. Field surveys conducted from 2013-15 recorded stalk rot incidence ranged from 18-45%in 10 major maize growing districts of Karnataka state. The typical symptoms were observed after flowering season and it starts with the drying of the lower leaves and premature wilting, lower internodes turned into grey-green color and stalks are hollow and weak leading to the lodging of the plant. In our previous studies, we identified several pathogens associated with stalk rot disease incidence and turned out to be major threat in all the study regions. However, identity of the isolate was not confirmed. Therefore, in the present study, identification was confirmed as by morphological, cultural and molecular sequence analysis was done. species associated with stalk rot disease were isolated on the PDA medium. A total of 219 fungal isolates were obtained from Post Flowering Stalk Rot samples and 132 were identified as which represented more than 68% of total isolations. Isolates were identified based on cultural and morphological characteristics. However, identity of 12 selected isolates was confirmed by molecular identification. The ITS-rDNA and TEF gene were amplified and sequenced using ITS1/ITS4, TEF1/TEF2 primer pairs, respectively. The BLASTn search and phylogenetic analysis confirmed their identity as . Pathogenicity tests conducted on 60-day-old maize plants by injecting conidial suspension produced typical stalk rot symptoms after 30 days of post-inoculation and the pathogen's identity was re-confirmed by cultural and morphological features. Occurrence of with ear rot, seed borne incidence and root rot of maize was reported from different regions. However, association of and its molecular characterization by ITSrDNA and TEF from Karnataka were limited and the present study provided evidence for the occurrence of as a major disease causing pathogen of stalk rot.
The study of computational fuzzy extractor for providing security to IOT nodes

: With the increase in the usage over the internet over the globe ,the security regarding the confidentiality of the data that is being transferred and received while Internet is being used is the area of major concern. In the recent years, the increase in the use and development in IoT has made it necessary to facilitate keys storage that is cost efficient. The traditional methods that were used before ,proved to be expensive for key storage. An alternative solution for cost minimization, keys were being generated using the noisy entropy. The keys could be secured and made cost efficient when bounded with fuzzy extractor which also made the keys strong cryptographically. To balance the entropy loss at the time of key extraction process, the theoretical fuzzy extractors needed a enormous range of input entropy. According to the study proposed in Fuller et al the entropy loss can be minimized with the study of error problems.The server device authentication could be made more scalable and robust by using the Computational Fuzzy Extractor.The paper focuses on storage,efficiency and entropy loss and we are using lossless Computational Fuzzy Extractor,where the entropy key and the source entropy are equal to one another other.The proposed system shows how device server authentication can be provided, also we compare our work with already in use system in used on the basis of security where we are exacting to achieve zero entropy loss.
FIBER MODEL FOR REINFORCED CONCRETE WALLS

Due to the taller reinforced concrete (RC) buildings that have been constructed in recent years, shear walls at lower levels are subjected to higher axial loads and bending moments. Although complex finite element inelastic models for shear walls can effectively couple several effects at the stress-strain level, they are computationally demanding, and hence robust and computationally efficient models are necessary to quickly assess the earthquake performance of these buildings. Herein, a pure two-node fiber element model that takes into account axial and bending components only, was modified to produce objective results under common loading conditions of the walls identified in Chilean buildings, i.e., high axial loads with linear bending moment variation between floors. A regularization is required to predict results independent of the element size and a shear model based on the modified compression field theory was added into this element to simulate the behavior of shear walls adequately. This investigation focuses in the formulation of the proposed model, its validation with experimental tests reported in the literature, and its application to actual RC walls of buildings. It was found that the steel stress-strain constitutive behavior, the inclusion of shear deformation, and the strain penetration effects played an important role in reproducing the experimental behavior of walls. Additionally, the proposed model is able to predict the observed collapse mechanisms of walls in buildings damaged during the 2010 earthquake. Since the element is capable of reproducing experimental tests and earthquake response, and since it is numerically more efficient than other approaches, its use for complete 3D inelastic dynamic analysis of buildings is promising.
Understanding the Diversity and Community Structure of Bdelloid Rotifer Soil Communities.

Rotifers, though very important to the microbial food-web and energy flux of a system, are still poorly understood in terms of their taxonomy and geographical distributions. Rotifers pose problems for taxonomists and evolutionary biologists due to the difficulties associated with isolation, identification and enumeration of organisms that have few distinguishing morphological characters. This lack of morphological information and their extreme abundance of micro-invertebrates make identification of rare, or even common cryptic, taxa a large and unwieldy task as only painstaking microscopy can be used to identify synapomorphies. To overcome these problems I made use of environmental DNA sequencing to perform largescale surveys of bdelloid rotifer communities. The goal of this study was to describe the diversity and distribution of bdelloid rotifer communities as they relate to space, soil environment, and co-occurring bacterial communities. I found that bdelloid rotifers are significantly limited in their dispersal capabilities on the order of 100 meters, despite historically being viewed as having easy-to-disperse propagules. Although dispersal limitation is a significant contributor to the diversity structure of bdelloid communities, its influence constitutes much less of an effect than local biogeochemistry. In contrast, co-occurring bacterial communities are less affected by both
Interpreting strokes on paper with a mobile assistant

Digital pen technology has allowed for the easy transfer of pen data from paper to the computer. However, linking handwritten content with the digital world remains a hard problem as it requires the translation of unstructured and highly personal vocabularies into structured ones that computers can easily understand and process. Automatic recognition can help to this direction, but as it is not always reliable, solutions require the active cooperation between users and recognition algorithms. This work examines the use of portable touch-screen devices in connection with pen and paper to help users direct and refine the interpretation of their strokes on paper. We explore four techniques of bi-manual interaction that combine touch and pen-writing, where user attention is divided between the original strokes on paper and their interpretation by the electronic device. We demonstrate the techniques through a mobile interface for writing music that complements the automatic recognition with interactive user-driven interpretation. An experiment evaluates the four techniques and provides insights about their strengths and limitations.
“Association of Prolonged QTc dispersion with left ventricular Diastolic Dysfunction in patients with acute anterior myocardial infarction: A study in National Institute of Cardiovascular Diseases and Hospital, Dhaka, Bangladesh”

Aims: Diastolic operate typically declines before heartbeat operate, and this precedes clinical signs in patients with acute coronary syndrome. Therefore, diagnosing of beat disfunction is extremely necessary for early diagnosing, follow-up, treatment, and prognostic analysis in cardiopathy with preserved ejection fraction (HFpEF) patients. The most objective of the study was to seek out out association between prolonged QTc dispersion and left chamber beat disfunction in Non ST phase Elevation MI (NSTEMI) patients in HFpEF. Objects: This cross sectional analytical study was conducted within the Department of medicine and sixty patients were enclosed as study population from August 2016 to February 2017. Then the study population was divided into 2 teams, every cluster consisted of thirty patients. Methods and Materials: Fifty-nine consecutive patients with acute myocardial infarct were randomised to receive seventy mmol of metal (n=31) infused over twenty four h or pla-cebo (n=26) incidence of bodily cavity arrhythmias and vital sign variability (SD of 5-min mean sinus beat intervals over a twenty four h amount, SDANN; low frequency/high frequency amplitude quantitative relation, LF/HF ratio), and also the variety of ischae-mic episodes on vectorcardiography were measured from the ®rst day of treatment. QTc dispersion corrected for vital sign was measured from the 12lead graph. {magnesium|Mg|atomic variety 12|metallic element|metal} attenuated the quantity of hourly bodily cavity premature beats (P<0 ́05). QTc dispersion corrected for heart rate was decreased in both measurements at 24 h and 1 week (P<0 ́001). SDANN and LF/HF ratio were unchanged. The number of ischaemic episodes on vectorcardiography were equal, and peak creatine kinase MB release did not diVer between the groups. In testing the pathophysiological mechanisms, serum magnesium levels after infusion corre-lated with hourly ventricular premature beats (rs = "0 ́47; P<0 ́01), ventricular tachycardias (rs="0 ́26; P<0 ́05), and QTc dispersion corrected for heart rate (rs = "0 ́75; P<0 ́001), but not with SDANN, LF/HF ratio or peakcreatine kinase MB. QTc dispersion corrected for heart rate correlated with hourly ventricular premature beats (rs =0 ́48; P<0 ́001) and ventricular tachycardias (rs =0 ́27; P<0 ́05. Conclusions Magnesium suppresses early ventriculararrhythmias in acute myocardial infarction. The decreased arrhythmicity is related to enhancement of homogeneity in repolarization, but not to attenuation of prevailing is-chaemia, improvement of autonomic nervous derangements or myocardial salvage.
Association of Inspiratory Muscle Strength with Sports Performance in Football Players

There is large population interested in football and have been playing since years. This is a multifaceted sport that not only requires strength and endurance but also requires well developed physic that are important co-determinants of football performance. Football (soccer) player have to maintain a high level of intensity throughout the whole game and so the need to evaluate the association of inspiratory muscle strength with the sports performance in football players by using Inspiratory muscle training device and YOYO Intermittent Recovery Test Level 1.Analytical study was carried on total of 126 football players with mean age 20.58±2.57 and year of experience with mean 6.19±3.34 were evaluated by using Inspiratory muscle training device and inspiratory muscle strength was checked and then the players completed YO-YO intermittent recovery test level 1 and VO2max was calculated. Spearman coefficient correlation(r) was used to assess the correlation and on statistical analysis spearman coefficient between inspiratory muscle strength and predicted VO2max is 0.24. VO2 max peak was more sensitive to decreasing orifice diameter in trained endurance football players than untrained individual. There is increase in circulatory capacity with endurance training which is increased by IMT, whole body aerobic capacity increases thus increase in VO2 max. This study concluded that there is weak correlation between inspiratory muscle strength and VO2max.
Alpha Smooth Muscle Actin (αSMA) Immunohistochemistry Use in the Differentiation of Pancreatic Cancer from Chronic Pancreatitis

Aim: Fibrosis is observed both in pancreatic cancer (PDAC) and chronic pancreatitis (CP). The main cells involved in fibrosis are pancreatic stellate cells (PSCs), which activate alpha smooth muscle actin (αSMA), which is considered to be the best-known fibrosis marker. The aim of the study was to evaluate the expression of the αSMA in patients with PDAC and CP as the possible differentiation marker. Methods: We enrolled 114 patients undergoing pancreatic resection: 83 with PDAC and 31 with CP. Normal fragments of resected specimen from 21 patients represented the control tissue. The immunoexpressions of αSMA were detected in tissue specimens with immunohistochemistry (Abcam antibodies, GB). Results: Mean cytoplasmatic expression of αSMA protein in PDAC stromal cells was significantly higher compared to CP: 2.42 ± 0.37 vs 1.95 ± 0.45 (p < 0.01) and control group 0.61 ± 0.45 (p < 0.01). Strong immunoexpression of the αSMA protein was found in the vast majority (80.7%) of patients with PDAC, in about half (58%) of patients with CP, and not at all in healthy tissue. The expression of αSMA of different intensity was found in all patients with PDAC and CP, while in healthy tissue was minimal or absent. In PDAC patients, αSMA expression was significantly higher in tumors of diameter higher than 3 cm compared to smaller ones (p = 0.017). Conclusions: Presented findings confirm the significant role of fibrosis in both PDAC and CP; however, they do not confirm the role of αSMA as a marker of differentiation.
Individual and societal risk metrics as parts of a risk governance framework for induced seismicity

This study serves three purposes: (i) to review appropriate metrics for life safety (ii), to provide the outline of a robust mathematical framework quantifying and comparing risk safety metrics, and (iii) to provide upper bounds for aggregate measures of risk. In recent decades, the significant increase in seismicity caused by anthropogenic activities such as hydraulic fracturing, fluid injections, and mining, has posed the challenge of establishing a framework governing the risks. Risk metrics, which provide the baseline for risk management and decisionmaking, are a pivotal component of the risk governance framework. There is a broad spectrum of metrics that can be implemented; however, all were developed for rather different disciplines. In this study, we select the ones that are more closely related to the context of induced seismicity. Next, we introduce a general mathematical framework to define generalized risk metrics, which is based on the concept of norms in functional analysis. This framework allows a uniform comparison of different individual and societal risk metrics, and comparison across different activities and technologies. The second part of the paper focuses on the computational framework for the calculation of individual and societal risk. The framework is based on the wellestablished PSHA analysis and the PEER probabilistic performance-based seismic evaluation framework. The final part of the paper deals with the challenge of aggregate risk measures by providing upper bounds that can be compared with prescribed life safety criteria.
Semantic Method of Recovering of Medical Articles

The amount of information produced today reached an unprecedented contingent in history, which makes it difficult to locate relevant documents in a search. This situation leads to the need to use tools that facilitate the search, making information retrieval area increasingly important in the development of this context. Based on the organization of documents and ontology, information retrieval area has the challenge of getting smarter ways to recovery than just select syntactically documents, exploiting to the full the semantic context when selecting the information. This paper presents a semantic enrichment method that seeks to improve the quality of results when querying a database of medical articles. The proposed method performs a search on the repository of articles that is submitted to latent semantic analysis together with the National Cancer Institute (NCI) ontology and the lexical WordNet ontology database. After this joint treatment, the semantic relationship of those new terms to the survey conducted in the context is performed in order to improve the accuracy of recovery and enabling the retrieve of more relevant articles regarding the search.
Deriving Customer Loyalty and Its Determinants from Online Reviews using Support Vector Machine

With the advent of the Social Web, a great number of end users constantly generate content through various social media including online reviews. It is known that customer opinions and loyalty play important roles in facilitating retailing. In this paper, we aim to address two key research problems, (1) can we accurately predict customer loyalty from online reviews, and (2) can we identify the main driving factors behind customer loyalty from online reviews?
The conservation ecology of the European nightjar (Caprimulgus europaeus) in a complex heathland-plantation landscape

Abstract The conservation ecology of the European nightjar (Caprimulgus europaeus) was investigated in a complex heathland-plantation landscape in eastern England. Using radio telemetry, 31 nightjars were tracked in 2009 and 2010. The breeding behaviour of males was found to have potential implications for nightjar survey methods. While surveyors should be aware of the possibility of song territory overlap between male birds, results suggest that the occurrence of roaming unpaired males would not lead to an over-estimate in population. Home range 95% kernels for females, paired and unpaired males were an order of magnitude larger than song territories, highlighting the importance of foraging habitat in the broader landscape. Compositional analysis showed that foraging nightjars selected open canopy plantation forest (aged 5-10 years) and newly planted coupes (aged 0-4 years), with grazed grass-heath also used when available within 2km of the territory centre. Open ungrazed and un-planted habitat within the forest was avoided by birds, relative to availability. Moth trapping indicated that birds may choose foraging habitat based on the ease of prey capture rather than prey abundance. Dissection of nightjar faecal pellets demonstrated that moths and beetles were key components of the diet and that variation in diet during the breeding season may be due to changes in resource abundance and varying chick requirements. Comparison of the diets of birds nesting or roosting at varying distances from grass-heath suggested that forest and heathland habitats provide similar foraging resources for the nightjar. Behavioural data from nest camera footage indicated that the factors influencing the duration and frequency of adult non-attendance at the nest were incubation stage, temperature and chick requirements. This thesis combines investigations of nightjar breeding behaviour, home range, habitat use and diet, which can be used as an evidence base to inform conservation management.
Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation

Interactive recommendation aims to learn from dynamic interactions between items and users to achieve responsiveness and accuracy. Reinforcement learning is inherently advantageous for coping with dynamic environments and thus has attracted increasing attention in interactive recommendation research. Inspired by knowledge-aware recommendation, we proposed Knowledge-Guided deep Reinforcement learning (KGRL) to harness the advantages of both reinforcement learning and knowledge graphs for interactive recommendation. This model is implemented upon the actor-critic network framework. It maintains a local knowledge network to guide decision-making and employs the attention mechanism to capture long-term semantics between items. We have conducted comprehensive experiments in a simulated online environment with six public real-world datasets and demonstrated the superiority of our model over several state-of-the-art methods.
Designing a NISQ reservoir with maximal memory capacity for volatility forecasting

Quantitative risk management, particularly volatility forecasting, is critically important to traders, portfolio managers as well as policy makers. In this paper, we applied quantum reservoir computing for forecasting VIX (the CBOE volatility index), a highly non-linear and memory intensive `real-life' signal that is driven by market dynamics and trader psychology and cannot be expressed by a deterministic equation. As a first step, we lay out the systematic design considerations for using a NISQ reservoir as a computing engine (which should be useful for practitioners). We then show how to experimentally evaluate the memory capacity of various reservoir topologies (using IBM-Q's Rochester device) to identify the configuration with maximum memory capacity. Once the optimal design is selected, the forecast is produced by a linear combination of the average spin of a 6-qubit quantum register trained using VIX and SPX data from year 1990 onwards. We test the forecast performance over the sub-prime mortgage crisis period (Dec 2007 - Jun 2009). Our results show a remarkable ability to predict the volatility during the Great Recession using today's NISQs.
What do you mean?: the role of the mean function in bayesian optimisation

Bayesian optimisation is a popular approach for optimising expensive black-box functions. The next location to be evaluated is selected via maximising an acquisition function that balances exploitation and exploration. Gaussian processes, the surrogate models of choice in Bayesian optimisation, are often used with a constant prior mean function equal to the arithmetic mean of the observed function values. We show that the rate of convergence can depend sensitively on the choice of mean function. We empirically investigate 8 mean functions (constant functions equal to the arithmetic mean, minimum, median and maximum of the observed function evaluations, linear, quadratic polynomials, random forests and RBF networks), using 10 synthetic test problems and two real-world problems, and using the Expected Improvement and Upper Confidence Bound acquisition functions. We find that for design dimensions ≥ 5 using a constant mean function equal to the worst observed quality value is consistently the best choice on the synthetic problems considered. We argue that this worst-observed-quality function promotes exploitation leading to more rapid convergence. However, for the real-world tasks the more complex mean functions capable of modelling the fitness landscape may be effective, although there is no clearly optimum choice.
Learning-Based Joint User-AP Association and Resource Allocation in Ultra Dense Network

With the advantages of Millimeter wave in wireless communication network, the coverage radius and inter-site distance can be further reduced, the ultra dense network (UDN) becomes the mainstream of future networks. The main challenge faced by UDN is the serious inter-site interference, which needs to be carefully addressed by joint user association and resource allocation methods. In this paper, we propose a multi-agent Q-learning based method to jointly optimize the user association and resource allocation in UDN. The deep Q-network is applied to guarantee the convergence of the proposed method. Simulation results reveal the effectiveness of the proposed method and different performances under different simulation parameters are evaluated.