Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of Physicochemical and Biological Properties of Household Food Waste Composting Using Actinomycetes Genus

Household food waste, farmyard cow dung waste, sawdust waste are used as substrate mixture and converted into organic composting using soil culture Actinomycetes genus. Isolation, characterization and biochemical test were carried to identify the Streptomyces spp. These species were subculture using nutrient media for inoculum dosage. An experimental setup was conducted using 1kg capacity of Poly Vinyl Chloride with six different ratio composting types induced and maintained in the thermophilic phase condition. This research sourced to characterization the physiochemical properties and microbial influence, after the composting. The composting types, C1 and C4 attain rich in Nitrogen while C2 and C5 attain rich in potassium and phosphorus. Carbon content rich in C3 and C6 composting. The concentration of the microbial population for each formulated composting was found and estimated. According to seedling and plant growth of Raphanus Sativus, C1 and C4 types composting attain mature growth within a short period and other composting maintain a slow growth rate. This beneficial research potential has made an interest or motivates to develop terrace farming, vertical farming and organic farming by individual household members.
Title : Over-Dispersed and Under-Dispersed Generalized Poisson Distribution

We propose an over-dispersed, and under-dispersed Generalized Poisson Distribution. This distribution will overcome some of the potential limitations suffered by the existing dominant distributions including lack of modeling over-dispersion, under-dispersion. This distribution is flexible and could be applied to a variety of problems from different disciplines like business, finance, medicine and reliability in engineering.
Mapping QTLs for Grain Yield in Maize: A Bayesian Approach Using Reversible Jump MCMC

Many traits that are important to agriculture, human health and evolutionary biology, in its majority, can be classified as quantitative, whose phenotypic expression presents continuous variation, attributed to the simultaneous segregation of many genes, in definite regions as QTLs (Quantitative Trait Loci). Efficient and robust identification and mapping onto genomic positions of those genes is a very important goal in quantitative genetics. Mapping QTL is identify its position in the genoma and to estimate its effect. The availability of genome-wide molecular markers provides the means for us to locate and map those QTL in a systematic way. Since the publication of Lander and Botstein (1989), that first proposed interval mapping method for a genome-wide scan of QTL, many statistical methods have been proposed and developed to map QTL , great part of them present Classical boarding. This work will present Bayesian approach. Adopting a Bayesian approach a model is fit to quantitative trait and molecular marker data, instead of fitting to locus. The output of a Bayesian analysis is a posterior distribution on the parameters, fully incorporating prior beliefs and parameter uncertainty. Reversible Jump MCMC (RJMCMC) is used in this work. Bayes Factor is used to estimate the number of QTL. Inference summaries for the locations of the QTL and their effects are derived from the corresponding marginal posterior densities. Through Bayesian interval, significant associations between markers and QTLs were obtained in four chromosomes and five QTLs has been mapped, which explained 11.23% of genetic variation. Most alleles that contributed positively in trait came from parental strain L0202D. The latter had the highest yield rate. We examine grain yield in maize to illustrate the applied method.
Dicranochaete – an enigmatic green alga with surprising adaptive capabilities

Abstract: Dicranochaete is a green coccoid alga with a spiny cap and a long branched seta that was described more than 100 yr ago from field samples. Although Dicranochaete has been repeatedly reported to be found on Sphagnum ‘leaves’ in peat bogs, our knowledge about its development is still fragmentary, and its phylogenetic affiliation remained controversial. To advance knowledge about this lesser-known alga, I used two Dicranochaete strains, grew them in a medium that mimics chemical properties of peat bogs and studied their life history and development. Furthermore, phylogenetic analyses using rDNA sequence comparisons were performed. My studies resulted in the following key observations: (1) Dicranochaete possesses a unique development that together with its characteristic morphology likely represents adaptations to the specific environment of peat bogs: the spiny cap, especially in early stages of algal development, presumably serves as a defence against predators. The seta provides an additional surface area to aid the uptake of nutrients, in particular phosphate. (2) Phylogenetic analyses identified Dicranochaete as a member of the Chaetopeltidales (Chlorophyceae). Moreover, a new species of Dicranochaete, Dicranochaete variabilis sp. nov., was erected, and the genus Dicranochaete Hieronymus (1890) was emended. In conclusion, this study provides new insights into the life history, developmental processes and phylogenetic position of Dicranochaete and provides insights into adaptation of epiphyllous microalgae to nutrient-poor environments (i.e. peat bogs).
Can the Bartlett test really detect the heterogeneity of variances ?

The hypotheses about the variances were established to propose a favorable situation (simulation on H0 is true, H0 : σ 2 1 = σ 2 2 = σ 2 3 = σ 2 4 = σ 2 and an unfavorable one (on H0 is false, H1 : 4σ 2 i , with i = 0, 1, 2 e 3) in order to assess the efficiency of Bartlett’s Test (1937). Six scenarios with seven response variables were generated without considering correlation between the variables: two under the hypothesis about the true averages, without differences as to the variance simulation (H0 − H0), and about heteroscedasticity (H0 − H1), two under H1 homoscedastic, with two standard deviations of difference between the averages, H1 : 2s−H0 and also with eight standard deviations of difference, H1 : 8s−H0, and two under H1 heterocedastic, H1 : 2s−H1 e H1 : 8s−H1, respectively. In order to evaluate the tests in each simulated response variable, the number of accepted null hypotheses was calculated. The results were presented as percentage (accepted number of H0/1000). Overall, Bartlett’s Test obtained high acceptance values of homoscedasticity, even for the simulated scenario under means difference of eight standard deviations (95.8%). This result suggests that in addition to Bartletts test other means of evaluating the variance homogeneity should also be used.
Freeform quad-based kirigami

Kirigami, the traditional Japanese art of paper cutting and folding generalizes origami and has initiated new research in material science as well as graphics. In this paper we use its capabilities to perform geometric modeling with corrugated surface representations possessing an isometric unfolding into a planar domain after appropriate cuts are made. We initialize our box-based kirigami structures from orthogonal networks of curves, compute a first approximation of their unfolding via mappings between meshes, and complete the process by global optimization. Besides the modeling capabilities we also study the interesting geometry of special kirigami structures from the theoretical side. This experimental paper strives to relate unfoldable checkerboard arrangements of boxes to principal meshes, to the transformation theory of discrete differential geometry, and to a version of the Gauss theorema egregium.
Reparametrizing the two-parameter Gnedin-Fisher partition model in a Bayesian perspective (Reparametrisation du modèle de partition Gnedin-Fisher à deux paramètres dans une perspective Bayésienne)

RÉSUMÉ) In modern Bayesian statistics exchangeable Gibbs partitions (Gnedin and Pitman, 2006), are playing a central role both in the construction and properties of almost surely discrete nonparametric priors and in the nonparametric treatment of species sampling problems. Here, by means of an alternative parametrization of the two-parameter Gnedin-Fisher species sampling model (Gnedin, 2010), a family of Gibbs partitions arising by mixtures of Fisher’s models, we obtain additional results for the partition model itself and a posterior analysis in a Bayesian nonparametric perspective. In particular, identifying the prior mixing distribution on the number of blocks with the shifted generalized Waring distribution (a family of probability laws arising by Beta mixtures of Negative Binomial distributions), we provide a direct construction of the exchangeable partition probability function, obtain the coniugate posterior mixing, and derive the structural distribution and its moments. On the Bayesian side, in the spirit of Lijoi et al. (2008), we provide distributional results for an additional sample, conditional on a basic observed sample, for quantities of statistical interest in species sampling problems. Int. Statistical Inst. : Proc. 58th World Statistical Congress, 2011, Dublin (Session CPS034) p.4683
Effects of refuge crops and sowing dates on the growth and yield performance of Bollgard II variety of cotton (Gossypium hirsutum L.) in Allahabad condition

Extensive use of pesticides and insecticides to control various kinds of pests and bollworms in Bt (Bacillus thuringiensis) cotton to create ecological imbalance in the environment. For managing that natural refuge crops helps to delay resistance in Bt cotton. An experiment was conducted to estimate the effect of refuge crop and sowing dates on growth and yield performance of Bollgard II variety of Bt cotton in two different Departmental Fields at Sam Higginbottom University of Agriculture, Technology and Sciences, Allahabad (Uttar Pradesh) during 2016. When alternatively sown 50% Bt with 50% refuge nBt cotton, significantly early planting cotton of first field gave more number of monopodial branches plant(2.55) sympodial branches plant (17.00) than the refuge nBt cotton whereas plant height (cm) was found maximum in 50% refuge nBt cotton (139.33) and yield attributes was found maximum in 50% Bt than 50% nBt. In case of planting date, early planting cotton during mid march gave significantly more plant height (130.44 cm), monopodial branches plant (2.50), sympodial (fruiting) branches plant (26.00), bolls plant (27.55), Average weight of bolls plant (3.38), No. of seeds plant (511.55) and lint weight (g) plant (19.07) than late sowing during mid May under different cultivars.
Research Progress of Rhododendron Breeding

This article has summarized Rhododendron germplasm resourses and research progress on breeding objectives of Rhododendron around the world,involved in colour,florescence,fragrance,resistance.In the end,the development of breeding techniques,especially cross and genetic engineering,is reviewed.Crossbreeding is still the key method for Rhododendron breeding and molecular biology will be highlighted in the future.
Recursive bootstrap L 1 -type regularized regression modeling based on parametric statistical test

The L 1 -type regularization approaches have been widely used for uncovering cancer driver gene based on various genome-scale information. Although the existing L 1 -type regularization methods have been widely used to various ﬁelds of research, there are several drawbacks as a tool for feature selection in high dimensional data analysis: limitation of subset size, erroneous estimation result, multicollinearity problem and time consuming procedures. We propose a novel statistical strategy, called a Recursive Random Lasso (RRLasso) for high dimensional data analysis in line with a random lasso. In order to time eﬀective analysis, we consider recursive bootstrap procedure based on random forest method. Furthermore, we introduce a parametric statistical test for variable selection based on bootstrap regression modeling results. We can see through Monte Carlo simulations that the proposed RRLasso not only provides time eﬀective performances but also performs well for high dimensional data analysis.
WIDEBAND DUAL-POLARIZED SIW CAVITY-BACKED PATCH ANTENNA WITH MULTIMODE CHARACTERISTICS

A new wideband dual-polarized patch antenna using substrate-integrated waveguide (SIW) technology is proposed in this paper. The antenna is composed of a patch radiator and a square SIW cavity. The square patch is internally embedded in the square SIW cavity with a surrounded slot. A pair of L-shaped probes are used for the excitation of the orthogonal linearly-polarized signals. The dominant resonant mode of the square patch resonator (TM010) and the modes of the SIW cavity (TE110 and TE120/TE210) are employed to achieve a wide impedance bandwidth under these resonances. By introducing two shorting pins, the isolation between two feeding ports can be enhanced to more than 21 dB. The resonant properties of these modes are investigated based on the cavity model theory. Then, their resonant frequencies are discussed to provide information for designing and optimizing such an antenna. For demonstration, a prototype is fabricated and measured. The measured results show that the proposed antenna achieves a wide impedance bandwidth about 66.7% (3.71–7.43 GHz) and 70.9% (3.58–7.52 GHz) for horizontal and vertical polarizations, respectively. A stable gain in the range of 7.15 to 8.03 dBi is obtained within the operating band. Due to the SIW cavity-backed structure, the antenna shows unidirectional radiation patterns and low back-lobe radiation at the resonant frequencies. Thus, the antenna is highly suitable for the base station antenna that is required to cover the bandwidth of 5.5 GHz WiMAX and 5.2/5.8 GHz WLAN systems.
ICHTHYOFAUNAL DIVERSITY AND CPUE (CATCH PER UNIT EFFORT) AT WATLAB GHAT, WULAR LAKE, KASHMIR

On a global level, mountains are the world’s largest repositories of biological diversity. In India, the mountain regions are characterized by the presence of cold waters, many of which harbor fish and support subsistence fishery on a large scale. There are around 8,243 km long streams and rivers, 20,500 ha natural lakes, 50,000 ha of reservoirs, both natural and manmade, and 2500 ha brackish water lakes in the high altitude (Mahanta & Sarma, 2010). These vast and varied water resources in the uplands harbour rich ichthyofaunal diversity comprising large populations of indigenous and exotic, cultivable and non cultivable fish species (Sehgal, 1999). Out of the total fish fauna available in India, 17 % fishes have been documented from the mountain ecosystems establishing the status of the area as a center of origin and evolution of biotic forms (Ghosh, 1997).
Interoperability Using Semantic Web Technologies

: Urbanization has increased tremendously over the last few years, and this has exacerbated problems in all areas of life, especially in the energy sector. The Internet of things is providing solutions to overcome the energy domain and other field’s issues. Billions of heterogeneous devices are linked to each other in its environment and this has led to the semantic interoperability problem between the connected devices. In the energy field, such as electricity and gas, several devices are interlinked. These devices cannot be understood by each other due to their heterogeneity. To overcome this problem, a model and ontology are proposed by applying Semantic Web Technologies and cloud storage to address the tracking of customers to observe Unaccounted for gas (UFG) in the gas domain of energy using. Semantization is achieved by transforming heterogeneous devices Sensor model language data into resource description framework without human interventions. Semantic interoperability is used to effectively and meaningfully share information from one location to another, therefore the proposed ontology and model focus more efficiently on customer tracking, forecasting, and monitoring to detect UFG in gas networks. This also helps to save gas companies from financial gas losses .
Dialysate calcium concentrations in hemodialysis-a linear mixed effects model

We investigated the longitudinal changes the serum calcium concentration due to changes in the calcium concentration in the dialysis bath. Over three years, 98 hemodialysis patients received three different calcium concentration dialysis bath, 3.5 mEq/L; 2.5 mEq/L; and 3.0 mEq/L. Both serum calcium concentration and parathyroid hormone (PTH) were recorded over time. Linear mixed effects model was fitted to longitudinal data of serum calcium concentration. It showed that serum calcium are clossely associated with calcium concentration in dialysate. The bath with calcium at 3.0% seems to be the most suitable.
Observational Abstract Interpreters

We are interested in algorithmically finding proofs of program judgments. Some common strategies used by program proof search algorithms include computing semantic program invariants, making hypotheses about program behavior via dynamic checks embedded in the program, and using observations about program behavior to assist in proof search. We present observational abstract interpreters , a new meta-theoretic construction, that brings these three strategies for program reasoning together into a single approach. Similar to abstract interpreters, observational abstract interpreters compute semantic program invariants. However, in observational abstract interpreters, the invariants are permitted to be hypothetical, with program safety ensured via dynamic checks and hypotheses are made based on observed behavior of the program. We formalize our ideas in the context of a simple higher-order language ( λ S ). We develop a generic observational abstract interpreter for λ S , drawing inspiration from the abstracting abstract machines (AAM) recipe for abstract interpreter construction. Observational abstract interpreters have a monadic structure and are capable of making hypotheses based on program observations during the computation of semantic program invariants. We present an instantiation of the generic observational abstract interpreter for λ S , yielding an observational variant of interval analysis for λ S .
Developing near-exact distributions for likelihood ratio statistics to test for Kronecker product structures in covariance matrices

The authors show how by using an adequate split of null hypotheses of the type H0 : V ⊗Σ, where Σ is a positive definite matrix which may or not bear some particular structure like circularity or compound symmetry, into a sequence of nested hypotheses which generate conditionally independent tests, it is possible to easily obtain not only the likelihood ratio test statistics as well as to derive the expressions for their moments. The authors also show how this split of the null hypothesis induces on the characteristic function of the logarithm of the statistic a much useful factorization and how this factorization may be used to lay down the basis for the development of very sharp near-exact distributions for the statistic, which enable an easy computation of very sharp near-exact p-values and quantiles.
Effective Boundary Conditions for the Heat Equation with Interior Inclusion

Of concern is the scenario of a heat equation on a domain that contains a thin layer, on which the thermal conductivity is drastically different from that in the bulk. The multi-scales in the spatial variable and the thermal conductivity lead to computational difficulties, so we may think of the thin layer as a thickless surface, on which we impose “effective boundary conditions”(EBCs). These boundary conditions not only ease the computational burden, but also reveal the effect of the inclusion. In this paper, by considering the asymptotic behavior of the heat equation with interior inclusion subject to Dirichlet boundary condition, as the thickness of the thin layer shrinks, we derive, on a closed curve inside a two-dimensional domain, EBCs which include a Poisson equation on the curve, and a non-local one. It turns out that the EBCs depend on the magnitude of the thermal conductivity in the thin layer, compared to the reciprocal of its thickness. AMS subject classifications: 35K05, 35B40, 35B45, 74K15
Knot deletion for robust penalized spline regression

Penalized spline regression is a well-known method to fit a smooth spline function to a set of observations. This is done by fitting to the data a system of basis functions using standard regression techniques. Regression coefficients are determined by minimizing a combination of a classical RSS criteria and a roughness penalty (in order to achieve smoothness of the resulting spline function). In using a RSS criteria the approach, however, becomes sensitive towards outliers. Therefore, robust penalized spline regression approaches replace the standard RSS measure by an appropriate robust alternative. The underlying system of basis functions is defined piecewise based on knots/breakpoints placed along the sample space. For positioning of these knots, two rather vague strategies are commonly used. Either the knots are uniformly distributed along the sample space, or they are placed according to the empirical density (i.e. by using quantiles). We propose a new placement strategy which builds upon the idea of identifying contaminated regions within the sample space. By reducing the number of knots within contaminated regions, the effect of outliers on the regression estimates can be further reduced (beyond the effect that is already achieved by robust estimators). The efficacy of the proposed procedure is shown in terms of a simulation study.
Predicting Sperm Motility and Morphology Using Deep Learning and Handcrafted Features

This paper presents the approach proposed by the organizer team (SimulaMet) for MediaEval 2019 Multimedia for Medicine: The Medico Task. The approach uses a data preparation method which is based on global features extracted from multiple frames within each video and then combines this with information about the patient in order to create a compressed representation of each video. The goal is to create a less hardware expensive data representation that still retains the temporal information of the video and related patient data. Overall, the results need some improvement before being a viable option for clinical use.