Kernel Samples Research Articles

Fused clustering mean estimation of central subspace

Recently, Yoo (Statistics 50:1086–1099, 2016) newly defines an informative predictor subspace to contain the central subspace. The method to estimate the informative predictor subspace does not require any of the conditions assumed to hold in usual sufficient dimension reduction methodologies. However, like sliced inverse regression (Li in J Am Stat Assoc 86:316–342, 1991) and sliced average variance estimation (Cook and Weisberg in J Am Stat Assoc 86:328–332, 1991), its non-asymptotic behavior in the estimation is sensitive to the choices of the categorization of the predictors and response. The paper develops an estimation approach that is robust to the categorization choices. For this, sample kernel matrices are combined in two ways. Numerical studies and real data analysis are presented to confirm the potential usefulness of the proposed approach in practice.

Physical and Chemical Fruit Quality Properties of Some Walnut Cultivars and Promising Local Selections Grown Under Plain Conditions in Malatya

This study was conducted between the years of 2017 and 2018 in Battalgazi county of Malatya Province, Turkey, in order to determine fruit quality properties of three walnut cultivars and three local walnut genotypes under plain conditions. For this aim; kernel color and shrinkage, nut height, width and thickness, nut and kernel weights, kernel/nut ratio, shell thickness were detected as physical parameters. Additionally, total oil, protein and ash contents of kernel samples were examined as chemical parameters. Results indicated significant differences in all evaluated parameters of genotypes included in the study. Nut height, width and thickness varied between 37.55 mm - 52.30 mm, 32.61 mm - 42.10 mm, and 30.09 mm - 41.10 mm, respectively. Nut weight varied between 11.22 g and 17.84 g and kernel/nut ratio varied between 43.89 % and 58.13 %. Total oil and protein contents varied between 64.10 - 73.75 %, and 13.50 - 19.66 %, respectively. The study resulted with various values in all evaluated physical and chemical parameters of the cultivars and the genotypes observed. Leading cultivar or genotype depended on different parameters, but according to overall evaluations two of the cultivars and two of the local genotypes were distinguished in terms of their performances under plain conditions. In addition, significant correlations were found between most of the characters examined in the study.

Deteriorative changes in maize kernels due to Aspergillus flavus Link. and Fusarium verticillioides (Sacc.) Nirenberg

The study aimed at measuring changes in chemical composition of maize kernels due to Aspergillus flavus Link. and Fusarium verticillioides (Sacc.) Nirenberg infection. The samples of maize kernels were incubated at 28 °C for 7, 14, 21, and 28 days. The samples were analysed for mycotoxin, moisture, crude fat, crude protein, crude ash, and crude fibre. Maize kernels inoculated with A. flavus and F. verticillioides exhibited a significant decrease in crude fat. Aflatoxin B1 (AFB1) contamination increased in maize kernels inoculated with A. flavus, and fumonisin B1 (FB1) in kernels inoculated with F. verticillioides. Crude ash and crude fibre content showed no changes. Incubation time significantly affected AFB1 and FB1 contamination levels, moisture, crude fat, and crude protein contents. AFB1 and FB1 contamination were significantly correlated with crude fat degradation. The tested strains had similar deteriorative effects on maize kernels. The significant changes in the proximate composition were only observed in maize kernels with mycotoxin contamination above the regulatory limit of 10 µg kg−1, thus not fit for human consumption.

Comparative assessment of einkorn and emmer wheat phenomes: II—phenotypic integration

Phenotypic integration, as the outcome of the number and strength of (co)variation among phenotypic traits in the genetically-related diploid einkorn (Triticum monococcum L. subsp. monococcum; genome AA) and tetraploid emmer wheat [Triticum turgidum subsp. dicoccon (Schrank) Thell.; genome BBAA] was estimated for seven modules (i.e., area, density, dimensions, dry weight, ecophysiology, structure, and yield components) based on 110 traits measured or estimated on plant, tiller, leaf, spike, spikelet and kernel samples at three growth stages during four growing seasons. Classical methods of phenotypic integration assessment using algorithms for dimensionality reduction, matrix correlation, ordination, discrimination, multidimensional scaling and functional relationships, generated insightful but inconsistence estimates of phenotypic integration; thus, making it challenging to compare the strength of integration across species and modules using a single and reliable phenotypic integration index. Divergence between einkorn and emmer due to polyploidy, although evident at the mean phenotypic index at whole plant phenotypic level (0.37 ± 0.08 and 0.59 ± 0.09, respectively), was manifested more at the reproductive than at the vegetative level; while at a multidimensional scale, emmer exhibited larger correct classification (86.0%) than einkorn (69.5%). A standardized z-score, which was based on partial least squares analysis of trait variation, when adjusted for plant size, provided unbiased estimates of phenotypic integration indices comparable across modules and species (minimum z-score of 16.9 ± 1.1 for density module in einkorn to 32.1 ± 2.0, for yield components module in emmer). Despite the polyploidy diversity bottleneck in emmer, both species overlap in the range of their genetic variation for many traits and seemingly share some phenotypic intermediate forms. Phenotypic variances are unequal among einkorn and emmer; their differences could be attributed to emmer’s polyploidy diversity bottleneck and manifested at the different numbers of significant common principal components shared between the species, with a range from three (area module) to nine (dimensions module) shared common components. Future research needs to explore how inter- and intraspecific phenotypic variation affect population dynamics and performance under field conditions. As potential alternative crops in future cropping systems, considerable opportunities and benefits, in influencing the structure and function of agroecosystems, are expected if intraspecific trait databases are developed for these early domesticates.

The limiting behavior of certain sampling series and cardinal splines

Many families of cardinal sampling kernels {Φα(x):α∈I} consist of members that decay rapidly as x→±∞ and enjoy the property that, as the parameter α tends to its limiting value, Φα(x) tends to the classical cardinal sine, sinc(x). We study the limiting behavior of the sampling series ∑ncnΦα(x−n) when the data sequence {cn} remains fixed. In the case when the sampling kernels are damped cardinal sines, appropriate conditions on the damping functions allow us to characterize the limiting behavior of the corresponding sampling series for a wide class of data sequences {cn}.These results lead to conclusions regarding the limiting behavior of classical piecewise polynomial cardinal splines when the fixed data sequence {cn} is of polynomial growth and the order tends to ∞. One application of this development shows that the classical spline summability method is regular. Another consequence, perhaps more interesting and unexpected, is a resolution of I. J. Schoenberg’s conjecture concerning the recovery, in terms of their samples {f(n)}, of members f(x) of the Bernstein class via the spline summability procedure.

Gravimetric and Spectrophotometric Determination of Surface Wax Content in Maize Kernels

There are several methods available to determine wax content in maize kernel. These methods are based on gravimetric, spectrophotometric, and chromatographic analyses. This study aims to determine the surface wax content on maize kernel with different reference methods and develop NIR (Near Infrared Reflectance Spectroscopy) calibration models based on those reference methods. In this study, samples from 250 different maize genotypes were used. Surface wax content in the samples was determined by gravimetric and UV-Spectrophotometric methods. The results of each reference method were used to develop NIR prediction models for surface wax content in kernel samples. Our results suggest that there is a medium-level correlation (r=0.708) between the data obtained from spectrophotometric and gravimetric methods. Also, significant differences were observed between the wax content obtained from reference methods. It has been found that the NIR calibration model (RMSE =4.381, R2 =0.143, r =0,378, RPD =1.08) based on the gravimetric method had lower estimation power than the NIR calibration model (RMSEC =7.257, R2 =0.221, r =0.471, RPD =1.34) based on the UV spectroscopic method. Keywords: Near Infrared Spectroscopy, Biochemical test, Zea mays, Regression DOI: 10.7176/JSTR/5-9-11

Non-parametric stochastic subset optimization for reliability-based importance ranking of bridges in transportation networks

• Proposes a non-parametric stochastic subset optimization algorithm for reliability-based importance ranking of bridges. • Proposes iterative approach with adaptive stochastic sampling to improve efficiency of algorithm. • Number of iterations only grows logarithmically with respect to the total number of bridges in the transportation network. • Algorithm is highly efficient and especially useful for large-scale networks with many bridges. Establishing reliability-based importance ranking of bridges in a transportation network usually entails significant computational efforts, especially for large-scale networks. This paper proposes a non-parametric stochastic subset optimization (NP-SSO) algorithm to efficiently identify the reliability-based importance ranking of bridges. It first generates failure samples from an augmented failure distribution and then directly establishes the importance ranking by comparing the number of failure samples for each bridge. To improve the efficiency, an iterative NP-SSO algorithm is established by restricting the search space to the subset of bridges identified in the previous iteration. For each iteration, the modified accept-reject algorithm with adaptive kernel sampling density (AKSD) is adopted to efficiently generate failure samples. The proposed NP-SSO algorithm is highly efficient for identification of important bridges, and the number of iterations only grows logarithmically with respect to the number of bridges, and NP-SSO is especially useful for large-scale networks with many bridges. The effectiveness and efficiency of NP-SSO are demonstrated through identifying the importance ranking of bridges in the transportation network of Los Angeles and Orange counties.

Isolation, Molecular Identification and Mycotoxin Profile of Fusarium Species Isolated from Maize Kernels in Iran.

Fusarium species are among the most important fungal pathogens of maize, where they cause severe reduction of yield and accumulation of a wide range of harmful mycotoxins in the kernels. In order to identify the Fusarium species and their mycotoxin profiles associated to maize ear rot and kernel contamination in Iran, a wide sampling was carried out from field in ten major maize-producing provinces in Iran, during 2015 and 2016. From 182 samples of maize kernels, 551 strains were isolated and identified as belonging to Fusarium genus. Among the 234 representative strains identified at species level by translation elongation factor (EF-1α) sequences, the main Fusarium species were F. verticillioides and F. proliferatum, together representing 90% of the Iranian Fusarium population, and, to a lesser extent, F. incarnatum equiseti species complex (FIESC), F. thapsinum and F. redolens. Fumonisin (FBs) production by F. verticillioides and F. proliferatum representative strains was analysed, showing that all strains produced FB1. None of F. verticillioides strains produced FB2 nor FB3, while both FB2 and FB3 were produced only by F. proliferatum. Total mean of FBs production by F. verticillioides was higher than F. proliferatum. The occurrence of different Fusarium species on Iranian maize is reason of great concern because of the toxigenic risk associated to these species. Moreover, the diversity of the species identified increases the toxigenic risk associated to Fusarium contaminated maize kernels, because of the high possibility that a multi-toxin contamination can occur with harmful consequences on human and animal health.

A Hierarchical Approach for Audio Capture, Archive, and Distribution

Recent interest in high-resolution digital audio has been accompanied by a trend toward higher sampling rates and expanded bit depths, yet the sound quality improvements show diminishing returns, which can be explained by a failure to reconcile human auditory capability with the information capacity of the audio channel. The authors propose an audio capture, archiving, and distribution framework based on sampling kernels having finite length, unlike the

Compressed Sensing with Gaussian Sampling Kernel for Ultrasound Imaging

Recently, compressed sensing (CS) has been applied to ultrasound imaging for either data reduction or frame rate improvement. However, there are no detailed reports yet on strategies for lateral undersampling of channel data in conventional focused beamforming (CFB) and its recovery exploiting the CS approach. We propose a strategic lateral undersampling approach for channel data using the Gaussian sampling scheme and compare it with a direct extension of the often-used uniform undersampling reported for axial undersampling to the lateral direction and 2-D random sampling reported in the literature. As opposed to the reported 2-D random undersampling, we explore undersampling of channel data in the lateral direction by acquiring radiofrequency data from only a reduced number of chosen receive elements and subjecting these data to further undersampling in the axial direction. The effect of the sampling schemes on CS recovery was studied using data from simulations and experiments for various lateral and axial undersampling rates. The results suggest that CS-recovered data from the Gaussian distribution-based channel data subsampling yielded better recovery and contrast in comparison to those obtained from the often-used uniform distribution-based undersampling. Although 90% of the samples from the original data using the proposed sampling scheme were discarded, the contrast of the CS-recovered image was comparable to that of the reference image. Thus, CS with the proposed Gaussian sampling scheme for channel data subsampling not only reduces the data size significantly, but also strategically uses only a few active receive elements in the process; thus, it can provide an attractive option for the affordable point-of-care ultrasound system.

Design and development of rapid and environment friendly conditioner for cashew nut processing

AbstractConventional hot air conditioning of cashew kernels before peeling is a time‐consuming operation causing environmental pollution. The aim of this research study was to design and develop a rapid and environment‐friendly cashew kernel conditioner and study the quality of the conditioned cashew kernels. Infrared heating was found suitable for rapid conditioning of cashew kernels, based on which a rapid conditioning machine was designed and developed. Drying kinetics study revealed that developed conditioner was suitable for rapid cashew kernel conditioning. Diffusivity values for conventional hot air and developed conditioning were found as 1.80 × 10−10 and 1.96 × 10−9 m2/s, respectively. Sensory evaluation study revealed no significant differences (p < .05) among differently conditioned cashew kernel samples. The well‐insulated conditioner was energy efficient (77.78%) and its operation had no harmful impact on the environment.Practical applicationsDeveloped infrared cashew kernel conditioner was found suitable for rapid cashew kernel conditioning. It is environmental friendly and energy efficient in operation. This developed conditioning machine would meet the requirement of cashew nut processing industries as suitable replacement of conventional hot air dryers. Processing time would get reduced significantly and the compact design would allow to accommodate the machine in less floor space. Developed machine is easy to operate for anyone with basic technical knowledge.

Climatic Effects on Quality Parameters and Their Relationships of Bread Wheat Genotypes (Triticum aestivum L.) Grown under Semi-arid Region

Aims: This study was conducted to determine climatic effects to quality parameters of bread wheat genotypes grown in the semi-arid region.
 Study Design: The experiment was carried out in a randomized complete block design with three replications. Thirty-three bread wheat genotypes (Triticum aestivum L.) were grown in a field trial and kernel samples analyzed for thousand kernel weight, test weight, protein content, wet gluten, dry gluten and SDS-sedimentation value.
 Place and Duration of Study: The research was carried out during the 2008 and 2009 growing seasons at Sanliurfa, Turkey.
 Methodology: For analyses 20 main spikes that contained fully developed kernels were chosen randomly from each plot and taken to the laboratory for analyses. The nitrogen content of kernels was determined using the Kjeldahl method and the result was multiplied by the factor 5.7 to calculate the protein content of kernels. Test weight and Sodium Dodecyl Sulphate (SDS)-sedimentation values were determined using standard procedures. Wet and dry gluten values were determined using a glutomatic system after separating gluten from the soluble starch and protein fractions.
 Results: Genotypes were significant (P ≤ 0.01) for all tested characteristics. Thousand kernel weight ranged from 25.8 to 42.3 g, test weight from 73.7 to 81.7 kg hl-1, protein content from 9.7 to 14.8%, wet gluten from 28.5 to 42.2%, dry gluten from 9.4 to 14.1% and SDS-sedimentation value from 19 to 39 ml. Bezostaya-I, Kutluk-94, Altay-85, Harmankaya-99, Lirasa, Kirgiz-95 and Dagdas-94 genotypes had the best quality among tested genotypes in the semi-arid climatic conditions. Protein content was positively correlated with wet gluten, dry gluten and SDS-sedimentation value, respectively. Relationships were significant between wet gluten and both dry gluten and SDS-sedimentation value. A positive significant correlation was found between thousand kernel weight and SDS-sedimentation value and between dry gluten and SDS-sedimentation value.
 Conclusion: Protein content, wet and dry gluten and SDS-sedimentation value were affected by climatic conditions of semi-arid region. Protein content, wet and dry gluten value were high but SDS-sedimentation value were low due to high temperature and low precipitation in semi-arid region.

Climatic Effects on Quality Parameters and Their Relationships of Bread Wheat Genotypes (Triticum aestivum L.) Grown under Semi-arid Region

Aims: This study was conducted to determine climatic effects to quality parameters of bread wheat genotypes grown in the semi-arid region.
 Study Design: The experiment was carried out in a randomized complete block design with three replications. Thirty-three bread wheat genotypes (Triticum aestivum L.) were grown in a field trial and kernel samples analyzed for thousand kernel weight, test weight, protein content, wet gluten, dry gluten and SDS-sedimentation value.
 Place and Duration of Study: The research was carried out during the 2008 and 2009 growing seasons at Sanliurfa, Turkey.
 Methodology: For analyses 20 main spikes that contained fully developed kernels were chosen randomly from each plot and taken to the laboratory for analyses. The nitrogen content of kernels was determined using the Kjeldahl method and the result was multiplied by the factor 5.7 to calculate the protein content of kernels. Test weight and Sodium Dodecyl Sulphate (SDS)-sedimentation values were determined using standard procedures. Wet and dry gluten values were determined using a glutomatic system after separating gluten from the soluble starch and protein fractions.
 Results: Genotypes were significant (P ≤ 0.01) for all tested characteristics. Thousand kernel weight ranged from 25.8 to 42.3 g, test weight from 73.7 to 81.7 kg hl-1, protein content from 9.7 to 14.8%, wet gluten from 28.5 to 42.2%, dry gluten from 9.4 to 14.1% and SDS-sedimentation value from 19 to 39 ml. Bezostaya-I, Kutluk-94, Altay-85, Harmankaya-99, Lirasa, Kirgiz-95 and Dagdas-94 genotypes had the best quality among tested genotypes in the semi-arid climatic conditions. Protein content was positively correlated with wet gluten, dry gluten and SDS-sedimentation value, respectively. Relationships were significant between wet gluten and both dry gluten and SDS-sedimentation value. A positive significant correlation was found between thousand kernel weight and SDS-sedimentation value and between dry gluten and SDS-sedimentation value.
 Conclusion: Protein content, wet and dry gluten and SDS-sedimentation value were affected by climatic conditions of semi-arid region. Protein content, wet and dry gluten value were high but SDS-sedimentation value were low due to high temperature and low precipitation in semi-arid region.

RESPONSE OF HEAVY METAL CONTENTS IN APRICOTS TO DIFFERENT TRANSPORTATION MODES

In order to evaluate the effects of different transportation hubs on cultivated soil and apricots, macro and micro elements and heavy metal contents of fruit, leaf, kernel and soil samples collected from apricot orchards located at the border of the railroad, the motorway, the airport, and an orchard far from transportation modes were detected by ICP/OES (inductively coupled plasma / optical emission spectrometry). The results indicated the highest Cd, Pb and Ni contents of soil, fruit, and kernel samples under impacts of railroad transportation modes, whereas the highest contents of leaf were found under motorway side. All fruit samples contained higher amounts of Cd and Pb compared to permissible limits of FAO/WHO, and contents differentiated between sampling locations. There were no correlative relations found between transportation modes and macro-micro element contents. As a conclusion, in terms of heavy metal contamination, the orchards located at railway sides have the highest risk and this was followed by motorway side.

Non-Iterative MDS Method for Collaborative Network Localization With Sparse Range and Pointing Measurements

Multi-agent localization is a basic requirement for many networked applications. The particular application to swarming unmanned aerial vehicles (UAVs) or munitions requires spatial coordination of agents, including the ability to assume and maintain a prescribed flight formation. An in-flight awareness of network morphology and node location is therefore needed. While global navigation satellite systems offer an attractive solution, signal occlusion, spoofing, and jamming present unacceptable vulnerabilities-particularly for mission-critical operations. Alternative network localization methods using inter-agent radio frequency ranging and Angle of Arrival have been well studied over the past 15 years, but existing algorithms are not well suited to fast-moving networks. Iterative methods tend to converge slowly. Faster noniterative multi-dimensional scaling (MDS) methods for range, bearing, or vector measurements have also been formulated. However, these MDS methods generally require the full pairwise inter-agent measurement matrix-placing a severe requirement on swarm connectivity and leading to low tolerance for missing or badly estimated measurements. Even vector-based MDS, which incorporates both range and direction constraints, is shown here to require 4-vertex connectivity to achieve perfect localization. Results from rigidity theory, however, suggest that a lower connectivity threshold should be sufficient to guarantee a unique configuration (up to translation and rotation). In contrast, our proposed “vertex resequencing” and “edge resequencing” techniques further lower the vertex-connectivity threshold to 3 and 2, respectively. These localization techniques, which extend vector-based MDS with Nyström approximation, prescribe a graph-based kernel sampling scheme and weighted coordinate reconstruction that suppress the effect of missing measurements.

Production of activated carbon from corn cobs and mango kernels via H3PO4activation and mediated hydrothermal treatment

In this study, the activated carbon produced from mango kernels and corn cobs by impregnating the hydrothermally treated raw materials with 85% H3PO4were characterize for their physical surface morphology and types of surface functional groups using SEM and FT-ir, respectively. Six samples of activated carbon were submerged for 1 hour, the second sample for 2 hours, and the third sample for 3 hours. SEM results showed that both KAC (Kernel Activated Carbon) and CAC (Corn Activated Carbon) had increasing roughness and irregularity along with residence time of the samples. Results from FT-ir (Fourier-transform infrared spectroscopy) testing of the mango kernels samples showed that a C-O stretch, C-H, C=O stretch, and C-N stretch on the surface. While corncobs consist of C-H bend, and O-H bend for the 1-HR sample. The 2-HR and 3-HR samples consist of C-O stretch, C-H wag, C-N stretch. Analysis of the relationship between residence time and adsorptive capacity was done using AAS via batch adsorption in a tri-metal solution of Cu(Copper), Ni(Nickel), and Pb(Lead) with results that showed CAC and KAC, with soaking time of 3 hours is a good adsorbent of Copper and Nickel, while soaking time of 2 hours yields the best adsorption conditions for both CAC and KAC.

Development of High-Speed Dual-Camera System for Batch Screening of Aflatoxin Contamination of Corn Using Multispectral Fluorescence Imaging

Abstract. Aflatoxins are fungal toxins produced by . Food and feed crops contaminated with carcinogenic aflatoxins result in economic losses as well as potentially serious health issues. Grain elevators need to unload, on average, one 2.27 metric ton (MT) truckload every 2 min. Current sampling-based analytical chemistry methods for aflatoxin detection cannot meet these large throughput screening requirements. Therefore, a high-speed, batch screening system with reliable accuracy is needed. To develop a high-speed multispectral screening system, two high-performance cameras in conjunction with dual UV excitation sources and novel image processing software were used to collect fluorescence images of corn samples. Each camera simultaneously captures a single-band fluorescence image (436 nm or 532 nm) from corn kernel samples, and the detection software processes the images to automatically detect contaminated kernels using a normalized difference fluorescence index (NDFI). The system was tested with various commercial samples collected from different locations in the U.S. and baseline samples that were prepared by artificial field inoculation. Each sample was imaged and screened four times, and the screened samples were chemically analyzed for aflatoxin content. All samples were shuffled between imaging repetitions to increase the likelihood of screening both the germ and endosperm sides of every kernel. Processing time for each screening was less than 1 s. Sensitivity and specificity were measured over given thresholds for NDFI, and the results were promising. High sensitivity (0.987) and specificity (0.96) were achieved for the baseline samples, and high sensitivity (0.75 to 1) and somewhat lower specificity were obtained for the commercial samples. Keywords: Aflatoxin, Corn, Dual cameras, Fluorescence image, Multispectral image, Rapid and non-destructive detection, detection.

Detection of mycoflora and mycotoxin in raw peanut Arachis hypogea L. kernels in Bangladesh

Raw peanut kernel samples were collected from 13 areas of Bangladesh for determination of mycoflora and mycotoxin. Fungi associated with the tested samples throughout the investigation were Aspergillus flavus, A. niger, Aspergillus sp. (1), Aspergillus sp. (2), Penicillium sp., Fusarium sp., Rhizopus sp. and Curvularia sp. A. flavus was the most common fungus followed by A. niger. Colonies of fungi were found to form 79.81 to 98.10% of raw peanut kernels. Out of the 13 samples, eight were found to be contaminated with 11.91 to 182.6 ppb of total aflatoxins and five samples were free from aflatoxins.

Diversity and mycotoxin production by Fusarium temperatum and Fusarium subglutinans as causal agents of pre-harvest Fusarium maize ear rot in Poland

Maize ear rot is a common disease found worldwide, caused by several toxigenic Fusarium species. Maize ears and kernels infected by Fusarium subglutinans contained significant amounts of beauvericin, fusaproliferin, moniliformin, and enniatins. In 2011, F. subglutinans sensu lato has been divided into two species: Fusarium temperatum sp. nov. and F. subglutinans sensu stricto, showing different phylogeny and beauvericin production within the populations of maize pathogens in Belgium. Isolates of the new species—F. temperatum—were also identified and characterized in Spain, Argentina, Poland, France, and China as one of the most important pathogens of maize. Moreover, F. temperatum was proved to be pathogenic to maize seedlings and stalks. We identified Fusarium isolates obtained from diseased maize ears collected between 2013 and 2016 in Poland (321 isolates). Based on morphological analyses, six Fusarium species were identified. Molecular identification performed on the set of selected isolates (42 isolates) revealed 34 isolates to be F. temperatum and only five to be F. subglutinans. Interestingly, the phylogenetic analysis showed that the population of F. temperatum infecting maize in Poland remained quite uniform for over 30 years with only a few exceptions. For the first time, a single isolate of Fusarium ramigenum was detected from the area of Poland. Significant amounts of BEA were found in Fusarium-damaged kernels. The same kernel samples contained also enniatins A1, A, B1, and B. The results clearly demonstrate the occurrence of F. temperatum as maize pathogen in Poland for over the last three decades.

A combined projection-outline-based active learning Kriging and adaptive importance sampling method for hybrid reliability analysis with small failure probabilities

In this paper, the adaptive importance sampling (AIS) method is extended for hybrid reliability analysis under random and interval variables (HRA-RI) with small failure probabilities. In AIS, the design space is divided into random and interval variable subspaces. In random variable subspace, Markov Chain Monte Carlo (MCMC) is employed to generate samples which populate the failure regions. Then based on these samples, two kernel sampling density functions are established for estimations of the lower and upper bounds of failure probability. To improve the computational efficiency of AIS in cases with time-consuming performance functions, a combination method of projection-outline-based active learning Kriging and AIS, termed as POALK-AIS, is proposed in this paper. In this method, design of experiments is sequentially updated for the construction of Kriging metamodel with focus on the approximation accuracy of the projection outlines on the limit-state surface. During the procedure of POALK-AIS, multiple groups of sample points simulated by AIS are used to calculate the upper and lower bounds of failure probability. The accuracy, efficiency and robustness of POALK-AIS for HRA-RI with small failure probabilities are verified by five test examples.