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A description of almost all the current widely used methods used for the interpretation of the results obtained in analyzing gases dissolved in oil (AGDO) is presented. In addition, a new method for identifying defect types, an algorithm for its application, and the advantages thereof over other methods are described. A technique for testing the reliability of the AGDO interpretation methods and results obtained upon testing these methods are presented. The quality of the detection provided by the considered methods has been compared with respect to various types of defects. In the course of the testing, special attention was paid to examining the issue that different methods of AGDO interpretation can identify different number of defect types. The evaluation of the quality of the methods and the testing of their reliability have been performed for a test sample containing 134 cases of transformer damage. For each case, there is a detailed description of the defect development process (the results of analyzing and testing of a transformer for the preceding period and the records in the operational documentation), as well as the results of transformer investigation, in the course of which the cause of damage done to it has been established. © 2019, Allerton Press, Inc.

A bstract Friuli Venezia Giulia (FVG, NE Italy) is an area of maximum rainfall in the whole Alpine chain territory, reaching more than 3200 mm of mean annual rain in the Julian Prealps. According to recent climatological studies, the same area is also one of the European spot in recent lightning climatologies, meaning that convective rain plays an important role in the total rainfall. A network of 104 raingauges placed around the FVG territory is used to extract the absolute maximum rain accumulated every 6 hours in four subareas of FVG. In an attempt to improve the original ECMWF maximum rain, these data have been targeted to develop 32 statistical downscaling models, according to the period of the day, of the year and specific sub-area. ECMWF 6-hour rain forecasts available for all the gridpoints encompassed in the FVG territory and some derived variables (absolute values, anomalies, standardized values, plus mean, max and SD in time and/or space) have been used as predictors. With respect to a previous version of this work, here also the instability pseudo-indices (derived from the vertical profile with the maximum vertical resolution available in the ECMWF hybrid levels) are used as candidate predictors. Moreover, also non-linear methods, namely neural networks, are implemented, together with exhaustive multiregression models. Results show that the 32 models improve -on average- R2 of 12% on the validation sample and of 5% on the 2017 test sample, with respect to the ECMWF rain forecast, but the improvement is particularly notable during the convective season (18%).

To realize the commercialization of SOFC, it must be ensured that it can work efficiently and stably. SOFC fault diagnosis becomes an essential part of the research. Due to the strong coupling of faults in the stack, this paper uses neural network algorithm to detect and diagnose faults. The simulation results verify that through the diagnosis of the test sample, the recognition rate of the test sample by the network is found to be 95%, which explains the neural network fault diagnosis model established in this paper on identifying the normal working state of the stack, the electrode stacking of the stack, and the gas leakage fault of the stack has good effectiveness and accuracy.

Indonesian nature is very rich in plants containing essential oils. Essential oils can be produced from various parts of plants such as roots, stems, leaves, flowers, or fruit. There are 50-200 species of essential oil producing plants. In Indonesia there are around 40 types of essential oils producing plants. One of the essential oils producing plants is lemongrass (Taufiq, Tuhana, 2008 p: 2). This study aims to determine the comparison of essential oils in Lemongrass (Cymbopogon citratus) which are harvested at 6 months and 9 months with a water distillation method. The procedure for selecting samples and research samples, directly with the object of research, is the citronella plant whose harvest age is 6 months taken from 1 clump and 9 months taken from 1 clump. The results of this study indicate that lemongrass plants with a harvest age of 6 months produce more essential oils compared to lemongrass plants with a harvest age of 9 months. This is influenced by several factors, including climate, harvest age. Lemongrass plants aged 6 months are the optimal age to be harvested, so that they get a higher yield of essential oils, besides that the factors of soil fertility and the intensity of sunlight also affect the content of essential oils of lemongrass.From the results of the research that has been done on the test sample, it can be concluded that the results of the essential oil ration on citronella (Cymbopogon citratus), which has 6 months of harvest age, obtained an average yield of 0.46% v / b and 9 months. 0.35% v / b with the results of the t-test obtained by the price t-count (2.984)> t-table (2.776). So that H0 is rejected and Ha is accepted and it can be concluded that there is a significant difference between lemongrass with 6 months and 9 months of harvest. Keywords: lemongrass, essential oil, age of harvest, water distillation

The energy approach for determining the durability of structural elements at high temperature creep and hydrogen activity was proposed. It has been shown that the approach significantly simplifies research compared with the known ones. Approbation of the approach was carried out on the example of determining the indicators of durability of the Bridgman sample under conditions of creep and different levels of hydrogenation of the metal. It was shown that with an increase of hydrogen concentration in the metal from 2 to 10 ppm, the durability of the test sample decreased from 22 to 58%.

Fiber intake improves gut health and prevents non-communicable diseases. The current study investigates the substitution of carrageenan in white bread and evaluates its effect on the physiochemical and structural characteristics of bread. The 100% wheat flour was used as control and the test sample contained 4% carrageenan. The physio-chemical analysis showed that carrageenan-substitution improved the hydration properties of the flour (WHC—1.33 g/g; SC—3.50 ml/g). Carrageen substituted bread had reduced the loaf volume. The fiber content in carrageenan-substituted bread was noticeably higher (9.4 g%) than control (3.5 g%). Crude lipid (4.6 g%) and protein (7.0 g%) content improved with carrageenan-substitution. The mineral contents (Na, K, Mg, Ca, Fe, and Zn) were increased in carrageenan-breads. The texture profile analysis showed a decreased hardness (H1—92.3 N, H2—62.5 N) and improved springiness (5.3 mm) in carrageenan-bread.

The paper provides a brief review of modern sources of ultraviolet (UV) radiation used in fluorescent penetrant testing (FT). The differences in implementation of the method with different radiation sources (excilamps, mercury UV lamp and LED UV lamp) are revealed experimentally. It is shown that the XeCl excilamp is not inferior to other sources of UV radiation used in FT.

During the construction of the unbound pavement layers of Corridors X and XI in Serbia, a problem was detected regarding the evaluation of cleanliness of the available materials. The European standard for the sand equivalent from 1999 to 2015 had significant changes related to the test procedure. A mass correction has been taken into account. It consists of replacing a part of the fines with rinsed material (0.063/2 mm) when the percentage of fines exceeds 10% in a tested fraction. Thus, the test sample usually yields high values of the sand equivalent, which often exceeds the limits of 50% and 60%. From the standpoint of the technical specifications, aggregates are considered clean, while using other methods like methylene blue or a plasticity index, the presence of clay particles above the limits can be confirmed. This paper presents the results of research on 63 laboratory-prepared test samples of limestone aggregate with small quantities of the smectite group of clay minerals, where the fines content in the unbound aggregate, 0/31.5 mm, gradually increased up to 20%. It was found that the sand equivalent values are continuously decreasing from 84 to 14% in the fraction 0/2 mm and from 87 to 18% in the 0/4 mm fraction, with increasing of the methylene blue value of the all-in aggregate (MBA) from 0.4 to 4.5 g/kg. Correlations of different methods and fractions, as well as with the content of fines were found. There are no correlations with SE10 because of grouping the test data around one point.

White skin of watermelon and the liquid of kepok banana tree trunk empirically widely used as hair growth, In addition ethanol extract white skin of watermelon and the liquid of kepok banana tree trunk has also been scientifically proven able to accelerate the growth hair of the rat. However the research activity of the combination of the two against the hair growth has never done. The purpose of this research is to know the combination of both these extracts which can give of the most activity is good for hair growth. Ethanol extract of white skin of watermelon extraction is done by of maceration using a solvent of ethanol 70 %, as for getting the test sample is liquid of kepok banana tree trunk done by cutting down banana tree trunk approximately 50 cm above the ground surface then the stem of banana leaf his satin-the core then covered with plastic and silenced for one night. The results showed that the combination of liquid of kepok banana tree trunk and Ethanol extract white skin of watermelon (2:1) have a long hair growth activity higher than the positive control (Minoxidil), While hair growth activity of weights lower than the positive control (Minoxidil)

The goal to improve prediction accuracy and robustness of predictive models is quite important for time series prediction (TSP). Multi-model predictions ensemble exhibits favorable capability to enhance forecasting precision. Nevertheless, a static ensemble system does not always function well for all the circumstances. This work proposes six novel dynamic ensemble selection (DES) algorithms for TSP, including one DES algorithm based on Predictor Accuracy over Local Region (DES-PALR), two DES algorithms based on the Consensus of Predictors (DES-CP) and three Dynamic Validation Set determination algorithms. The first dynamic validation set determination algorithm is designed based on the similarity between the Predictive value of the test sample and the Objective values of the training samples. The second one is constructed based on the similarity between the Newly constituted sample for the test sample and All the training samples. Finally, the third one is developed based on the similarity between the Output profile of the test sample and the Output profile of each training sample. These proposed algorithms successfully realize dynamic ensemble selection for TSP. Experimental results on twelve benchmark time series datasets have demonstrated that the proposed DES algorithms greatly improve predictive performance when compared against current state-of-the-art prediction algorithms and the static ensemble selection techniques.

The purpose of this research is to know whether the influence of bulk aggregate grains influence the targeted compressive strength value, test sample to concrete compressive strength. This research is a quantitative research that starts from the testing of materials used as concrete materials and make the concrete mix design based on the procedure to make normal concrete on SNI 03-2834-2000. The analyzing data using simple statistical method by comparing the average value of compressive strength from various sample forms. From the result of analysis, obtained data from of compressive strength achieved with the bulk of the granular aggregate grains used differently. The coarse aggregate grain also affects the compressive strength value with the greater tendency of coarse aggregate grains used, the greater the compressive strength.

Two Phase Test Sample Sparse Representation (TPTSSR) classifier was recently proposed as an efficient alternative to the Sparse Representation Classifier (SRC). It aims at classifying data using sparse coding in two phases with l2 regularization. Although high performances can be obtained by the TPTSSR classifier, since it is a supervised classifier, it is not able to benefit from unlabeled samples which are very often available. In this paper, we introduce a semi-supervised version of the TPTSSR classifier called Semi-supervised Two Phase Test Sample Sparse Representation (STPTSSR). STPTSSR combines the merits of sparse coding, active learning and the two phase collaborative representation classifiers. The proposed framework is able to make any sparse representation based classifier semi-supervised. Extensive experiments carried out on six benchmark image datasets show that the proposed STPTSSR can outperform the classical TPTSSR as well as many state-of-the-art semi-supervised methods.

Recently, the structural analysis of protein complexes by cryo-electron microscopy (cryo-EM) single particle analysis (SPA) has had great impact as a biophysical method. Many results of cryo-EM SPA are based on data acquired on state-of-the-art cryo-electron microscopes customized for SPA. These are currently only available in limited locations around the world, where securing machine time is highly competitive. One potential solution for this time-competitive situation is to reuse existing multi-purpose equipment, although this comes with performance limitations. Here, a multi-purpose TEM with a side entry cryo-holder was used to evaluate the potential of high-resolution SPA, resulting in a 3 Å resolution map of apoferritin with local resolution extending to 2.6 Å. This map clearly showed two positions of an aromatic side chain. Further, examination of optimal imaging conditions depending on two different multi-purpose electron microscope and camera combinations was carried out, demonstrating that higher magnifications are not always necessary or desirable. Since automation is effectively a requirement for large-scale data collection, and augmenting the multi-purpose equipment is possible, we expanded testing by acquiring data with SerialEM using a β-galactosidase test sample. This study demonstrates the possibilities of more widely available and established electron microscopes, and their applications for cryo-EM SPA.

Background ThePhageDx™ E. coli O157:H7 Assay originally required a 5 h enrichment period and a 10 mL test sample for 25 g ground beef. The proposed method modifications seek to include beef trim (375 g) matrix and a new method procedure for 25 g ground beef comprised of 6–7 h enrichment and 1 mL test sample. Objective To validate the method modifications for PhageDx E. coli O157:H7 Assay to include beef trim (375 g) matrix and modify enrichment time and test sample size for 25 g ground beef. Method For both matrixes, pre-warmed tryptic soya broth (TSB; 42 ± 1°C) was added in a 3:1 (media:sample size) ratio, blended, and enriched for 9–10 h (beef trim, 375 g) or 6 h (ground beef, 25 g) at 42 ± 1°C. One milliliter samples were transferred to a microfuge tube, centrifuged, and the supernatant removed. The pellet was resuspended in 0.2 mL of TSB and phage reagent was added. Samples were incubated for 2 h at 37 ± 1°C. After infection, the sample was centrifuged, and 150 µl of the supernatant was transferred to a 96-well plate. Then, lysis buffer and luciferase substrate were added and the sample read on a luminometer to determine the presence or absence of E. coli O157:H7. Results No significant differences were found between the PhageDx E. coli O157:H7 Assay method modifications and the U.S. Department of Agriculture Food Safety and Inspection Service (FSIS) Microbiology Laboratory Guidebook (MLG) reference method. Conclusions The independent study demonstrated that the PhageDx E. coli O157:H7 Assay method modifications meet the qualifications for Performance Tested Method SM status. Highlights The PhageDx E. coli O157:H7 Assay is a rapid detection method capable of detecting E. coli O157:H7 in 25 g ground beef and 375 g beef trim samples in as little as 8.5 h and 11.5 h total turn around time, respectively.

The sensing of neurotransmitters is currently a difficult task for scientists in the field of biomedical research. The design of a new compact and accurate sensor would benefit greatly the research being done on neurotransmitters. In this effect, optoelectronic sensors are ideal candidate as they are non-invasive and non-reactive to the test sample. This work describes the design, fabrication, calibration, and tests of a new type of neurotransmitter sensor using spectral analysis based on a Grism. This new optical sensor was fitted with a self calibrating algorithm and showed superior resolution as well as a smaller footprint than previously observed.

This study evaluated the feasibility of various 3D printed mid-turbinate swab designs in terms of both manufacturability and liquid retention. Earlier studies showed that mid-turbinate swab testing was a preferred SARS-CoV2 test method[1]. We chose to use HP Multi Jet Fusion (MJF) for manufacturing due to the need for rapid, cost-effective production. For each of the swab designs, we looked at their ability to hold fluid. The assumption was that the larger the test sample, the more likely the test would be accurate. We then looked at the manufacturing yield of each swab. After investigating each design configuration's liquid retention, quality, and production yield, we ranked these swabs based on three metrics: 3D print quality, post-print process quality, and sample collection efficiency. We found that our "wavy design" topped all three metrics.

Based on minute-by-minute precipitation data from 1961 to 2016 obtained from the National Basic Weather Station of Shapingba, Chongqing, China, the Chicago rainstorm method, the Pilgrim & Cordery (P&C) method, the Common-frequency method and the natural rainstorm moving regularity (NRMR) method were used to design short-duration (1–3 h) and long-duration (24 h) rainstorm hyetographs. Then, a standardization method for test samples was designed to ensure that each test sample was standardized and underwent an actual precipitation process. The designed rainstorm hyetographs and test samples were compared and analyzed, and the results show that the hyetographs for the 1, 3, and 24-h durations obtained with the NRMR method best represented the actual corresponding precipitation processes. The hyetograph for the 2-h duration obtained with the Chicago rainstorm method was best for the actual 2-h precipitation process. The design results of the Common-frequency method were generally good, but not outstanding. Finally, the P&C method showed a relatively poor performance for each duration.

This paper describes a full-scale test carried out on a high-rise reinforced concrete wall subjected to fire loading conditions in the special Vulcain furnace (very large and modular gas furnace) at CSTB, France. The test sample was designed to represent a slender wall with a very large height/thickness ratio. The experimental results showed a large deflection of the high wall in fire conditions (almost equal to the wall height/24) even for a fire exposure time of 90 min. Such a deflection was significantly higher than the maximum value recommended by design codes (about the wall height/30 for 120 min of fire exposure). Furthermore, other test results (full-field displacements in the 3 directions, residual deformed shape after the heating and cooling phases) may also provide additional useful experimental data for validating available or future models (which are usually based on some specific assumptions). In addition, the measurements of thermal-induced deformed shape of the wall are compared to those predicted by an analytical solution derived from a simplified one-dimensional (1D) beam model, as well as to those obtained from non-linear finite-element simulations (with 2D plate elements), in order to assess the practical applicability of these simulations. These comparisons show a reasonably good agreement between the experimental results and the predictions of these models, thereby providing a first validation of the latter.

A target recognition method for synthetic aperture radar (SAR) image based on complex bidimensional empirical mode decomposition (C-BEMD) is proposed. C-BEMD is used to decompose the original SAR image to obtain multilevel complex bidimensional intrinsic mode functions (BIMF), which reflect the two-dimensional time-frequency characteristics of the target. In the classification stage, the decomposed multilevel BIMFs are represented using the multitask sparse representation. Finally, the target category of the test sample is determined according to the reconstruction errors related to different training classes. In the experiment, the standard operating condition (SOC) and extended operating conditions (EOC) are designed based on the MSTAR dataset to test and verify the proposed method. The results confirm the effectiveness and robustness of the method.

Research of crack healing in concrete by autogenous healing, secondary crystallization admixtures or by microorganisms often uses large test samples. In order to develop and identify the most suitable method of crack healing, it is necessary to create many samples and examine them with different boundary conditions. This paper presents a newly designed small textile reinforced concrete test sample, and its production technology, which contains a large amount of various cracks. The small size allows for simultaneous testing of large numbers of test samples under identical conditions which helps to evaluate objectively the efficiency of various additives supporting the autogenous healing of concrete.

KNN is one of the simplest algorithm used for classification of new data point. In this paper, another strategy of classification method is proposed by combining Class Confidence Weighted (CCW) KNN and Weighted KNN (WKNN) for enhancing the performance of KNN. Inspired from the classic KNN, the main idea being classifying test sample using the most frequent neighbors tag. The optimized KNN uses weights to classify test sample. The new technique is tested on four standard datasets. Results show the significant increment in accuracy in comparison with conventional KNN strategy.

In modern hot rolling process, operating performance assessment is of great practical significance for guiding the production adjustment for operators. From the perspective of classification, operating performance assessment is a multi-class classification problem. Since support vector data description (SVDD) is a one-class classifier, conventional methods usually construct an independent SVDD model for each class, which ignores the correlation among different classes. The hyperspheres of different classes may not be isolated but overlapped. If a test sample exists in the overlapping region, how to determine which class it belongs to is a knotty problem. Moreover, conventional methods treat all samples equally, but in practice, the sample number of different classes can be imbalanced, which will affect the classification performance of SVDD. In this study, an operating performance assessment method based on weighted multi-sphere SVDD (WMSVDD) is proposed for solving the aforementioned issues. WMSVDD considers the interactions among different classes in a unified way, optimizes the hyperspheres of different classes globally, and introduces a weight coefficient to the model for eliminating the affects of uneven class sizes. Simulation results on a real hot rolling process illustrate the effectiveness of the proposed method comparing to the traditional multi-class SVDD.

This work aims to develop technical solutions that allow providing the specified parameters of the accuracy of the shape of parts in the cross-section during processing on a CNC machine. Experimental studies were performed on a screw-cutting lathe. An acoustic signal in the range from 6 to 12 kHz was used as a diagnostic sign to assess the wear of the cutting tool, since during preliminary studies, it was found that this range is most sensitive to changes in processing modes. Studies were performed at different values of wear of the cutting tool (estimated by the width of the wear chamfer). For estimating the life of a cutting tool, a neuro-fuzzy model has been developed. Using models of this class allows adjusting to specific conditions (machine, tool), and correctly evaluating the tool life. The model error for the test sample does not exceed 10%. The test results showed that using the proposed solutions makes it possible to increase the accuracy of the manufacturing of shut-off valve parts by 20-30%.

Most methods for sparse representation are designed to be used in the original space. However, their performance is not always satisfactory especially when training samples are limited. According to the previous studies, more information can be obtained from samples in the feature space than those in the original space. The authors propose a novel kernel difference maximisation-based sparse representation method, and its remarkable performance in face recognition is demonstrated by the experiments. The proposed method converts all the samples into the feature space, and a test sample can be denoted as a representation with all the training samples’ linear combinations. Besides, a novel solution scheme for sparse representation is utilised to obtain the [inline-formula] regularisation-based sparse solution. Finally, the classification of the test sample can be easily judged according to the representation result. The representation results of test samples from different classes obtained by their method are very different, making the classification of test samples easier. Besides, the proposed method is simpler than the related methods and does not require dictionary learning.

Background There is an urgent need for population-based studies on managing patients with pulmonary nodules. Research Question Is it possible to identify pulmonary nodules and associated characteristics using an automated method? Study Design and Methods We revised and refined an existing natural language processing (NLP) algorithm to identify radiology transcripts with pulmonary nodules and greatly expanded its functionality to identify the characteristics of the largest nodule, when present, including size, lobe, laterality, attenuation, calcification, and edge. We compared NLP results with a reference standard of manual transcript review in a random test sample of 200 radiology transcripts. We applied the final automated method to a larger cohort of patients who underwent chest CT scan in an integrated health care system from 2006 to 2016, and described their demographic and clinical characteristics. Results In the test sample, the NLP algorithm had very high sensitivity (98.6%; 95% CI, 95.0%-99.8%) and specificity (100%; 95% CI, 93.9%-100%) for identifying pulmonary nodules. For attenuation, edge, and calcification, the NLP algorithm achieved similar accuracies, and it correctly identified the diameter of the largest nodule in 135 of 141 cases (95.7%; 95% CI, 91.0%-98.4%). In the larger cohort, the NLP found 217,771 reports with nodules among 717,304 chest CT reports (30.4%). From 2006 to 2016, the number of reports with nodules increased by 150%, and the mean size of the largest nodule gradually decreased from 11 to 8.9 mm. Radiologists documented the laterality and lobe (90%-95%) more often than the attenuation, calcification, and edge characteristics (11%-14%). Interpretation The NLP algorithm identified pulmonary nodules and associated characteristics with high accuracy. In our community practice settings, the documentation of nodule characteristics is incomplete. Our results call for better documentation of nodule findings. The NLP algorithm can be used in population-based studies to identify pulmonary nodules, avoiding labor-intensive chart review.

Relative potency assays for biological therapeutics require statistical evaluation to demonstrate similarity between the dose–response curves of a reference standard and the test samples. We developed an equivalence testing approach that can be utilized for the complete potency assay life cycle, from early development until commercialization. This approach was based on the use of generic equivalence margins to enable equivalence testing at the beginning of assay development, when the body of assay-specific data is still very limited. Generic equivalence margins for equivalence testing of four-parameter logistic curve fits were established for bioassays and binding assays spanning a variety of designs, formats, and read-outs. We also established that equivalence testing using ratios of the reference standard and test sample was superior to equivalence testing using absolute differences. Based on a large body of historical data, generic equivalence margins were determined for the curve upper asymptote, slope, and dynamic range. Furthermore, we developed a road map to guide the implementation of generic or assay-specific margins to ensure the appropriate data analysis approach is being applied during the assay life cycle.

In this report, the results obtained from the laboratory tests conducted at LNEC, between February 2017 and October 2019, on concrete samples extracted from an existing laboratory-aged slab cast in October 2013, at the Electric Power Research Institute laboratory (EPRI) in Charlotte (North Carolina, USA), with the intent of determining the concrete residual free expansion potential, are presented and analysed.

This study, realized for EPRI in order to complete round-robin tests, aims at characterizing concrete cores of a concrete and the initial aggregates used for the casting.

This report describes the results of a petrographic investigation aimed at quantifying the degree of damage associated with alkali-silica reaction (ASR).

This report presents results of a round robin test in WG-1 of RILEM TC 259-ISR (ASR prognosis of deterioration and loss of serviceability in structures affected by alkali-silica reaction), performed at the research laboratory of Taiheiyo Consultant (THC) in Japan. This was planned to verify the effectiveness of proposed testing methods in the RILEM WG-1 documents, with the challenges below.

The Swiss method to determine the residual expansion of concrete from structures is based on the storage of cores in a container at 38 °C and 100 % relative humidity for half a year. First, the cores are conditioned at 20 °C for a week to reach water saturation. Afterwards, they are moved to the container. Length changes are recorded regularly. At the end of the test, the cores are dried at 20 °C and low relative humidity until they reach their initial mass before the start of conditioning. The length change during the storage in the container and the irreversible length change (length difference before conditioning and after drying at the end of the test) are used to assess the expansion kinetics of the concrete.

This document provides an example of use of the residual expansion test described in the testing method LPC nr. 44.

The results of the development of THz imaging system based on 140 GHz CW Clinotron tube and quasi-optical antenna have been presented and discussed. The preliminary tests were carried out for a single-frequency mode. The image restoration is performed by using the Wiener deconvolution algorithm showing a good agreement for the test sample.

The presented paper summarizes the main research findings on the impact of the environment concerning the durability and service life of building joint sealants. The focus is placed on sealed joints in fa&ccedil, ade applications, which can serve different purposes and can also be several meters long which often intensifies the stresses that the joint needs to withstand and therefore its service life can be significantly shortened. Different approaches, test sample geometries and high-performance sealants, were used in this context to determine the most critical aspects for the studied application sector. The research was divided into three phases where the joints were subjected to (I) artificial weathering in a laboratory environment, (II) real weathering in an external environment, and (III) weathering via a real application that was monitored for almost 4 years. The extensive research scope confirmed one commonly known presumption, that standardized artificial weathering/aging methods are not able, from a long-term perspective, to simulate the impact of a real environment. The most valuable results were obtained in the third phase of the research, where the monitoring of a real fa&ccedil, ade brought to light completely different conclusions. The joints exposed to the real environment were either completely deteriorated or showed signs of advanced aging.

Classification is one of the methods of the intelligent data analysis (data mining), which allows to study the existing data set and create a model for each class based on its data features. To increase the accuracy of classification, ensemble methods are used, including some of the most effective ensemble methods based on boosting and bagging. This study presents the results of a comparative analysis of machine learning methods for classifying the state of a computer system. The ensemble methods based on boosting and bagging were modified using a special procedure for selecting the main classification parameters: the number of trees (the number of learners), the number of branches (the number of splits or leaves nodes), and the learning speed (the learning rate). Modified methods were tested with the following initial data of the computer system functioning: loading of the central processor and memory, the amount of incoming and outgoing traffic (network utilization), the number of read / write operations to disk, the statistical data of the system events analysis (the number of operations with the system registry, file system, number of processes, etc.). It was found that the classification accuracy with the test sample was more than 99%.

Air-gap is one of the most common defects in the manufacturing and transportation of XLPE insulation, which might cause severe damage to power cables. XLPE insulation with air-gap defect would be degradation under long-term electrical stress. In acorrdance with actual defect, three-layer XLPE insulating sheet samples were designed and manufactured. Step-up voltage and pulse current method were carried out in the experiment. Afterwards, partial discharge signals and test samples at different development stages were collected. Then Kernel Pincipal Component Analysis (KPCA) was applied to reduce the characteristic parameters. Four development stages were divided based on the features by clustering: initial stage; developing stage; pre-breakdown stage and breakdown. Scanning electron microscope (SEM) and 3D surface profiler were employed to observe surface morphology and roughness of test samples at different discharge stages. The experimental results show that the results of are consistent with the observed discharge characteristics and trends in the air-gap discharge test. SEM result shows that the surface morphology of the test sample changes dramatically in the different discharge stages. Specifically, electrical tree and “Craters” began to appear on surface at the initial discharge; “3D meshes” and creepage appeared at the developing stage; “fractures” were discovered and molecular structure of insulation changed in the pre-breakdown stage. Roughness of insulation was also examined in this study. Above all, surface damage characteristics of XLPE insulation are strongly related to the development of air-gap discharge. It provides a experimental reference for a further study of the degradation mechanism of XLPE insulation by air-gap discharge.

The concept of Friction Stir Welding (FSW) technique was invented two decades ago, but its utilization has been restricted to low strength material because of the problems of tool wear, weld efficiency and sufficient heat required during the joining process in case of high strength material. Many secondary heat sources have been used for the addition of the heat to the joining process. In this paper, the study of the use of secondary heat sources to Friction Stir Welding has been used to join the high strength material like Mild Steel and Stainless Steel. Both MS and SS material have been joined with each other like MS to MS and MS to SS and the test sample has been tested and analyzed for the characterization of the material joined. The analysis has been done to resolve the problems rose during the joining of the high strength material and focused on the study on increase of the weld efficiency. Special focus on the variation of UTS and hardness has been done to find the effect of an addition of a secondary source of electric current on FSW for joining MS and SS material.

A comparative analysis of 18 popular speech databases used in the field of affective computing was carried out. A database containing unique audio recordings of Russian speech (words, phrases, and sentences), registered in natural experience of emotions by eyewitnesses and/or participants in adverse events under stress, has been developed. Voice recordings were taken from video materials of disasters and emergencies occurred on the territory of Russia in 2015-2019. Examination results of psychological and psychiatric reactions of 293 eyewitnesses and/or participants (119 men and 174 women, aged 15 to 65 years) to traumatic factors of adverse events are presented. In accordance with the development results, the following key conclusions have been made: in the future, the database will allow researchers to objectively assess the capabilities of existing methods and approaches for analyzing emotional speech, as well as to assess the need for new mathematical devices in speech signal processing; the voice database can be successfully used as training or test sample, necessary for effective training of systems for automatic recognition of stressful emotions in human-robot interaction.

Most of the real-world datasets suffer from the problem of imbalanced class representation, with some classes having more than sufficient samples, while some other classes are heavily underrepresented. The imbalance in the class distributions of the majority and minority samples renders conventional classifiers like the k-Nearest Neighbor (kNN) classifier ineffective due to the heavy bias toward the majority class. In this paper, we propose to counter the class-imbalance problem by data space transformation of the training set by distance metric learning prior to an enhanced classification phase. The classification phase comprises of partitioning the set of k-Nearest Neighbors of each transformed test sample into two clusters based on their distances from the two extreme members in the set. A majority voting of the training samples within the ‘Elite’ cluster that is closest to the transformed test sample indicates the label of the test sample. Our proposed method is called Data Space Transformation with Metric Learning and Elite k-Nearest Neighbor cluster formation (DST-ML-EkNN). Extensive experiments on benchmark datasets using a variety of metric learning methods, with comparisons to the state of the art, establish the supremacy of our learning approach for imbalanced datasets.

Handwritten digit recognition is a basic and important problem in computer vision. While it has been studied intensively, the newer datasets keep bring new challenges to existed solutions. A new Kannada-MNIST dataset is mainly digital images of the Kannada language, which contains 70,000 28×28 gray-scale sample images and 10 classes. It also contains a Dig-MNIST dataset, which is an out-of domain test sample dataset and a more challenging test dataset for recognition. In this paper, we evaluate the state-of-art deep learning models for Kannada-MNIST and improve their performance with data augmentation and transfer learning techniques. We manage to achieve a best accuracy of 90.27% for Dig-MNIST in the literature with Inception Model.

One of the frontiers in the field of biosensors is the ability to quantify specific target molecules with enough precision to count individual units in a test sample, and to observe the characteristics of individual biomolecular interactions. Technologies that enable observation of molecules with "digital precision" have applications for in vitro diagnostics with ultra-sensitive limits of detection, characterization of biomolecular binding kinetics with a greater degree of precision, and gaining deeper insights into biological processes through quantification of molecules in complex specimens that would otherwise be unobservable. In this review, we seek to capture the current state-of-the-art in the field of digital resolution biosensing. We describe the capabilities of commercially available technology platforms, as well as capabilities that have been described in published literature. We highlight approaches that utilize enzymatic amplification, nanoparticle tags, chemical tags, as well as label-free biosensing methods.

We have been developing P-channel Charge-Coupled Devices (CCDs) for the upcoming X-ray Astronomy Satellite XRISM, planned to be launched in 2021. While the basic design of the CCD camera (Soft X-ray Imager: SXI) is almost the same as that of the lost Hitomi (ASTRO-H) observatory, we are planning to reduce the "light leakages" that is one of the largest problems recognized in Hitomi data. We adopted a double-layer optical blocking layer on the XRISM CCDs and also added an extra aluminum layer on the backside of them. We develop a newly designed test sample CCD and irradiate it with optical light to evaluate the optical blocking performance. As a result, light leakages are effectively reduced compared with that of the Hitomi CCDs. We thus conclude that the issue is solved by the new design and that the XRISM CCDs satisfy the mission requirement for the SXI., 6 pages, 4 figures, accepted for publication in NIMA, proceedings of the 12th International "Hiroshima" Symposium on the Development and Application of Semiconductor Tracking Detector (HSTD12)

In order to meet the implementated labeling requirement, this study focused on the development of real-time quantitative polymerase chain reaction (real-time QPCR) method to detect genetically modified maize (GM-maize). Primers and probes specific for inserted genes in Event176, Bt11 (Syngenta company), MON810, GA21 (Monsanto company) and T25 GM-maize (Aventis company) were designed and used to conduct the real-time QPCR assays. A plasmid containing both transgene and internal control gene targets on the same molecules for each GM-maize event was also constructed as quantitative reference molecules. Further, constructed plasmids and test samples were applied to validate this quantitative system. Results showed that the slope of the standard curve generated by serial dilution of constructed plasmids was in the range of -3.31~-3.45 with a correlation of 0.99~1.0. Test samples spiked 1%, 2% and 5% GM-maize were quantitated by the method developed in the study. The mean values of 1%, 2% and 5% test sample were between 1.01%~1.23%, 2.00%~2.31% and 4.46%~5.55%, respectively. The standard deviation (S.D.) ranged from 0.05 to 0.46 with coefficient variance (C.V.) of 5%~15%. The limit of quantitation was 0.1% (w/w) for these GM-maize crops. In addition, proficiency test samples were analyzed by the method developed in this study and good results were obtained.

In this study Brute Force and Stemming algorithms are combined in the mashdar word search system. The purpose of this study is to facilitate the user in finding the basic words of a word along with its changes based on the review of basic words to mashdar. The data used in this study were obtained from 50 books of Tashrif by Sheikh Hasan bin Ahmad. The results of the study by combining brute force and stemming algorithms in mashdar search with a test sample of 15 words obtained an accuracy value of 78.9%, 86.6% precission, 84.6% recall and 66.6% specificity.

This paper surveys estimates of the transmission features of the novel coronavirus, and then proposes a model to address sample-selection bias in estimated determinants of infection. Containment assumptions of the infection forecasting models depend on assumed effects of policies and self-regulating behavior. In the commons dilemma of the pandemic, the perceived ‘low risks’ of unregulated marginal choices do not reflect the full social cost, implying non-pharmaceutical interventions (NPI) to reduce mortality can enhance social welfare. As more economic activity renews with liftings of restrictive NPI (RNPI), a critical question concerns the ability of milder NPI (MNPI) and voluntary precautions to mitigate the risk of greater infections and deaths while also limiting the pandemic’s economic damage and its social costs. Ineffective NPI could lead to continued COVID-19 waves and new types of crises, worsened expectations and delayed economic recoveries. From the central range of surveyed estimates of transmission and alternative herd-immunity-threshold estimates, a ‘worst-case’ virus guidepost suggests eventual deaths of around 25 to 41 million worldwide and 1.1 to 1.7 million in the U.S. needed to reach herd immunity with no vaccine or treatment. The most optimistic study surveyed (theoretical model from a non-reviewed preprint study) combined with the low end of the range of the estimated mortality rate suggests 6 to 9 million deaths worldwide and 250 to 370 thousand in the U.S. to reach herd immunity. Successes in the mix of NPI, treatments, and vaccine can limit the eventual global death toll of the virus. Improved estimation models for forecasting and decision making may assist in better targeting the local timings and mix of NPI. Diagnostic tests for the virus have been largely limited to symptomatic cases, causing possible sample selection bias. A recursive bivariate probit model of infection and testing is proposed along with several possible applications from cross-section or panel-data estimation. Multiple potential explanatory variables, data sources, and estimation needs are specified and discussed.