Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature selection for software defect prediction using an improved �re�y algorithm

As all we know, the performance of the data-based learning models heavily depend on the quality of data that feature selection (FS) method is used to reduce the redundant features and irrelevant features to improve the classi-ﬁcation performance. In this research, a feature selection method using an improved ﬁreﬂy algorithm (FA) is proposed to address the ”curse-of-dimensionality” problem in software defect prediction (SDP). In the proposed method, where the simulated annealing (SA) approach is used to avoid falling into local traps, a multi-group strategy is employed to obtain a promising optimal solution to the optimization problems, called the multi-group simulated annealing ﬁre-ﬂy algorithm (MSAFA). To evaluate the performance of M-SAFA, excessive experiments have been conducted on a total of 28 optimization benchmark problems (CEC2013) and 6 class-imbalanced datasets selected from the NASA repository. In the comparison with other state-of-the-art swarm-based intelligence algorithms, MSAFA outperforms them to provide the optimal solutions to most optimization problems and SDP models. In short, the proposed feature selection method can obtain the promising solution to support data-based classiﬁed models in SDP.
The boundary element method for 3-D dc resistivity modeling in layered earth

The integral equation for dc resistivity modeling of 3‐D inhomogeneous bodies buried in a layered earth is derived by using Green's theorem. The main features of this method are (1) the layers above and below the 3‐D object can be included, (2) multiple subsurface inhomogeneous bodies can be embedded in the different layers, and (3) the boundary element method (BEM) is used to solve the integral equation using triangular surface elements. Linear variation of the electrical properties is assumed within each element. The potential on the ground surface is obtained by solving the linear equation system with Gaussian elimination.Model calculations demonstrate that the results obtained by this method compare well with the analytical solution of a sphere in a uniform half‐space and the asymptotic behavior for the solution of a buried body beneath a surficial layer as the layer resistivity approaches that of the half‐space. A comparison of responses over elongate 3‐D bodies with responses over 2‐D bodies of iden.
Learning-Based Rule-Extraction From Support Vector Machines: Performance On Benchmark Data Sets

Over the last decade, rule-extraction from neural networks (ANN) techniques have been developed to explain how classification and regression are realised by the ANN. Yet, this is not the case for support vector machines (SVMs) which also demonstrate an inability to explain the process by which a learning result was reached and why a decision is being made. Rule-extraction from SVMs is important, especially for applications such as medical diagnosis. In this paper, an approach for learning-based rule-extraction from support vector machines is outlined, including an evaluation of the quality of the extracted rules in terms of fidelity, accuracy, consistency and comprehensibility. In addition, the rules are verified by use of knowledge from the problem domains as well as other classification techniques to assure correctness and validity.
Clinical XLNet-based End-to-End Knowledge Discovery on Clinical Text Data using Natural Language Processing

A modern framework for assessing patient histories and conducting clinical research has been developed as the number of clinical narratives evolves. To discover the knowledge from such clinical narratives, clinical entity recognition and relation extraction tasks were performed subsequently in existing approaches, which resulted in error propagation. Therefore, a novel end-to-end clinical knowledge discovery strategy has been proposed in this paper. The clinical XLNet was used as a base model for handling the discrepancy issue. To predict the dependent clinical relation association, the multinomial Naïve Bayes probability function has been incorporated. In order to improve the performance of the proposed strategy, it takes into account entity pairs presented consecutively through the multi-head attention layer. Tests have been conducted using the N2C2 corpus, and the proposed methodology achieves a greater than 20% improvement in accuracy over existing neural network-based and transformer-based methods.
Vertical structure of tropical cyclones at onset of the rapid intensification in the HWRF model

In this study, the tropical cyclone structure at the onset of rapid intensification (RI) is examined using the cloud‐permitting version of the Hurricane Weather Research and Forecast (HWRF) model. Idealized experiments with different vortex initial vertical structures in different environments show that the HWRF model vortex possesses a specific constraint in the dynamical and thermodynamic structure at the RI onset including (i) a warm anomaly of 1–3°K, (ii) a moist column with relative humidity > 90% within the storm central region, and (iii) low‐level tangential flow ≥12 m s−1. Regardless of vortex structures or environment conditions applied in this study, model vortex does not intensify if the above constraint is not established. Such a requirement in the model moisture and dynamical structure at the RI onset can explain why the RI onset is much delayed in dry experiments as compared to the onset in moist experiments.
A Spectral Learning Based Model to Evaluate Semantic Textual Similarity

Semantic Textual Similarity (STS) is a task in NLP that compares two sentences in a sentence-pair and scores the relationship between them using the degree of semantic equivalence. It has wide applicability in various ﬁelds. Consequently, the research around the task is constantly evolving. The demand for new as well as improved meth-ods is endless. Numerous methods have been proposed that largely belong to either unsupervised or supervised learning approaches. The model proposed here is fairly simple and provides a fresh take on this classiﬁcation problem using spectral learning. The model does not engage a large labeled corpus or lexical database like most STS supervised and unsupervised methods. Although, supervised STS methods achieve an accuracy that outperforms humans in some cases, but are often held back due to a lack of interpretation of the features instrumental in molding the decision-making process. The proposed model on the other hand generates features (latent knowledge) that are easy to ascertain and have a mathematical foundation. Given a sentence pair, the work focuses on ﬁnding latent states and variables from each sentence and performs classiﬁcation by generating a similarity score. The latent variables are a result of projections learned by performing Canonical Correlation Analysis (CCA) amongst the sentence pair. To perform matching and determine the similarity score, Cosine similarity and Word Mover’s Distance (WMD) are employed. The performance of the proposed model does exhibit an improvement over various sophisticated supervised techniques such as LSTM and BiLSTM.
Similarity Mapping of National Drug Code Formulary Systems Between Nations

Background: A fundamental problem in international health care research is comparing national prescribing practices and drug formularies between nations that use inconsistent drug naming, dosages, and packaging sizes. However, there is currently a lack of quality mapping between diﬀerent ontology systems. This study aims to evaluate the eﬃciency of automated mapping by drug names using similarity measure algorithms. The motivating example was cross-mapping between the British National Formulary (BNF) and the Anatomical Therapeutic Chemical (ATC) drug classiﬁcation systems. Method: We used edit-based, token-based, and hybrid similarity algorithms to match the drug’s active pharmaceutical ingredients between classiﬁcation systems. Results: A variety of similarity measures were also compared. We found a substantial trade-oﬀ between optimizing matching and false positives for the cross-mapping of drug datasets. Conclusion: In general, edit-based algorithms with a pre-processing step to remove stop words had a consistently good performance by recall, precision, and F1 scoring.
Power Decoding Reed – Solomon Codes Up to the Johnson Radius

Power decoding, or “decoding using virtual interleaving” is a technique for decoding Reed–Solomon codes up to the Sudan radius. Since the method’s inception, it has been an open question if it possible to incorporate “multiplicities”, the parameter allowing the Guruswami–Sudan algorithm to decode up to the Johnson radius. In this paper we show how this can be done, and describe how to efficiently solve the resulting key equations. We investigate its failure behaviour theoretically as well as giving simulation results, and we show how the method can be made practically faster using the re-encoding technique or a syndrome formulation.
Revision of the position of the Ordovician–Silurian boundary in southern Ontario: regional chronostratigraphic implications of δ13C chemostratigraphy of the Manitoulin Formation and associated strata

delta C-13 values of 142 samples from the Manitoulin Formation and subjacent strata collected from 14 exposures and two drill-cores on Manitoulin Island, Bruce Peninsula, and the region south of Georgian Bay suggest that the Manitoulin Formation is latest Ordovician (Hirnantian) rather than earliest Silurian in age. A delta C-13 excursion identified as the Hirnantian isotope carbon excursion (HICE), which has a magnitude of nearly 2.5 parts per thousand above baseline values, is present in an interval from the upper Queenston Formation to the lower to middle part of the Manitoulin Formation in most of Bruce Peninsula and in the area south of Georgian Bay, whereas on Manitoulin Island the RICE appears to be absent. This indicates that a significant part of the Manitoulin Formation is older on the Bruce Peninsula and in its adjacent region than on Manitoulin Island. The chemostratigraphically based conclusions are consistent with biostratigraphic data from conodonts and brachiopods. The Hirnantian delta C-13 curve from Anticosti Island, Quebec is closely similar to those of southern Ontario. Traditionally, the Ordovician-Silurian boundary has been placed at the base of the Manitoulin Formation, but the new results suggest that it is more likely to be at, or near, the base of the overlying Cabot Head Formation. These new results have major implications For the interpretation of the geologic history and marine depositional patterns of the latest Ordovician of a large part of the North American Midcontinent.
The Combination of Gravity and Satellite Altimetry Data for Determining the Geoid Surface.

Abstract : A recent report by Brown (1973) initiated a new approach to satellite altimetry adjustment, in the sense that highly accurate reference orbits are no longer required. Computer simulations indicated that in this way, global oceanic geoid accurate to within 1 to 2 meters rms may be determined from the reduction of GEOS-C satellite observations. However, worthwhile geoidal accuracies cannot be obtained over land masses from the reduction of satellite altimetry alone.
A cnidarian of actinian-grade from the Ediacaran Pound Subgroup, South Australia

Inaria karli gen. et sp. nov., a moderate sized lobate fossil from the Late Precambrian Ediacara assemblage, occurs in a number of modes of preservation involving composite moulding of a compressed, thin-walled bulbous organism. Numerous specimens occur on the soles of sandstones interbedded with relatively thick claystones within the Ediacara Member of the Rawnsley Quartzite (Pound Subgroup) in the central Flinders Ranges. Reconstruction suggests a sack-shaped anemone with an erect pharynx. It occupied a benthic position on the shelf below fair weather wave base. Comparisons with Ediacaran and Early Cambrian trace fossil and body fossil impressions with radial symmetry suggest that Inaria was a non burrowing precursor to actinian infauna.
The response of carbon dioxide exchange to manipulations of Sphagnum water content in an ombrotrophic bog

Sphagnum mosses contribute to the long‐term carbon sequestration of many northern peatlands. Variability in both Sphagnum and peatland CO2 exchange is strongly influenced by water availability, but there is limited work examining the links between these relationships, particularly in a field setting. In this study, the effects of varying moss water content (WC), through precipitation exclusion (PE) and water addition (WA) treatments, on net ecosystem CO2 exchange (NEE) and component fluxes were assessed during a single growing season at Mer Bleue bog, a temperate ombrotrophic peatland. CO2 exchange by Sphagnum, vascular plants, and the total ecosystem were measured in situ using chambers. On average, the hummock species of Sphagnum dominant at the site maintained WCs within the optimal range for photosynthesis in all treatments and no treatment effect on CO2 exchange was observed. However, in a few samples associated with below optimal WCs, Sphagnum photosynthesis was reduced. Water table (WT) depth and direct precipitation were both important controls on Sphagnum WC, suggesting that frequent small rain events may be important in maintaining Sphagnum WC. This study only explored one variable associated with drought conditions (i.e. water availability through intercepted precipitation). Consequently, further study is necessary to assess the relative importance of deep WT positions and high temperatures on moss WC. Nevertheless, the results of this study suggest that decreased Sphagnum photosynthesis has the potential to influence peatland CO2 uptake at Mer Bleue in drought years, but only if moss WC is reduced below the optimal range. Copyright © 2010 John Wiley & Sons, Ltd.
Combination of FTA and FMEA methods to improve efficiency in the manufacturing company

Toroidal is an inductor with an O-ring core. In 2021 there was an increase in the percentage of toroidal disability, namely the highest percentage of disability at 2.78%, while in 2020 the highest percentage of disability was only 1.33%. Therefore, companies need to make improvements to the quality of their products to prevent a continuous rise in the percentage of defects over time. This study aims to propose an enhancement to reduce/prevent defects in toroidal inductor products. The method used is the Fault Tree Analysis (FTA) and Failure Mode and Effect Analysis (FMEA). The FMEA method is utilized to reduce flaws that occur by considering the value of the risk priority number (RPN). The FTA method is used to identify possible defects by applying analysis of the fault tree description. The results of this study are that improvements should be focused on the failure mode of the wound wire because it gets the highest RPN value of 216 with the cause being less focused workers, while the lowest RPN value is on the red pin failure mode of 56 with the cause of no sandpaper changing schedule. From the results of the study, several suggestions for improvements were made in the form of increasing supervision for workers, increasing rest periods, conducting training, installing display limits for stripping, repairing stripping machines, replacing vise, repairing worktables, and making a schedule for sandpaper replacement prevents the defect. Companies must make improvements to human flows and ensure tools and materials are in optimal condition.
DyGS: A Dynamic Gene Searching Algorithm for Cancer Detection

Cancer is a lethal disease and ranks as the world’s second-most prevalent cause of death. So far, biopsy is the most common method conducted to determine the progression of tumors. However, this traditional technique is invasive and cannot be used repeatedly. On the other hand, liquid biopsies, also known as blood-sample tests, have become more and more promising. One critical step in liquid biopsy is to create an effective gene panel that covers the maximum number of cancer cases possible. The hypotheses of this study are that both common genes and genes that are mutated in specific cancer types will be present in the selected gene lists for the 12 cancer types, and some types may have longer lists than others. In this study, we developed a novel dynamic gene-searching algorithm called Dynamic Gene Search (DyGS) to create a gene panel for each of the 12 cancers with the highest annual incidence and death rate. This algorithm generated lists of genes for the 12 cancer types, ranging from 12 to 153 genes, with a median of 47 genes. Notably, many of the genes included in the panel can be targeted by various drugs. Therefore, the gene panels designed by the DyGS algorithm can be used as actionable drug targets for cancer treatment as well as biomarkers in liquid biopsy to help identify the early stages of cancer.
Evaluating Landsat Thematic Mapper spectral indices for estimating burn severity of the 2007 Peloponnese wildfires in Greece.

A vast area (more than 100 000 ha) of forest, shrubs and agricultural land burned on the Peloponnese peninsula in Greece during the 2007 summer. Three pre- and post-fire differenced Landsat Thematic Mapper (TM)-derived spectral indices were correlated with field data of burn severity for these devastating fires. These spectral indices were the Normalised Difference Vegetation Index (NDVI), the Normalised Difference Moisture Index (NDMI) and the Normalised Burn Ratio (NBR). The field data consist of 160 Geo Composite Burn Index (GeoCBI) plots. In addition, indices were evaluated in terms of optimality. The optimality statistic is a measure for the index’s sensitivity to fire-induced vegetation depletion. Results show that the GeoCBI–dNBR (differenced NBR) approach yields a moderately high R2 = 0.65 whereas the correlation between field data and the differenced NDMI (dNDMI) and the differenced NDVI (dNDVI) was clearly lower (respectively R2 = 0.50 and R2 = 0.46). The dNBR also outperformed the dNDMI and dNDVI in terms of optimality. The resulting median dNBR optimality equalled 0.51 whereas the median dNDMI and dNDVI optimality values were respectively 0.50 and 0.40 (differences significant for P < 0.001). However, inaccuracies observed in the spectral indices approach indicate that there is room for improvement. This could imply improved preprocessing, revised index design or alternative methods.
An assessment of viscoelastic nite-difference models applied to the Brazilian pre-salt

Abstract Viscoelastic waves are modeled using a 3D time-domain ﬁnite-diﬀerence scheme with three classical rheological models – Maxwell (M), Kelvin-Voigt (KV), Standard Linear Solid (SLS) – each of them with a single relaxation mechanism. Attenuation and dispersion eﬀects are introduced in terms of the quality factor Q for a certain angular frequency ω . Initially, seismic attenuation of the 2D poststack seismic data from Brazilian pre-salt is evaluated through a frequency analysis. After that, numerical results are validated with analytical solutions based on a homogeneous model with constant seismic properties. The behavior of each rheological model are discussed based on phase, wave amplitude and spectrum. A simpliﬁed multilayered geological model is proposed considering regions of the poststack seismic data from the Brazilian pre-salt, B´uzios ﬁeld. Synthetic data are then generated for each rheological model considering two approaches in terms of Q distribution: a constant Q and a varying Q per layer. Both waveform and spectra are extracted from seismograms and compared with selected regions of B´uzios poststack data. The amplitude spectra conﬁrmed that the shallowest
Stock Price Prediction Using Machine Learning algorithm with Web Interface (GUI)

Predicting the stock market trend is difficult due to the stock market’s high level of uncertainty. Many approaches for stock price and market prediction have been developed in the past. Different algorithms such as Long Short-Term Memory (LSTM), Support Vector Machine (SVM), Linear regression, and Decision trees are utilized in this study to forecast the future closing price of stocks for a specific firm. In this paper, the accuracy of these mentioned algorithms is also being compared. A seven-day study is also performed, illustrating the actual and anticipated closing prices for two companies using different algorithms. The outcome of the previous forecast is compared to the other algorithms, and a final algorithm is chosen based on it. A final model is created by taking many technical parameters in consideration in order to have more precise anticipated the price. Based on the final model, a 10-days examination of the actual and anticipated closing prices for ten different firms is also performed. Finally, A web application-based Graphical User Interface (GUI) was built using the Stream-lit library. The GUI includes features such as a forecast graph, a graph for historical data, and a company selection as well as the start and the end date.
Identification of a widespread Kamchatkan tephra: A middle Pleistocene tie‐point between Arctic and Pacific paleoclimatic records

Very few age controls exist for Quaternary deposits over the vast territory of the East Russian Arctic, which hampers dating of major environmental changes in this area and prevents their correlation to climatic changes in the Arctic and Pacific marine domains. We report a newly identified ~177 ka old Rauchua tephra, which has been dispersed over an area of >1,500,000 km2 and directly links terrestrial paleoenvironmental archives from Arctic Siberia with marine cores in the northwest Pacific, thus permitting their synchronization and dating. The Rauchua tephra can help to identify deposits formed in terrestrial and marine environments during the oxygen isotope stage 6.5 warming event. Chemical composition of volcanic glass from the Rauchua tephra points to its island‐arc origin, while its spatial distribution singles out the Kamchatka volcanic arc as a source. The Rauchua tephra represents a previously unknown, large (magnitude >6.5) explosive eruption from the Kamchatka volcanic arc.
The metamorphic history of the Isua Greenstone Belt, West Greenland

Abstract New geological investigations in the c. 3.7–3.8 Ga Isua Greenstone Belt in West Greenland have revealed that the belt comprises a number of separate structural domains. Five such domains have been identified on the basis of lithological and structural differences. This study uses the morphology and compositional zoning of garnet porphyroblasts in pelites to investigate the extent to which the various domains within the greenstone belt preserve contrasting deformational and metamorphic histories. Up to three episodes of garnet growth have been identified in a single domain and significant differences in garnet growth history are noted between domains. A distinction is drawn between the relatively simple metamorphic history of a low-strain zone in the NE of the greenstone belt and other domains where more complex histories are preserved. Combining this result with existing geochronology suggests that in the south and west, the greenstone belt was metamorphosed twice, at c. 3.74 Ga and at c. 2.8 Ga, whereas in the NE there was a single event at 3.69 Ga. Preliminary garnet-rim thermometry indicates that some rocks experienced an early metamorphism in which temperatures exceeded 610°C. Kyanite is thought to have been in equilibrium with these assemblages, indicating pressures of at least 6 kbar. A later prograde metamorphic event shows a temperature rise from 480 to 550°C. The high pressures indicate a crustal thickness of at least 20 km at 3.7 Ga.