Numerous Features Research Articles

Modeling the Colatina lineament using geophysical methods: Insights into the geology of ES, Brazil

The study of the primary dikes in a given region is essential for comprehending its geological evolution. This study focuses on the offshore region in the state of Espírito Santo (ES), which encompasses one of the major oil sedimentary basins in the country, namely the Espírito Santo Basin. The study area is considered structurally complex due to the presence of numerous structural features, and the present work focuses on the Colatina Lineament. Despite several decades of oil and rock prospecting studies, the lateral and depth boundaries of the lineament are still unknown. Therefore, this study aims to utilize geophysical methods, including magnetic.and gravity, obtained from aerial and terrestrial surveys, respectively, to characterize the Colatina Lineament structurally. The study generated thematic maps using highlight filters to investigate the structural importance of the zone of influence. Additionally, 2.5D forward modeling was performed from gravity data, revealing numerous geological structures generated by crustal movements. To visualize the geological encompassed by the Colatina Lineament, a 3D inversion of the magnetic data was performed. The outcomes facilitated the visualization and mapping of the dike bundle and confirmed the impact of tectonic processes in the region.

Creating ad hoc graphical representations of number

The ability to communicate about exact number is critical to many modern human practices spanning science, industry, and politics. Although some early numeral systems used 1-to-1 correspondence (e.g., ‘IIII' to represent 4), most systems provide compact representations via more arbitrary conventions (e.g., ‘7’ and ‘VII'). When people are unable to rely on conventional numerals, however, what strategies do they initially use to communicate number? Across three experiments, participants used pictures to communicate about visual arrays of objects containing 1–16 items, either by producing freehand drawings or combining sets of visual tokens. We analyzed how the pictures they produced varied as a function of communicative need (Experiment 1), spatial regularities in the arrays (Experiment 2), and visual properties of tokens (Experiment 3). In Experiment 1, we found that participants often expressed number in the form of 1-to-1 representations, but sometimes also exploited the configuration of sets. In Experiment 2, this strategy of using configural cues was exaggerated when sets were especially large, and when the cues were predictably correlated with number. Finally, in Experiment 3, participants readily adopted salient numerical features of objects (e.g., four-leaf clover) and generally combined them in a cumulative-additive manner. Taken together, these findings corroborate historical evidence that humans exploit correlates of number in the external environment – such as shape, configural cues, or 1-to-1 correspondence – as the basis for innovating more abstract number representations.

Understanding the relationship between reviews, search and sales: a study of the Indian car market

PurposeWhile there has been extensive research on understanding the effects of online reviews on product sales, there is not enough investigation of the inter-relationships between online reviews, online search and product sales. The study attempts to address this gap in the context of the Indian car market.Design/methodology/approachThe research uses text mining and considers six important review features volume, valence, length, deviation of valence, sentiment and readability within the heuristic and systematic model of information processing. Panel data regression is used along with mediation analysis to study the inter-relationships between features of reviews, online search and sales.FindingsThe study finds that numerical heuristic features significantly affect sales and online search, numerical systematic feature affects sales and the textual heuristic and systematic features do not affect sales or online search in the Indian car market. Further, online search mediates the association between features of reviews and sales of cars.Research limitations/implicationsAlthough only car sales data from India is considered in this research, similar relationships between review features, online search and sales could exist for the car market of other countries as well.Originality/valueThis research uncovers the unique role of online search as a mediator between review features and sales, whereas prior literature has considered review features and online search as independent variables that affect sales.

Design of Experiments Technique Applied to Artificial Neural Network Models for FPSO Mooring System Analysis

Online monitoring of mooring system response for the FPSO platform in any operational condition is so far challenging for machine learning (ML). This paper presents a new dynamic NARX ANN model for time series of mooring tension and a static MLP model for the offset chart prediction of a taut-leg moored FPSO with different working scenarios. A novel method for supervised feature selection of the dataset was applied to determine the most influential design features. Additionally, a design of experiments (DOE) technique was implemented for test matrix creation, simulation, database generation, and supervised selection characteristics in ML. The DOE analysis revealed that the mooring configuration, platform loading condition, and environmental loads alter the platform’s six-degree-of-freedom motion response patterns. These input data were used to predict the mooring tension and the offset chart of the floater. The results include the fair values of statistical error for mooring tension (R2 = 0.8–0.98, E ≈ 1.3–5.7%, RMSE ≈ 6–66 kN) and platform offset (E ≈ 0.1–1 m) prediction when testing the trained models with unseen data representing new operational conditions. Faster convergence can be achieved by adding non-numeric (string) input values to dataset numeric features. Supervised feature selection of the dataset is a step forward in ML to improve prediction accuracy.

Mapping landscape ecological patterns using numeric and categorical maps.

The reciprocal relationships between ecological process and landscape pattern are fundamental to landscape ecology. Landscape ecologists traditionally use raster maps portraying classified features such as land use or land cover categories, and metrics suggested by the patch-corridor-matrix conceptual model of pattern. Less attention has been given to the landscape gradient conceptual model and raster maps portraying numeric features such as greenness or percent vegetation cover. We introduce the open-source tool GraySpatCon to calculate and map a variety of landscape pattern metrics from both conceptual models using either categorical or numeric maps. The 51 metrics, drawn mostly from the landscape ecology and image processing literatures, are calculated from the frequencies of input pixel values and/or the pixel value adjacencies in an analysis region. GraySpatCon conducts either a moving window analysis which produces a continuous map of a pattern metric, or a global analysis which produces a single metric value. We describe an implementation in the GuidosToolbox desktop application which allows novice users to interactively explore GraySpatCon functionality. In the R desktop environment, we demonstrate several metrics using an example map of percent tree cover and illustrate a multi-scale moving window analysis to identify scale domains. Comparisons of computational efficiency indicate a substantial GraySpatCon advantage over related software in the R environment.

Driving Environment Inference from POI of Navigation Map: Fuzzy Logic and Machine Learning Approaches.

To adapt vehicle control and plan strategies in a predictive manner, it is usually desired to know the context of a driving environment. This paper aims at efficiently inferring the following five driving environments around vehicle's vicinity: shopping zone, tourist zone, public station, motor service area, and security zone, whose existences are not necessarily mutually exclusive. To achieve that, we utilize the Point of Interest (POI) data from a navigation map as the semantic clue, and solve the inference task as a multilabel classification problem. Specifically, we first extract all relevant POI objects from a map, then transform these discrete POI objects into numerical POI features. Based on these POI features, we finally predict the occurrence of each driving environment via an inference engine. To calculate representative POI features, a statistical approach is introduced. To composite an inference engine, three inference systems are investigated: fuzzy inference system (FIS), support vector machine (SVM), and multilayer perceptron (MLP). In total, we implement 11 variants of inference engine following two inference strategies: independent and unified inference strategies, and conduct comprehensive evaluation on a manually collected dataset. The result shows that the proposed inference framework generalizes well on different inference systems, where the best overall F1 score 0.8699 is achieved by the MLP-based inference engine following the unified inference strategy, along with the fastest inference time of 0.0002 millisecond per sample. Hence, the generalization ability and efficiency of the proposed inference framework are proved.

AER-HYBRITECH: Averaging Ensemble Regression with Hybrid Encoding and Enhanced Feature Selection Technique for Predictive Maintenance

Predictive maintenance is critical to modern industrial operations, preventing unexpected equipment failures and minimizing downtime. Existing methods often encounter challenges related to data preprocessing, missing data imputation, and feature selection. This paper presents "AER-HYBRITECH," a novel approach that addresses these challenges and enhances the predictive maintenance process. Traditional methods overlook the intricate relationships within the data, resulting in suboptimal predictive performance. To bridge this gap, the proposed AER-HYBRITECH algorithm is introduced. AER-HYBRITECH stands out in several ways. Firstly, it utilizes a hybrid encoding technique that converts categorical data into a more informative numerical representation by incorporating the average values of label-encoded data and its frequency, leading to improved feature utilization. Furthermore, it introduces the AER-MDI (Averaging Ensemble Regression-based Missing Data Imputation) technique, which combines M5P, REPTree, and linear regression models to impute missing data, ensuring a more complete dataset. The algorithm also implements Min-Max normalization to scale numeric features, making them compatible for further analysis. One of the key innovations of AER-HYBRITECH is its enhanced hybrid feature selection (EHFS) approach. The AER-HYBRITECH algorithm transforms and preprocesses the data and ensures that predictive maintenance models are built on a solid foundation, resulting in more accurate predictions and reduced maintenance costs.

Continuity-sets of pullback random attractors for discrete porous media equations with colored noise

For a random non-autonomous porous media lattice system driven by nonlinear colored noise, we prove the unique existence and local compactness of a pullback random attractor. We then mainly study the continuity-set (the set of all points of continuity) of the pullback random attractor on the time-sample plane with respect to the Hausdorff metric. With some calculations, we find that the continuity-set has four geometrical numerical features:(1) The continuity-set is residual and dense in the time-sample plane.(2) The continuity-set is composed of infinitely many diagonal rays (with slope one).(3) The continuity-set is dense in any non-diagonal line (with an inclined angle not being 45∘).(4) Restrictions of the continuity-set on diamonds are composed of parallel line segments with increasing numbers.

Research on a hierarchical feature-based contour extraction method for spatial complex truss-like structures in aerial images

Spatial truss-like structures are three-dimensional frame structures consisting of a series of linear members and nodes, which are widely used in the power engineering. UAVs and various land-air robots are widely used in power inspection to reduce the reliance on manual labor. Extracting the contours of such objects (represented in the form of points and line segments) in aerial images will further improve the efficiency of power inspections. Because existing methods struggle to deal with the complex background interference in aerial images, an innovative contour extraction method based on hierarchical features is proposed in this paper, which uses two artificial neural networks to extract image-wise features and patch-wise features respectively and link them with patch division. The IoU of the method can reach 83.4%, which is improved by 5.4% and 50.8% compared to the lightweight semantic segmentation network and HED-based method, respectively. Meanwhile the Polygons and line segments measurement (PoLis) outperforms the other four types of methods compared with it by at least 39.1%, which overcomes the drawbacks of the other methods that have poor contour extraction ability in complex backgrounds and aerial images with numerous feature. Meanwhile, the running time of this method is 85 ms. The proposed method overcomes the shortcomings of existing contour extraction methods, improves the effect of contour extraction in complex backgrounds in power inspection scenarios, facilitating the improvement of the intelligence level of inspection robots, which in turn promotes the automation level in power engineering.

Numerical study of second-grade fuzzy hybrid nanofluid flow over the exponentially permeable stretching/shrinking surface

The study of hybrid nanoliquids can aid in developing numerous advanced features that facilitate heat transmission, such as pharmaceutical processes, hybrid-powered engines, microelectronics, engine cooling, and domestic refrigerators. In the current study, a mathematical model is designed to elaborate the physical inception of an unsteady second-grade hybrid nanofluid with Al2O3−Cu/SA, a combination concentrated over the permeable exponentially heated stretching/shrinking sheet under hydromagnetic, heat source/sink, and viscous dissipation implications. The set of similarity transforms is used to convert underlying partial differential equations into the system of ordinary differential equations. The well-known homotopy analysis method is applied to tackle the formulated differential system in the MATHEMATICA program, which can obtain non-uniqueness outcomes. The imprecision of nanofluid and hybrid nanofluid volume fractions was modeled as a triangular fuzzy number [0%, 5%, 10%] for comparison. The double parametric approach was applied to deal with the fuzziness of the associated fuzzy parameters. The nonlinear ordinary differential equations are converted into fuzzy differential equations, and the homotopy analysis method is used for the fuzzy solution. In terms of code validity, our results are matched to previous findings. The features of several parameters against the velocity, surface-friction coefficient, heat transfer, and Nusselt number are described via graphs. Furthermore, the nanoparticle volume fraction magnifies the fluid temperature and retards the flow profile throughout the domain, according to our findings. Thermal profiles increase with progress in the heat source, nanoparticles volumetric fractions, viscous dissipation, and nonlinear thermal radiation. The percentage increase in the drag force and heat transfer rate are 15.18 and 5.54 when the magnetic parameter takes input in the range 0.1 ≤ M ≤ 0.3 and nanoparticle volume fraction inputs 0.01 ≤ ϕ1 ≤ 0.15. From our observation, the hybrid nanofluid displays the maximum heat transfer compared to nanofluids. This important contribution will support industrial growth, particularly in the processing and manufacturing sectors.

Observation of weak ferromagnetism in the antiferromagnetic host and large electronic heat capacity in β-Mn-type structured Mn3Al alloy

Numerous interesting features of antiferromagnets open up the possibility of designing new antiferromagnetic materials that can be useful for spintronic device applications. Here we present the structural, magnetic, thermal and electrical transport properties of polycrystalline Mn3Al alloys prepared by arc melting. Rietveld refinement of the room temperature X-Ray diffraction patterns reveals that the arc-melted Mn3Al crystallizes in a β Mn phase (space group 213). Detailed analysis of the magnetic data suggests for a dominant antiferromagnetic ordering. However, an in-homogenous magnetic disorder is observed in the system having a weak ferromagnetic phase in the antiferromagnetic host. In spite of the strong spin fluctuations in this system, the signature of spin glass-like nature in these alloys are not observed as probed by the frequency-dependent AC-Susceptibility measurements. Furthermore, the heat capacity was insensitive to the applied field of 3 Tesla with a negligible magnetic contribution suggesting a large electronic contribution to the heat capacity in these alloys. This work provides a useful insight into the tunable physical properties of the Heusler alloys for exploring antiferromagnetic based spintronic applications.

Developing a Staging Scheme for Essential Tremor: A Discussion of Organizing Principles.

Essential tremor (ET) is a chronic, progressive neurological disease that may negatively affect patients' lives. While there has been considerable progress in ET research, some fundamental issues remain unaddressed. One such issue is disease staging. Staging schemes have inherent value and are part of the dialogue that clinicians have with other movement disorders patients. We highlight the value of and challenges with developing a staging system for ET and organize a discussion around the potential steps in developing such a system. Diseases for which there are staging schemes generally have a number of shared characteristics. ET has numerous features that would lend themselves to a staging scheme: emerging evidence supporting the existence of a premotor phase of disease, insidious onset, progressive worsening of arm tremor, spread of tremor to other body regions, the observation that patients seem to be at increased risk for other conditions within the same organ (i.e., emergence of Parkinson's disease and Alzheimer's disease in excessive numbers of ET patients), pathological changes in the cerebellum whose evolution can be ordered from (i) those that compromise the physical integrity and physiological function of Purkinje cells, (ii) subsequent changes that are reparative and regenerative, and (iii) eventual cell death. Challenges to formulating a staging scheme are the absence of both a biological marker and an "end stage" of disease. The sum of combined evidence suggests that a staging scheme would be of value. We provide initial thoughts as to how to begin to structure such a staging scheme.

Performance of Machine Learning Models in Predicting Sentiments of Post-Covid Patients

With the widespread use of social media platforms, sentiment analysis of user-generated content has become a crucial task in understanding public opinion and trends. In this paper, we compare the performance of three popular machine learning models, namely Random Forest, Support Vector Machine (SVM), and Logistic Regression, in predicting sentiments of post-COVID patients on social media tweets. The study utilizes a dataset of labeled tweets representing positive, negative, and neutral sentiments. The preprocessing of textual data involves tokenization, stop-word removal, and conversion to lowercase to create a suitable input for the models. We utilize Term Frequency-Inverse Document Frequency (TF-IDF) vectorization to transform the text data into numerical features. The sentiment labels are converted to numeric representations for model training and evaluation. The three machine learning models are trained and evaluated on the dataset using metrics such as accuracy, precision, recall and F1-score. The evaluation results are presented and analyzed for each model, providing insights into their strengths and weaknesses in predicting sentiments. The experimental results demonstrate that Random Forest achieves the highest accuracy and F1-score, closely followed by SVM, while Logistic Regression performs slightly lower in comparison. However, all three models exhibit strong predictive capabilities, and their performances vary depending on the specific sentiment class. The findings provide valuable information for researchers and practitioners seeking to employ sentiment analysis in social media monitoring and other related applications. Overall, this study contributes to the understanding of the capabilities of Random Forest, SVM, and Logistic Regression models in sentiment analysis of social media tweets, and offers valuable insights for selecting the most suitable model for specific sentiment prediction tasks.

Hybrid deep learning approach to improve classification of low-volume high-dimensional data

BackgroundThe performance of machine learning classification methods relies heavily on the choice of features. In many domains, feature generation can be labor-intensive and require domain knowledge, and feature selection methods do not scale well in high-dimensional datasets. Deep learning has shown success in feature generation but requires large datasets to achieve high classification accuracy. Biology domains typically exhibit these challenges with numerous handcrafted features (high-dimensional) and small amounts of training data (low volume).MethodA hybrid learning approach is proposed that first trains a deep network on the training data, extracts features from the deep network, and then uses these features to re-express the data for input to a non-deep learning method, which is trained to perform the final classification.ResultsThe approach is systematically evaluated to determine the best layer of the deep learning network from which to extract features and the threshold on training data volume that prefers this approach. Results from several domains show that this hybrid approach outperforms standalone deep and non-deep learning methods, especially on low-volume, high-dimensional datasets. The diverse collection of datasets further supports the robustness of the approach across different domains.ConclusionsThe hybrid approach combines the strengths of deep and non-deep learning paradigms to achieve high performance on high-dimensional, low volume learning tasks that are typical in biology domains.

Cohesive properties of ice powders analogous to fresh plume deposits on Enceladus and Europa

Active jets of water vapour and ice grains discovered by the Cassini spacecraft at the South Pole of Enceladus result in the deposit of freshly erupted materials on the icy surface of the moon, potentially coming directly from its subsurface water ocean. As suggested by sporadic detection of plume activity, similar processes may be at work on Europa as well. Such fresh ice deposit areas are primary targets for future landing and sampling missions for the search of potential bio-signatures. Determining the mechanical properties of such fresh icy powder-like materials is essential to identify safe landing sites and appropriate sampling techniques. Ice surface energy or cohesion is a key parameter driving the porosity of the material, its strength, stickiness and flowability, which determine the stability of fresh surface deposits. In this work we performed an experimental study on laboratory analogues of fresh icy regolith, consisting in investigating the flowability of ice powders through a rotating drum device at several temperatures. Our experimental results show that the flowability of fresh icy deposits significantly decreases with increasing temperatures over the range of 90 to 150 K. Using a simple scaling analysis, we derive the surface energy of ice grains from the observed angle of the flowing surface inside the drum and we show that the surface energy increase by a factor of 5 to 10 between 90 K to 150 K, a behaviour not observed in any other powder material. This result indicates that the flowability of fresh icy deposits at the surface of Enceladus and Europa should significantly change depending on the local surface temperature, which can vary spatially and temporally on the same range of temperature explored here. Due to lower gravity, ice powder will behave as a more cohesive powder on Enceladus than on Europa. Higher cohesion on Enceladus may result in the formation of a very loose regolith with high porosity, making stable landing more challenging. On Europa, the reduction of ice cohesiveness with decreasing temperature should help the flow of freshly eruptive granular ice materials, potentially explaining numerous flow features observed throughout the surface.

Finding a needle in a haystack: insights on feature selection for classification tasks

AbstractThe growth of Big Data has resulted in an overwhelming increase in the volume of data available, including the number of features. Feature selection, the process of selecting relevant features and discarding irrelevant ones, has been successfully used to reduce the dimensionality of datasets. However, with numerous feature selection approaches in the literature, determining the best strategy for a specific problem is not straightforward. In this study, we compare the performance of various feature selection approaches to a random selection to identify the most effective strategy for a given type of problem. We use a large number of datasets to cover a broad range of real-world challenges. We evaluate the performance of seven popular feature selection approaches and five classifiers. Our findings show that feature selection is a valuable tool in machine learning and that correlation-based feature selection is the most effective strategy regardless of the scenario. Additionally, we found that using improper thresholds with ranker approaches produces results as poor as randomly selecting a subset of features.

Towards Smart and Green Features of Cloud Computing in Healthcare Services: A Systematic Literature Review

Background: The healthcare sector has been facing multilateral challenges regarding the quality of services and access to healthcare innovations. As the population grows, the sector requires faster and more reliable services, but the opposite is true in developing countries. As a robust technology, cloud computing has numerous features and benefits that are still to be explored. The intervention of the latest technologies in healthcare is crucial to shifting toward next-generation healthcare systems. In developing countries like Ethiopia, cloud features are still far from being systematically explored to design smart and green healthcare services. Objective: To excavate contextualized research gaps in the existing studies towards smart and green features of cloud computing in healthcare information services. Methods: We conducted a systematic review of research publications indexed in Scopus, Web of Science, IEEE Xplore, PubMed, and ProQuest. 52 research articles were screened based on significant selection criteria and systematically reviewed. Extensive efforts have been made to rigorously review recent, contemporary, and relevant research articles. Results: This study presented a summary of parameters, proposed solutions from the reviewed articles, and identified research gaps. These identified research gaps are related to security and privacy concerns, data repository standardization, data shareability, self-health data access control, service collaboration, energy efficiency/greenness, consolidation of health data repositories, carbon footprint, and performance evaluation. Conclusion: The paper consolidated research gaps from multiple research investigations into a single paper, allowing researchers to develop innovative solutions for improving healthcare services. Based on a rigorous analysis of the literature, the existing systems overlooked green computing features and were highly vulnerable to security violations. Several studies reveal that security and privacy threats have been seriously hampering the exponential growth of cloud computing. 54 percent of the reviewed articles focused on security and privacy concerns. Keywords: Cloud computing, Consolidation, Green computing, Green features, Healthcare services, Systematic literature review.

A Widely Applicable Modeling and Efficient Simulation Method for Power Electronics Grids Based on Unit Switching Circuits

As an essential part of modern power systems, power electronics have improved system performance. They also brought new dynamic and transient problems and posed significant challenges to precise and efficient simulation. Traditional modeling and solving methods fail to balance between the simulation accuracy and efficiency, and they are not flexible enough to adapt to converters with various new topologies. This article proposed a widely applicable modeling and simulation method based on the unit switching circuit that greatly promote the simulation efficiency and modeling simplicity by converting switching circuits into constant sub-grid equations with injection functions. Critical issues for engineering applications, such as the blocking state and the triggering deadband of converters, were also discussed and addressed by boundary balance and conduction path estimation. The error and stability of the solving method were qualitatively analyzed, with further discussions on the validity and numerical features. Several cases, including common and general cases and a real project of power electronic microgrid, tested and verified the accuracy, generalization ability, and efficiency of the proposed method.

Global Ontology Entities Embeddings

Ontologies are among the most widely used types of knowledge representation formalisms. The application of deep learning techniques in the field of ontology engineering has reinforced the need to learn and generate representations of ontological data. This allows ontologies to be exploited by such models, and thus automate various ontology engineering tasks, where most of the existing tools and machine learning approaches require a numerical feature vectors associated with each concept. This paper outlines a novel approach for learning global ontology entities embeddings by exploiting the structure and the various taxonomic and semantic relationships present in ontologies, taking into account all the information present in the ontological graph and carried by the OWL/RDF triples. Thus, producing global ontology entities embeddings capturing the global ontological graph semantics and similarities enclosed in the source ontology. Three different neural network models have been proposed based on two architectures: multi-input and multi-output, trained using the contrastive estimation technique. The evaluation on OWL/RDF ontologies and word semantic similarity tasks using various graph and WordNet based similarity measures, show that our approach yields competitive results outperforming the state-of-the-art ontology and word embedding models.

MBTI-BASED PERSONALITY PREDICTION FROM TEXT USING MACHINE LEARNING TECHNIQUES

Personality prediction research seeks to define and comprehend the nuanced differences in human behavioural tendencies, thought patterns, and emotional expressions. Utilizing an array of methodologies, including psychological assessments, behavioural observations, and computational modelling, researchers aim to anticipate and clarify an individuals distinctive personality traits and characteristics. Natural Language Toolkit (NLTK) approaches are used to preprocess and translate text data into numerical features that can be predicted by machine learning models. The aim of this work is to predict the personality type of an individual linked to their posts and to explore the relevance of the test in the study and categorization of human behaviour using learning models. With the aid of a machine learning model and dataset, the main objective of this research is to determine a persons Myers-Briggs Type Indicator (MBTI) personality type based on their postings. This involves utilizing various methodologies, including psychological assessments and computational modelling, to analyse and classify the unique personality traits and characteristics associated with each MBTI type. The research aims to contribute valuable insights into understanding human behaviour and leveraging machine learning for predictive personality analysis. KEYWORDS: Myers-Briggs Type Indicator, Machine Learning Models, Natural Language Toolkit