Significance Of Various Features Research Articles

A Novel Benchmarking Approach to Assess Fire Safety of Liquid Electrolytes in Lithium‐Ion Batteries

AbstractEstablishing authoritative electrolyte safety assessment methods at laboratory levels is crucial for addressing conflicts from thermal runaway of lithium‐ion batteries. However, self‐extinguishing time (SET), as the most widely used evaluation method now, lacks benchmarks and heavily relies on the specific implementation of test procedures. This work systematically summarizes and investigates key test parameters. Based on repeatability and reliability, burning a glassfiber separator (Φ16 mm) with absorption of 0.1 g liquid electrolyte (LE) can be proposed as a unified method. The concept of self‐extinguishing efficiency (SEE) is further proposed with a new evaluation criterion of i) SEE≤70, Flammable; ii) 70<SEE<90, Flame Retarded; iii) 90≤SEE≤100, Nonflammable. The feasibility of the new protocol is verified by employment in evaluating the effects of 15 representative flame retardants (FRs) on combustion behaviors of LEs. Meanwhile, the underlying flame retardancy mechanism is revealed, indicating that preceding or simultaneous vaporization and pyrolysis of FRs in conjunction with the decomposition of LE promote mitigating electrolyte fire risk. In addition, machine learning is employed to aid in analyzing the significance of various features on SEE, providing further validation of the proposed mechanism. This work provides a benchmark for the safety evaluation of LEs, facilitating the advancement of fire‐safe batteries.

Machine Learning for Identifying Emergent and Floating Aquatic Vegetation from Space: A Case Study in the Dniester Delta, Ukraine

Monitoring aquatic vegetation, including both floating and emergent types, plays a crucial role in understanding the dynamics of freshwater ecosystems. Our research focused on the Lower Dniester Basin in Southern Ukraine, covering approximately 1800 square kilometers of steppe plains and wetlands. We applied traditional machine learning algorithms, specifically random forest and boosting trees, to analyze Sentinel-2 satellite imagery for segmenting aquatic vegetation into emergent and floating types. Our methodology was validated against detailed in-situ field measurements collected annually over a 5-year study period. The machine learning classifiers achieved an F1-score of 0.88 ± 0.03 in classifying floating vegetation, outperforming our previously suggested histogram-based thresholding methodology for the same task. While emergent vegetation and open water were easily identifiable from satellite imagery, the robustness and temporal transferability of our methodology included accurately delineating floating vegetation as well. Additionally, we explored the significance of various features through the Minimum Redundancy - Maximum Relevance algorithm. This study highlights advancements in aquatic vegetation mapping and demonstrates a valuable tool for ecological monitoring and future research endeavors.

An enhanced artificial bee colony algorithm using radial bases function neural networks for color image segmentation

The aim of segmentation process is to partition the image into homogeneous regions or objects. The proposed segmentation algorithms using adaptive feature weighting to distinguish the significance of various features. To extract effective feature extraction could be used for establishing regions in region based image segmentation. The main goal of this exploration work is to segment objects that separating the foreground and background of the color images. The segmentation for color images is focused on the Enhanced Artificial Bee Colony segmentation algorithm that utilizes the Radial Basis Function (EABCRBF) neural network algorithm. This work arranges RBF neural networks to predict the end limits of the segmentation, which are formed from the artificial bee colony province made in the image histogram topography. The radial basis function initial parameters, for instance, centers and widths are automatically set upon the histogram peaks and minima. The proposed EABCRBF strategy is compared with two existing techniques of Artificial Bee Colony (ABC) and Fuzzy C-Means (FCM). The EABCRBF method gives great outcomes for color image segmentation as it extracts additional information from color images. The experimentation results are shown through Mat Lab R2013a.

Detecting the Confusion of Students in Massive Open Online Courses Using EEG

Confusion among students hinders learning and contributes to demotivation and disinterest in the course materials. However, it takes a lot of time and resources to identify confused pupils in extensive courses. Using LSTM and Attention, we suggest a deep learning model for monitoring students' confusion by EEG signals from students when they watching MOOC videos. The model obtained an accuracy of 0.82 on the EEG data, exceeding the previous experimental results for this dataset. Experiments show that the attention mechanism picks up on the significance of various features on prediction results. It can effectively solve the overfitting problem and improve the model classification effect.

Differing morphology and ultrastructure of the male copulatory apparatus in species-groups of Anagrus Haliday (Hymenoptera: Mymaridae)

Male genitalia of 13 members of the mymarid wasp genus Anagrus have been studied: Anagrus (Anagrella) mymaricornis (Bakkendorf), Anagrus (Paranagrus) unilinearis Soyka, A. (Anagrus) ustulatus Haliday, A. (A.) erythroneurae Trjapitzin and Chiappini, A. (A.) atomus (Linnaeus), A. (A.) aegyptiacus Soyka, A. (A.) vilis Donev, A. (A.) sensillatus Viggiani and Jesu, A. (A.) breviphragma Soyka, A. (A.) incarnatus Haliday, A. (A.) obscurus Förster sensu Soyka, A. (A.) fennicus Soyka and A. (A.) avalae Soyka. Taxonomically significant differences between subgenera and the atomus and incarnatus species-groups are described. The functional significance of various features, and in particular, the arrangment of sensilla are discussed.

Primary depositional and diagenetic features in the banded iron formation and associated iron-ore deposits of Noamundi, Singhbhum district, Bihar, India

The Banded Hematite Jasper (BHJ) Formation of Noamundi region in Bihar, belonging to the lower part of the Iron Ore Group of early Precambrian age (c. 2900–3200 Ma), exhibits numerous primary depositional and diagenetic features, both in BHJ as well as the associated iron ore deposits. Observed primary depositional features include banding and bedding of different geometric-types, surface-markings including interference ripple-marks, current crescents, linear markings, scour-and-fill structures, etc. and within-mass microstructures such as spherulites, granules, discs and maculose cylindrical bodies. Diagenetic features, such as fabric changes, micro-dessication structures, gravity-density features, etc. and motion-and-disruption features of various kinds are also seen. The significance of various features in terms of probable mode and environments of deposition of BHJ and the iron ore beds has been considered. In general, shallow water environment of deposition in a region proximal to the shoreline with a rather steep paleoslope of the shelf has been surmised.

New developments in high fidelity

The features and performance of current home high-fidelity products will be described and compared. The evolution of modern electronic and electro-acoustic audio components is related to technological developments and consumer pressures. The audible or practical significance of various features and performance parameters is also discussed. The status of four-channel sound is reviewed. Some probable trends in future equipment designs are considered.

Tyres and Crime

The need for accurate, comprehensive and speedy permanent records of tyre marks is emphasised. The significance of various features found in tyre marks is discussed, and the means by which the make of vehicle may be deduced from tyre marks are suggested. Features which enable a tyre mark to be related to a specific tyre are detailed. Reference is also made to the double transfer of tyre marks onto clothing and cloth marks onto tyres.

Application of quantitative electron microscopy to the study of Mycobacterium lepraemurium and M. leprae.

SUMMARY: The standard methods for assessing viability of micro-organisms are not applicable to rat and human leprosy bacilli since neither organism can be grown in vitro. McFadzean & Valentine (1958, 1959) suggested that the electron microscope might provide a quantitative guide to the viability of these organisms by allowing dead forms to be identified. This technique is further investigated and shown to be valid and reasonably accurate for Escherichia coli provided the organisms are not morphologically fixed by the killing agent. It is concluded that the method can assess death of leprosy bacilli in the host when this occurs either naturally or aided by bactericidal drugs, and also loss of viability on storage, but not sudden killing by more violent chemical or physical means. The method has been found useful for following the survival of Mycobacterium lepraemurium in tissue cultures. It is suggested that death in the host but not death occurring on storage can be measured by a simple classification of the bacilli seen with the light microscope after the conventional carbol fuchsin stain. It is indicated that many of the bacilli obtained from untreated human cases of leprosy are dead, while from rats the percentage which is degenerate is low. The significance of various features of the leprosy bacilli seen with the electron microscope is discussed. There is no evidence that the bacilli form spores or capsules.