Identification Of Class Research Articles

Classification and identification of pest, diseases and nutrient deficiency in paddy using layer based EMD phase features with decision tree

Pest attack, disease incidence, and nutrient deficiency are the major factors limiting the yield of paddy. Therefore, the paper proposes a classification system for the identification of pest, disease, and nutrient deficiency classes. This approach initially preprocesses leaf images using entropy filtering followed by a leaf segmentation process. Multiple layers are then constructed on the leaf image through which features are extracted. The Gray Level Co-occurrence Matrix (GLCM) algorithm and Principal Component Analysis (PCA) are used to extract the global texture features of the leaf image. A 1D-signal sequence is constructed on each layer, which is decomposed by the Empirical Mode Decomposition algorithm from which the phase features are estimated. The features are trained/classified using the decision tree classifiers that classify the pest attack, disease incidence, and nutrient deficiency categories. The proposed approach provides a precision, accuracy, specificity, sensitivity, and F1-score of 97 %, 97.88 %, 96.52 %, 96.7 %, and 96.7 % respectively.

“What’s that supposed to mean?”: Examining Chicanx/Latinx transfer student intersectional microaggressions at a public four-year research-intensive university

ABSTRACT This study employed a Critical Race Theory’s intersectionality framework to examine microaggressions Chicanx/Latinx students experienced targeting their transfer student identity while simultaneously interrogating their racial, ethnic, class, and gender identities. The authors define transfer student intersectional microaggressions as microaggressions rooted in stereotypical beliefs that associate the community college transfer student identity with racialized, classed, gendered, and ageist-based assumptions to suggest lower academic abilities, socioeconomic status, and/or a questionable moral character. Based on a qualitative case study of 16 Chicanx/Latinx transfer students at a research-intensive public institution, two major forms of transfer student intersectional microaggressions emerged: (1) interrogation of perceived academic/intellectual capabilities and (2) interrogation related to race, class, gender, and/or age. Participants consistently grappled with the impact of microaggressions on their intersecting identities. This study revealed that the transfer student identity amplifies intersectional oppression for Chicanx/Latinx students. The authors offer policy and practice recommendations.

Phenotypic and genotypic identification of class C and D β-lactamases in clinical isolates of Pseudomonas aeruginosa: a cross-sectional study.

The emergence of Pseudomonas aeruginosa (P. aeruginosa) antibiotic resistance is an important public health problem worldwide that can negatively affect infection control. Therefore, obtaining knowledge about antibiotic resistance mechanisms is necessary for infection control policies. This study aimed to determine the frequency of class C and D β-lactamases in P. aeruginosa strains isolated from patients referred to Ardabil hospitals using phenotypic and genotypic tests. Phenotypic detection of β-lactamases including AmpC cephalosporinase, oxacillinase (OXA)-type extended-spectrum β-lactamases (ESBLs), and OXA-type carbapenemases were performed using the disk diffusion-based methods. Amplification of genes encoding classes C (ampC and FOX genes) and D (OXA-1, OXA-2, OXA-10, OXA-23, and OXA-48 genes) β-lactamases was performed using the polymerase chain reaction (PCR) method. A quantitative reverse transcription PCR (qRT-PCR) method was used to determine the expression level of the ampC gene among multiple drug-resistant and imipenem-resistant P. aeruginosa strains. In phenotypic tests, the prevalence of AmpC cephalosporinase, OXA-type ESBLs, and OXA-type carbapenemases were 52.5%, 7.2%, and 95.8%, respectively. In genotypic tests, the prevalence of ampC, FOX, OXA-1, OXA-2, OXA-10, OXA-23, and OXA-48 genes were 100%, 0%, 4.3%, 60.8%, 42%, 29.7%, and 2.9%, respectively. In addition, the ampC gene overexpression was seen in 16 (33.3%) drug-resistant P. aeruginosa clinical isolates. Given the presence of class C and D β-lactamases in clinical isolates of P. aeruginosa in Ardabil hospitals, early detection of these strains can help prevent the spread of resistant strains in hospital environments and subsequent treatment failure.

Hybrid deep learning with pelican optimization algorithm for M2M communication on UAV image classification

<p>Machine-to-machine (M2M) communication for unmanned aerial vehicle (UAVs) and image classification is essential to current remote sensing and data processing. UAVs and ground stations or other linked devices exchange information seamlessly using M2M communication. M2M connectivity helps UAVs with cameras and sensors communicate aerial pictures in real time or post-mission for image categorization and analysis. During flight, UAVs acquire massive volumes of picture data. Image classification, commonly using deep learning (DL) methods like convolutional neural network (CNN), automatically categorizes and annotates photos based on predetermined classes or attributes. This work uses UAV photos to produce hybrid deep learning with pelican optimization algorithm for M2M communication (HDLPOA-M2MC). HDLPOA-M2MC automates UAV picture class identification. GhostNet model is used to derive features in HDLPOA-M2MC. The HDLPOA-M2MC approach leverages pelican optimization algorithm (POA) for hyperparameter adjustment in this investigation. Finally, autoencoder-deep belief network (AE-DBN) model can classify. The HDLPOA-M2MC method’s enhanced outcomes were shown by several studies. The complete results showed that HDLPOA M2MC performed better across measures.</p>

The Effect of Defining Model on the Accuracy of Syntactic Class Identification in When-Definitions in Digital Dictionaries

The Effect of Defining Model on the Accuracy of Syntactic Class Identification in When-Definitions in Digital Dictionaries

Peptide-mimetic treatment of Pseudomonas aeruginosa in a mouse model of respiratory infection

The rise of drug resistance has become a global crisis, with >1 million deaths due to resistant bacterial infections each year. Pseudomonas aeruginosa, in particular, remains a serious problem with limited solutions due to complex resistance mechanisms that now lead to more than 32,000 multidrug-resistant (MDR) infections and over 2000 deaths in the U.S. annually. While the emergence of resistant bacteria has become ominously common, identification of useful new drug classes has been limited over the past over 40 years. We found that a potential novel therapeutic, the peptide-mimetic TM5, is effective at killing P. aeruginosa and displays sufficiently low toxicity in mammalian cells to allow for use in treatment of infections. Interestingly, TM5 kills P. aeruginosa more rapidly than traditional antibiotics, within 30–60 min in vitro, and is effective against a range of clinical isolates, including extensively drug resistant strains. In vivo, TM5 significantly reduced bacterial load in the lungs within 24 h compared to untreated mice and demonstrated few adverse effects. Taken together, these observations suggest that TM5 shows promise as an alternative therapy for MDR P. aeruginosa respiratory infections.

Development of a Plasma-Based Nanosensor Platform for Triage of Neurological Tumors

Neurological tumors have diverse etiologies and treatments. Presently, diagnostic technologies such as MRI or CT scans are limited by poor resolution, and thus imprecise identification of tumor classes. As a result, diagnostic methods rely on highly invasive intracranial biopsies for conclusive tumor identification. Development of non-invasive methods to accurately identify tumor class may lead to earlier and more accurate diagnoses, enabling prompt and targeted treatment strategies. To address this unmet clinical need, we report the development a molecular-recognition-based nanosensor array that can accurately identify a molecular fingerprint of glioma and meningioma from plasma. We developed this capability using sensor arrays of chemically modified carbon nanotubes that can transduce subtle differences in the physicochemical properties of molecules in biofluids via sensitive molecular binding interactions. Using high-throughput near-infrared fluorescence spectroscopy, we screened 23 chemically modified (6,5) carbon nanotubes sensors in 324 patient plasma samples. We trained machine learning models to classify sensor responses as coming from either glioma or meningioma tumors. The best performing models were able to achieve a positive predictive value of 0.75. Sensor feature analysis revealed a set of spectral features positively associated with class differentiation. This work has the potential to aid clinical care by facilitating the early triage of different types of tumors to provide better treatment options for patients.

TinyML-Raman: A novel IoT based field-deployable spectra analysis for accurate identification of pharmaceuticals and trace dye-pesticide mixtures from facile SERS method

BackgroundUpcoming inexpensive, compact Internet of Things (IoT) microcontrollers i.e., tiny-machine learning (TinyML) takes the ML driven Raman spectroscopy one step ahead for realization of more affordable and highly compact field deployable instruments. Further, lack of large spectral datasets and need for numerous specialized SERS substrates impede the development of various ML-based surface enhanced Raman spectroscopy (SERS) applications. The aim is to introduce TinyML analysis on wide range of spectra classes using customized dataset obtained with low-cost SERS. In this regard, it is vital to establish an optimum ML model and efficient data handling methodology for low memory TinyML units. ResultsWe introduce a novel TinyML methodology for accurate classification of large spectra classes with smartphone assistance for data communication and results visualization. To generate large customized spectral dataset, we present a facile, micro-drop SERS using Au colloids and reusable grooved Al substrates. The results demonstrated that memory efficient 8-bit data quantization based convolutional neural network is effective for accurate identification of 22 different spectra classes of trace dye-pesticide mixtures and pharmaceuticals. In this novel quantized data analysis on significantly varied intensity and complex variation spectra classes i.e., many individual, binary-mixtures and some with varied compositions, data normalization is shown to be powerful for improving ML classification accuracy from about 55 % to >99.5 %. Its robustness is demonstrated using inter-instrument driven data variations such as spectral shifts, high noise, and abscissa-flip, with five-fold cross validation of model performance. In addition, on-site quantification of analyte through spectral intensity is also demonstrated. SignificanceThis study opens up a new approach of ML analysis towards realization of next generation field deployable analytical instruments maintaining data privacy. It presents a detailed procedure of quantized spectral data analysis and its implementation in TinyML, attractive for various users and instrument manufacturers. The presented innovative computer-free ML analysis can be employed in all types of spectrometers, meeting the common goal of Raman spectroscopy i.e., accurate identification of complex spectra classes.

A comprehensive review of antimicrobial peptides: Mechanisms, classifications and designations

Antimicrobial peptides (AMPs) are a group of alkaline poly-peptide compounds that have antibacterial activity and are produced by insects. This research examines three key aspects of antimicrobial peptides (AMPs): their mechanism of action, categorization, and designation. Antimicrobial peptides have a wide range of antimicrobial action, with a significant number of them being naturally occurring peptides. Consequently, they are increasingly being recognized as promising candidates for therapeutic applications. Antimicrobial peptides can be categorized into many categories based on their structural characteristics and origin. The field of computer-assisted peptide design integrates computer-aided design technology with the process of drug creation, so leveraging its inherent benefits and offering a rational and efficient theoretical framework for the design of peptides. The identification of novel classes of antibiotics is a promising approach for addressing the issue of antibiotic resistance. Antimicrobial peptides possess significant potential for application within the pharmaceutical sector due to their notable antibacterial efficacy, extensive range of antibacterial activity, diverse structural characteristics, and extensive array of options. Moreover, these peptides have a reduced likelihood of inducing resistance mutations in target strains. Currently, a number of poly-peptide antibiotics are being subjected to preclinical feasibility investigations. One of these antibiotics, magainins, has progressed to the phase III clinical trial stage.

Class Identity Signaling through the Strategic Rebranding of Traditional Egyptian Food-Related Terms on Digital Platforms

This study investigates the way re-branding traditional Egyptian food-related terms is strategically used by an emerging socio-economic class to assert their class identity in digital milieus. Specifically, it analyzes the strategies employed for the Englishization and hybridization of culinary culture and examines the underlying socio-cultural motivations and dynamics driving such an innovative linguistic practice. Drawing on insights from sociolinguistics and sociology, we analyzed two data sets collected by means of an online survey and semi-structured interviews. Findings revealed that there is an emerging exclusive socio-economic class that manifests itself through specific lexical preferences based on diverse types of lexical borrowings. Additionally, the Englishization of traditional Egyptian food-related terms partially drove the emerging class’s rejection of social equality and solidarity with collective socio-cultural Egyptian identity. Such a class division, primarily initiated on digital platforms, was found to be motivated by complex factors at play, including age, gender, educational level, lifestyle, technology usage, etc. The findings of this study have implications for understanding the intersection of language, class identity, and technology in contemporary Egyptian society.

A new approach for identification of dispersivity class of soils by combining physical and chemical tests

The safety and performance of embankment dams after impounding water are highly related to the characteristics of the selected dam material. Impermeability is achieved by using clay soils as the core material in embankment dams. It has been observed that there are many collapsed dams, especially when dispersive soils with very low internal erosion resistance are used in the impermeable zone. For this reason, such soils should be determined at the design stage and not used in the construction of embankment dams. The dispersion mechanism is known to be controlled by clay mineralogy and cations in the pore fluid of soil. The physicochemical behaviour of the clay-water system is crucial to clarify the dispersion mechanism. Physical and chemical tests used for determining the dispersibility potential of soils are extensive and time-consuming methods. An alternative method has been proposed within the scope of this study. Three user-friendly and practical models based on Gene Expression Programming (GEP) were produced using chemical and double hydrometer test results to predict dispersion percentage (D%). In addition, a new classification criteria was proposed to identification of dispersivity class based on pinhole test data. If the dispersion percentage is less than 12% is soil is classified as non-dispersive, the dispersion percentage is between 12% and 30% and greater than 30%, it is defined as intermediate and dispersive, respectively. With this proposed criterion, more consistent and successful results were obtained for the dispersivity class.

Book Review: Global Sports and Contemporary China: Sport Policy, International Relations and New Class Identities in the People’s Republic by Oliver Rick and Longxi Li

Book Review: <i>Global Sports and Contemporary China: Sport Policy, International Relations and New Class Identities in the People’s Republic</i> by Oliver Rick and Longxi Li

Systematic Review of Scholarly Work on Social Class Identities in Second and Foreign Language Educational Contexts

This paper presents a review of scholarly work on social class identities (SCI) in second and foreign language (S/FL) educational contexts. Of the 94 studies identified, 31 were annotated. Thematic analysis was employed to identify trends elucidating where, what, and how SCI has been conceptualized, theorized, and methodologically designed. Inclusion criteria focused on SCI research in S/FL contexts, while studies examining SCI outside educational settings were excluded. Results indicate a slow-growing interest in SCI and critical theory, with a Europe-Western dominance. SCI has been approached as: a fixed category based on socioeconomic status determining educational and L2 learning factors; a performed sociocultural phenomenon grounded in individuals lived experiences; a process experienced by study-abroad and migrant individuals; and as an element of colonial difference. Research gaps include SCI examination in EFL contexts and from decolonial perspectives, highlighting the need for political action to subvert social inequalities and address colonial wounds related to socioeconomic issues.

Assessing interpreter’s disagreements in land cover reference data collection from historical Landsat time series in Amazon

ABSTRACT Land cover information, derived from the classification of Remote Sensing images, is only useful if accompanied by a rigorous accuracy assessment, usually dependent on reference samples. As field data can be very rare for multi-temporal analysis, many studies use samples visually interpreted from Remote Sensing images. This subjective process is prone to errors in interpretation, leading to uncertainties not fully quantified in the reference sample collection of historical time series. We evaluated the agreement rate among independent interpreters in the identification of land cover classes assigned to pixels for yearly Landsat observations from 1984 to 2020, in the Lower Tapajós Basin region (within the Brazilian Amazon). Considering 10 classes, we found that sets of three interpreters completely disagreed on the label of 14.8% of the pixels for each year, and completely agreed in less than half (39.6%). These values change to 2.7% and 68.8% when we consider only five classes in the analysis. We observed time-dependent variations in agreement for specific land cover classes: whereas forest areas tend to present an increase in the disagreement rate over time, land cover types that happen only in previously deforested areas tend to present a higher proportion of pixels with better agreement rates. Disagreements tend to be higher in pixels identified as borders or near borders between targets. However, there is a non-negligible amount of disagreements in pixels identified as not borders caused by limitations derived from the unit of analysis, image composition techniques, and intrinsic similarities among classes. We highlight problems with the identification of classes that happen in complex spatiotemporal patterns (Secondary Vegetation, Small-scale Agriculture, and Degraded Forest). These results alert to possible inconsistencies in reference samples collected for the accuracy assessment of land cover time series and the correspondent values of the calculated accuracy indexes.

On strongly quasi S-primary ideals

This paper centers around one of several generalizations of primary ideals. Which is an intermediate class between S-primary ideals and quasi S-primary ideals. Let R be a commutative ring with identity and S be a multiplicative closed subset of R. A proper ideal I of R disjoint from S is called strongly quasi S-primary if there exists an s ∈ S such that whenever x , y ∈ R and xy ∈ I , then either s x 2 ∈ I or sy ∈ I . Many basic properties of strongly quasi S-primary ideals are given, and examples are presented to distinguish the last concept from other classical ideals. Moreover, forms of strongly quasi S-primary ideals in polynomial rings, power series rings and idealization of a module are investigated.

Not to blame, but still responsible: Negotiating social representations of neoliberal feminism amongst urban middle‐class Indians

AbstractIndian gender advocacy has been criticised for promoting individualistic neoliberal solutions to issues requiring structural change. Such solutions are most accessible to middle‐class women and their dominance in gender advocacy is attributed to the middle‐class viewership of campaigns. Few studies have researched how this intended audience responds to such messages. As patriarchy benefits men, it is also important to understand how gender intersects with social class to shape responses. Accordingly, this research explores the reception of neoliberal feminist ideals in Indian gender advocacy amongst an urban middle‐class audience of 25 participants, comprising of 13 women and 12 men in Bengaluru, India. The theoretical and methodological framework is the social representations theory. Participants viewed three campaign videos and undertook semi‐structured interviews. Thematic analysis was employed to explore their perceptions of the causes and solutions to the issues portrayed in the campaigns, leading to three themes being identified. In the theme of ‘individuals’, participants held women responsible for resisting oppression. In the theme of ‘culture’, participants viewed culture as a cause of patriarchy, with cultural change seen as a solution. In the theme of ‘institutions’, participants discussed institutional sexism but were pessimistic about institutional reform. These findings suggest that whilst patriarchy is blamed on cultural norms and institutional failure, solutions are often conceived in terms of self‐transformation of individual women. These results are situated in social representations' theoretical premise of themata and positioning to analyse how social locations like gender, class and regional identity were mobilised by participants to contest or maintain a neoliberal feminist ideology. Please refer to the Supplementary Material section to find this article's Community and Social Impact Statement.

Enhancing ECG Heartbeat classification with feature fusion neural networks and dynamic minority-biased batch weighting loss function

Objective. This study aims to address the challenges of imbalanced heartbeat classification using electrocardiogram (ECG). In this proposed novel deep-learning method, the focus is on accurately identifying minority classes in conditions characterized by significant imbalances in ECG data. Approach. We propose a feature fusion neural network enhanced by a dynamic minority-biased batch weighting loss function. This network comprises three specialized branches: the complete ECG data branch for a comprehensive view of ECG signals, the local QRS wave branch for detailed features of the QRS complex, and the R wave information branch to analyze R wave characteristics. This structure is designed to extract diverse aspects of ECG data. The dynamic loss function prioritizes minority classes while maintaining the recognition of majority classes, adjusting the network’s learning focus without altering the original data distribution. Together, this fusion structure and adaptive loss function significantly improve the network’s ability to distinguish between various heartbeat classes, enhancing the accuracy of minority class identification. Main results. The proposed method demonstrated balanced performance within the MIT-BIH dataset, especially for minority classes. Under the intra-patient paradigm, the accuracy, sensitivity, specificity, and positive predictive value for Supraventricular ectopic beat were 99.63 % , 93.62 % , 99.81 % , and 92.98 % , respectively, and for Fusion beat were 99.76 % , 85.56 % , 99.87 % , and 84.16 % , respectively. Under the inter-patient paradigm, these metrics were 96.56 % , 89.16 % , 96.84 % , and 51.99 % for Supraventricular ectopic beat, and 96.10 % , 77.06 % , 96.25 % , and 13.92 % for Fusion beat, respectively. Significance. This method effectively addresses the class imbalance in ECG datasets. By leveraging diverse ECG signal information and a novel loss function, this approach offers a promising tool for aiding in the diagnosis and treatment of cardiac conditions.

Wind resource assessment for turbine class identification in Bayanzhaganxiang, China

Abstract The wind resource assessment has been used effectively to identify the classification of wind turbines at a particular wind farm site. The current study used WAsP software and various statistical methods such as graphical, energy pattern factor, standard deviation, and Rayleigh distribution methods to find the Weibull parameters by evaluating the raw data collected from August 2005 until July 2006 at four (4) different heights of the meteorological mast station in Bayanzhaganxiang, China. The Weibull parameters were utilized to find the annual mean wind speed, probability density, and cumulative distribution functions of wind conditions at the reference heights of 70 m, 50 m, 30 m, and 10 m. The wind shear coefficient was 0.130 with an overall roughness factor of 0.0385 m, suggesting the site vicinity is an open country with no significant structures and vegetation. The results also showed that the post-processed output from WAsP and standard deviation method at the sensor’s height of 70 m have a correlation coefficient and confidence level of 0.99977 and above 95%, respectively. Based on the turbine classification from GL Wind 2003 and IEC 61400-1 Ed.2, it was found that the turbine class ideal for the site is class III wind turbines with an annual mean wind speed of 7.439 m/s at a hub height of 99 m. The measured wind power density at hub height was calculated according to IEC 61400-12-1, which yields 464.36 W/m2. The characteristic wind turbulence at 70 m high is IEC subclass B. Among the selected wind turbines, the net annual energy production with efficiency is 8,059.57 MWh/year using Avantis AV1010, with the highest capacity factor of 40.05%. It has been found that the lowest energy generation cost is US$ 0.0292/kWh for a period of 20 years.

Classification of Cardiovascular Arrhythmia Using Deep Learning Techniques: A Review

Deep Learning (DL), an offshoot of Machine Learning (ML) has emerged as a powerful and feasible solution for medical image analysis due to advancements in robust computer software and hardware technologies. It plays a key role in Cardiovascular disease (CVD) diagnosis by detecting anomalies in Electrocardiogram (ECG) signals. Cardiac arrhythmia, which refers to irregular heartbeat, may signal an early symptom of CVD and can lead to fatal outcomes if ignored. Accurate detection of arrhythmia is very challenging even for experts to distinguish between acute and chronic conditions in ECG readings. This triggered the focus of researchers to explore the application of Artificial Intelligence for ECG classification. Traditional machine learning methods use handcrafted features that require domain knowledge. The new era in DL makes the automatic detection of Cardiovascular Disease (CVD) possible. In this paper, an exhaustive review of DL-based techniques for ECG classification has been presented. Research findings in this survey indicate the challenges and issues with arrhythmia detection, such as single lead and multiple lead ECG signals, choice of the size of the training data set, and the number of arrhythmia classes, etc. The study also signifies that there is great scope for improving the performance of arrhythmia prediction models by employing hybrid ensemble learning, time series analysis using Recurrent Neural Network architectures and identification of unexplored classes of arrhythmia.

The first national survey on cultural participation in Portugal: exploring social and professional backgrounds

ABSTRACT Portugal undertook the first national survey of cultural practices (2020) with support from the Calouste Gulbenkian Foundation. The present paper describes the contemporary cultural eclecticism in keeping with the analysis of key indicators for the cultural consumption of cinema, live performances, theatre, dance, circus, festivals, and local events. In line with previous European Survey conclusions, the first Portuguese National Survey results demonstrate how the younger and better educated, alongside those raised by educated parents, display “cultural omnivorism”. The main findings converge around how the influence of social backgrounds, positions in the professional structure and class identities, are shaping cultural consumption as pointed out by Reeves in his cross-national European study.