Identification Of Class Research Articles

Education-related cognitive labour in Chinese immigrant families

ABSTRACT While the cognitive dimension of family work has received considerable scholarly attention, one of the aspects unaccounted for is couples’ mental efforts in promoting children’s social and intellectual development, referred to as education-related cognitive labour in this study. Using 40 in-depth interviews with members of 20 Chinese immigrant couples in Ireland, I identify four interacting but distinct forms of education-related cognitive labour: strategizing; learning and teaching; disciplining; and assessing. Though these work are perceptible and acknowledged between the spouses, the labourer experiences little satisfaction from task accomplishment. Besides, worry is embedded in those thinking activities, which derives from Chinese immigrants’ internalized insecurities about their class and ethnic identities as well as uncertainty over the quality of Irish education. The division of education-related cognitive labour is also a gendered phenomenon with mothers carrying a heavier cognitive load and performing more onerous tasks than fathers. In addition, unlike ‘concerted cultivation’, the parenting practices of the sampled Chinese immigrants are ‘instrumental cultivation’. These features are associated with the persistent influence of Confucian culture among Chinese immigrant parents, their migration experiences in Ireland and the conflicting educational values between the first and second-generation immigrants.

YOLO-Based Bacteria Detection and Identification: A Deep Learning Approach for Microbial Diagnosis

Abstract: This article describes the rapid detection and identification of bacteria using deep learning. Rapid and accurate detection and identification of bacterial strains is important not only for effective diagnosis and treatment in clinical settings, but also for public health surveillance and ensuring food safety. Traditional bacterial detection methods, some of which, such as culture-based techniques, are time- consuming and labor-intensive, limiting their applicability in high- throughput and real-time analysis. Machine learning algorithms using deep convolutional neural networks (CNNs) offer a promising alternative. Here, we present a deep learning-based approach combined with the Yolo algorithm for rapid and accurate detection of bacterial class identities. Our method is designed to analyze high-dimensional data in real time, achieving rapid identification with high accuracy. We used multi-class classification to identify Gram-positive and Gram- negative strains and differentiate between all tested bacterial strains. We propose a simple YOLO and CNN architecture and use a many- class bacterial isolation dataset for training and testing. We achieve a discrimination accuracy of about 86% with near real-time discrimination speed. Our results show that the processing time is significantly reduced compared to traditional methods, reducing the detection time to minutes. The proposed system provides a scalable solution that can be integrated into clinical and laboratory workflows and will be a valuable tool to improve patient outcomes and public health responses. Keywords: Bacteria, Rapid detection, Deep learning, CNN Traditional methods, YOLO classification, Image Processing

In vitro antiplasmodial activity and cytotoxicity of three Ziziphus (Rhamnaceae) species from South Africa.

In vitro antiplasmodial activity and cytotoxicity of three Ziziphus (Rhamnaceae) species from South Africa.

Collaborative DDoS defense for SDN-based AIoT with autoencoder-enhanced federated learning

Collaborative DDoS defense for SDN-based AIoT with autoencoder-enhanced federated learning

Transforming urban waste collection inventory: AI-Based container classification and Re-Identification.

Transforming urban waste collection inventory: AI-Based container classification and Re-Identification.

Key class identification: a comprehensive dataset and a new GNN model

Key class identification: a comprehensive dataset and a new GNN model

Епістемологічні засади «синтетичної філософії» Андреаса Рюдіґера

The article aims to (1) reconstruct the epistemological foundations of Rüdiger's “synthetic philosophy"; (2) to reveal the main innovations of his view on the origin of ideas in comparison with the views of the representatives of English empiricism; (3) to identify the possible influence of his epistemological ideas on the further development of German philosophy of the 18th century. The article shows that Rüdiger's main innovation is the thesis that the final point of reliable knowledge is not external but internal sensation, free from all internal and external influences. Considering ideas as actions of the intellect, Rüdiger endows them with synthetic, formative functions. Having essentially expanded the idea concept from imaginative and conceptual to propositional and discursive, Rüdiger presents knowledge as a reflexive creative process of obtaining new knowledge, whose reality and reliability are based on internal sensation. These ideas, and his concept of knowledge as a reflexive process of successive synthesis of knowledge, could influence not only his followers (Chr. Crusius and J. N. Tetens) but also Kant and other representatives of German classical idealism.

Piperazine-Substituted Pyranopyridines Exhibit Antiproliferative Activity and Act as Inhibitors of HBV Virion Production.

Advances in medicinal chemistry have led to the development of anticancer and anti-infectious drugs. However, many types of cancer and viral infections such as hepatitis B virus or SARS-CoV-2 are still treated ineffectively. Therefore, further development of effective and selective lead compounds as potential drugs is still highly demanded. In this study, we synthesized a novel series of piperazine-substituted pyranopyridines and evaluated their anticancer and antiviral properties. Antiproliferative activity was determined in a panel of various tumor cell lines as well as non-tumor hepatic HepaRG cells. Mechanisms of cytotoxicity were assessed by fluorescent microscopy techniques. Antiviral activity was analyzed towards DNA and RNA viruses in infectious cell culture systems. Several compounds showed antiproliferative activity towards various cancer cell lines at micromolar and submicromolar concentrations. Mechanisms of cytotoxicity involve the induction of apoptosis and are not mediated via ERK1/2 pathway or oxidative stress. Several compounds exhibit selective activity against hepatitis B virus by preventing the formation of virion particles. This study led to the identification of a novel class of piperazine-substituted pyranopyridines with antiproliferative activity towards a wide range of tumor cell lines as well as the non-toxic inhibitor of HBV virion production.

Decoding the Complex Functional Landscape of the ykkC Riboswitches.

The ykkC class is the most diverse riboswitch class to date, recognizing structurally and chemically diverse ligands using only minor changes in sequence and structure. Structural studies have demonstrated how sequence changes correspond to altered specificity; however, they are insufficient to define the requirements for functional riboswitch specificity. Here, we report an extensive mutational analysis of the ppGpp riboswitch to investigate the functional role in transcriptional control for this variant riboswitch. Disruption of the terminator hairpin at a single base pair is sufficient to abolish nearly all function, highlighting the fine-tuning of the terminator hairpin to its corresponding aptamer domain. This fine-tuning has been observed in other riboswitches, suggesting that high levels of tunability may be a common feature of riboswitches. Additionally, mutational analysis shows that the previously reported binding site position, G93, does not necessarily correspond to PRPP-driven function as expected. Phylogenetic analysis of natural riboswitches that contain G93 revealed an additional ykkC subclass that binds to both XMP and GMP. This variant subclass is associated with genes for de novo GMP synthesis. Identification of this variant class provides further evidence for small sequence changes corresponding to altered ligand specificity.

Open-world Radio Frequency Fingerprint Identification via Augmented Semi-supervised Learning

In complex electromagnetic environments, the identification and differentiation of diverse radio frequency (RF) emitters become particularly crucial. Existing RF fingerprinting methods demonstrate limitations when dealing with numerous unknown emitters, making it challenging for accurate classification and recognition. These limitations hinder the effective handling of specific unknown emitters.To address this issue, we introduce a novel RF fingerprinting method suitable for open-world conditions for the first time. We develop a novel RF fingerprinting model, Roinformer, to extract signal features with positional attention. We then leverage data augmentation strategies such as noise jitter and signal frame rearrangement to construct an effective pre-training model. Moreover, by incorporating instance-level similarity loss and a novel local entropy regularization approach, we significantly enhance the accuracy of known class identification and mitigate the catastrophic forgetting of known signal samples. Experimental results on three temporal signal datasets demonstrate that our method effectively recognizes both the known and unknown classes, outperforming several state-of-the-art methods by a large margin.

From Invisibility to Visibility: Reshaping Spanish Students Understanding of Afro-Latinx Identity Through Classroom Learning

ABSTRACT Afro-Latinx communities often face invisibility despite being an integral part of Latin America, the Caribbean, and the United States. In the Spanish language classroom, Afro-Latinx are either erased, or students are presented with incomplete narratives of Afro-Latinx populations. The present study thus implements a targeted intervention to address the invisibility of Afro-Latinx communities in college level Spanish curricular. Adopting a qualitative approach, (1) we examine students’ preconceptions of Afro-Latinx identity, (2) the underlying sources shaping these preconceptions and (3) how an Afro-Latinx module impacts students understanding of this identity in a college level intermediate Spanish class. The theoretical frameworks guiding the study are Raciolinguistics, and Critical Language Awareness. The data is drawn from surveys, discussion activities, and student reflections. The results indicate limited initial knowledge regarding Afro-Latinx identity. Students held preconceptions such as monolithic perceptions, color-based assumptions, Afro-Latinx as a racial “other” and the need to “pick a side.” Students underlying assumptions came from the media/entertainment industry, and personal/family backgrounds. However, because of the module, students’ preconceptions changed. They expressed an increased awareness of intersectionality and identity fluidity, underrepresentation, colorism, and discrimination. They also developed empathy and respect for Afro-Latinx individuals and communities.

Tunnel face rock mass class rapid identification based on TBM cutterhead vibration monitoring and deep learning model

Rapid identification of the rock mass condition at the tunnel face is a key problem for TBM operating parameters optimization and subsequent tunnel support measures selection. The vibration induced by the rock breaking contains essential information for evaluating the tunnel face condition. However, conventional vibration-based methods face difficulties in continuously obtaining vibration records for long tunnel sections. Additionally, there’s a lack of TBM cutterhead vibration monitoring, and they heavily depend on expertise and prior knowledge. In this study, an end-to-end deep learning (DL) method was developed for rock mass class identification of TBM tunnel working faces based on the measurement of TBM cutterhead vibration signals, including cutterhead vibration signal measurement, signal preprocessing, model training and optimization, and application verification. The model combines the advantages of 1DCNN, BiLSTM, and self-attention mechanisms, where the structural innovation of 1DCNN inspired by Inception v2 for multi-scale feature extraction. Which can automatically extract the spatial and temporal domain features in the signals to promptly identify the rock mass class at the working face without stopping the normal tunneling process. The accuracy on the test set is 95.89% compared to 85.34% for a traditional ML model, and it has better performance than other DL model architectures. The model underwent validation during subsequent TBM tunneling within the same project, successfully proving its practical reliability.

“I Learned That from You”

The narratives within this autoethnography reiterate a time in the author’s life and describe the feelings, emotions, and (mis)understandings formed surrounding returning home. The author explores the implications of multiple “passings” experienced during that time: passing as working-class poor, passing as middle-class white collar, passing of the previous iteration of self, and the passing of family lineage of blue-collar workers. Examining his father’s decision to shut the family business and the struggle with watching his father’s body shut down, the author reflexively explores his own positionalities through the oscillation of identities to and from his blue- and white-collared worlds. In examining the vulnerabilities, desires, and relationships in and through constructing these narratives, the author also offers insight into class identities, perceived and performed masculinities, the relationship to his father, his place in family, and himself. These collective narratives as a site of resonation and evocation also offer an opportunity for others to gain a new perspective on their selves with regards to the roles they perform, their relationships, classed identities, performed masculinities, and the critical, cultural implications of these events.

Identification of Erosion Classes on Slope Changes Using the Universal Soil Loss Equation (Usle) Method in the Period 2013 and 2023 in the Gunungmasigit Village Area, West Bandung District

Identification of Erosion Classes on Slope Changes Using the Universal Soil Loss Equation (Usle) Method in the Period 2013 and 2023 in the Gunungmasigit Village Area, West Bandung District

Tunnel face rock mass class identification based on multi-domain feature extraction and selection of TBM cutterhead vibration signals

Tunnel face rock mass class identification based on multi-domain feature extraction and selection of TBM cutterhead vibration signals

A real-time rock mass class identification model of the tunnel face based on TBM tunneling and the corresponding muck characteristic parameters

A real-time rock mass class identification model of the tunnel face based on TBM tunneling and the corresponding muck characteristic parameters

Deep learning-based approach for reservoir fluid identification in low-porosity, low-permeability reservoirs

The complex pore structure and weak logging responses in low-porosity, low-permeability reservoirs pose challenges for traditional methods to identify reservoir fluids. Additionally, the imbalance in fluid categories leads to classification bias toward majority classes, reducing recognition performance for minority categories. To address these challenges, this paper proposes a Reservoir Fluid Identification method that combines an improved synthetic minority over-sampling technique (SMOTE) with a revisiting mobile convolutional neural network (CNN) from vision transformer (ViT) perspective (RepViT)-transformer hybrid model. Logging data is processed using a sliding window to generate sequence samples that preserve stratigraphic information while optimizing sample structure for CNN. The improved SMOTE algorithm employs a symmetric generation strategy to expand Minority Class Samples while maintaining data distribution characteristics, mitigating the class imbalance problem. The hybrid model integrates the local feature extraction of RepViT and the global dependency capturing capabilities of transformer, enhancing Classification Accuracy and Stability. Applied to logging data from the Dagang Oilfield, the method outperformed bidirectional long short-term memory (BiLSTM) and transformer models in both overall recognition performance and minority class identification, achieving 94.41% accuracy on the test set—4.92% and 10.24% higher than BiLSTM and transformer, respectively. These results demonstrate the method's effectiveness in identifying fluids in low-porosity, low-permeability reservoirs, offering a novel approach for fluid evaluation using conventional logging data.

Social Class and Personality: The Effects of Educational Mobility on Personality Trait Change

Transitioning into young adulthood often brings about significant changes in personality traits. However, the reasons behind these personality changes remain unclear. This study integrates insights from research on personality development and the psychology of social class to study how the construction of one’s social class identity in young adulthood might trigger changes in personality traits (i.e., Big Five, locus of control, and risk-taking). We tested our preregistered hypotheses in the context of educational mobility, using data from the German Socio-Economic Panel (SOEP) study ( N = 4,776). Specifically, we investigated personality changes of young adults whose parents did not go to university, comparing those who are educationally mobile (i.e., go to university) with those who do not during the study period. Overall, the results indicated that upward educational mobility only leads to changes in risk-taking. Theoretical implications for the psychology of class mobility (selection vs. socialization effects) are discussed.

Linguistic identity and insecurity in class of national languages and cultures in Cameroon

This article, which focuses on identity management and linguistic insecurity, focuses on national language and culture classes (LCN) and uses a communicative approach. We have positioned ourselves in the teaching pole of the didactic triangle. The results obtained highlight the linguistic insecurity of identity marked by the fault of the teacher and the correctness of the learner, the linguistic insecurity materialized specifically in this study by the orthographic, morphological and morphosyntactic dysfunctions that teachers face, and the consequent deviations in terms of distortion that this imprints on knowledge. The general finding of this study is that linguistic insecurity in the LCN classroom leads to an inversion of learner/teacher identities and a reduction in the latter's authority. Based on this observation, we have made proposals for pedagogical types of teacher training in an attempt to stem this phenomenon, which profoundly affects the teaching/learning process in national language and culture classes and leads to an obvious decay of knowledge.

Western Feminism and Indian Womenhood- A Postmodern Analysis

This paper presents a postmodern analysis of Western feminism and Indian womanhood, challenging the metanarrative approach frequently employed in feminist studies. By reconceptualising 'Feminist Theory' as 'Theories of Feminism', the study compares Western feminism and Indian womanhood as distinct theoretical frameworks rather than variations of a single narrative. The research critically examines the homogeneous understanding of "women" and explores how intersecting identities of gender, race, class, and nationality shape women's experiences and societal roles. Drawing on postmodern and postcolonial perspectives, the paper highlights the limitations of Western feminist ideals of individualism, equality, and autonomy when applied to diverse cultural contexts. In contrast, Indian womanhood is presented as a complex interplay of caste, religion, colonial history, and socio-economic factors, emphasising collective rights and cultural sensitivity. The study posits that Indian feminism aligns more closely with postmodern traditions, resisting the imposition of Western feminist frameworks and constructing a discourse that acknowledges the diversity of women's experiences within India. This comparative analysis contributes to a more nuanced understanding of feminist theories and their application across different cultural contexts.