Month: July 2024

Abstract

The available classifications of self-regulated learners may not be applicable to second or foreign language writing due to the contextual nature of self-regulated learning. This study intended to fill the gap by exploring the profiles of English as a foreign language (EFL) learners’ writing self-regulation and their association with writing-relevant individual differences. A total of 391 tertiary students from Southwest China were recruited to participate in the current study, including freshmen, sophomores, and juniors. Their writing self-regulation was measured by the Writing Strategies for Self-Regulated Learning Questionnaire. Latent profile analyses discovered two profiles of self-regulated learners in EFL writing: “highly self-regulated group” and “moderately self-regulated group”. Moreover, ANOVA and Welch’s Test showed that the participants assigned to the two profiles differed significantly in L2 grit, writing achievement goals, and writing self-efficacy rather than language aptitude and working memory. Perseverance of effort, mastery goals, and self-regulatory self-efficacy are found to predict profile membership significantly. Additionally, the results of path analyses revealed that the profiles varied in the predictive effect of individual differences on EFL learners’ writing regulation. These findings contributed to furthering our understanding of classification of self-regulated learners and the role of individual differences in the classification.

Abstract

Accurate breed identification in dairy cattle is essential for optimizing herd management and improving genetic standards. A smart method for correctly identifying phenotypically similar breeds can empower farmers to enhance herd productivity. A convolutional neural network (CNN) based model was developed for the identification of Sahiwal and Red Sindhi cows. To increase the classification accuracy, first, cows’s pixels were segmented from the background using CNN model. Using this segmented image, a masked image was produced by retaining cows' pixels from the original image while eliminating the background. To improve the classification accuracy, models were trained on four different images of each cow: front view, side view, grayscale front view, and grayscale side view. The masked images of these views were fed to the multi-input CNN model which predicts the class of input images. The segmentation model achieved intersection-over-union (IoU) and F1-score values of 81.75% and 85.26%, respectively with an inference time of 296 ms. For the classification task, multiple variants of MobileNet and EfficientNet models were used as the backbone along with pre-trained weights. The MobileNet model achieved 80.0% accuracy for both breeds, while MobileNetV2 and MobileNetV3 reached 82.0% accuracy. CNN models with EfficientNet as backbones outperformed MobileNet models, with accuracy ranging from 84.0% to 86.0%. The F1-scores for these models were found to be above 83.0%, indicating effective breed classification with fewer false positives and negatives. Thus, the present study demonstrates that deep learning models can be used effectively to identify phenotypically similar-looking cattle breeds. To accurately identify zebu breeds, this study will reduce the dependence of farmers on experts.

Abstract

Background

Intellectual disability (ID) is a neurodevelopmental condition affecting around 2% of children and young adults worldwide, characterized by deficits in intellectual functioning and adaptive behavior. Genetic factors contribute to the development of ID phenotypes, including mutations and structural changes in chromosomes. Pathogenic variants in the HCFC1 gene cause X-linked mental retardation syndrome, also known as Siderius type X-linked mental retardation. The MN1 gene is necessary for palate development, and mutations in this gene result in a genetic condition called CEBALID syndrome.

Methods

Exome sequencing was used to identify the disease-causing variants in two affected families, A and B, from various regions of Pakistan. Affected individuals in these two families presented ID, developmental delay, and behavioral abnormalities. The validation and co-segregation analysis of the filtered variant was carried out using Sanger sequencing.

Results

In an X-linked family A, a novel hemizygous missense variant (c.5705G > A; p.Ser1902Asn) in the HCFC1 gene (NM_005334.3) was identified, while in family B exome sequencing revealed a heterozygous nonsense variant (c.3680 G > A; p. Trp1227Ter) in exon-1 of the MN1 gene (NM_032581.4). Sanger sequencing confirmed the segregation of these variants with ID in each family.

Conclusions

The investigation of two Pakistani families revealed pathogenic genetic variants in the HCFC1 and MN1 genes, which cause ID and expand the mutational spectrum of these genes.

Abstract

While traditional family portraits have always been value-laden constructs, generative artificial intelligence’s text-to-image tools are adding new postdigital complexities to already sophisticated photo and video editing processes. To explore what is being produced or generated by text-to-image generative artificial intelligence, this postdigital semiotic case study assembles a comparative corpus of family portraits. Analysis of portraits from three different platforms indicates that artificial intelligence is not entirely devoid of agency but rather a type of language-user entangled within complex processes of technosemiosis resulting in a series of hallucinations. Theorising postulates at least three distinct types of visual hallucinations, including hallucinations as perceived errors, hallucinations as a distorted view of reality, and hallucinations as regimes of Whiteness. In view of the ethnocentric ‘myths’ emerging from the corpus of hallucinations, the study articulates ethical imperatives for developing human-in-the-loop guardrails. In the process, the study debunks not merely the seeming ‘magic’ of artificial intelligence’s algorithmic black box but also the quick fixes of predetermined theoretical approaches. As a result, the study raises broader issues concerning the openness of postdigital research which is required to address the multifaceted contagion of racial capitalism. While navigating these opaque constellations, the study reinforces the axiological scope of the postdigital semiotic framework to open theorising beyond scholarly performativity and epistemic complacency.

Abstract

Background

During COVID-19, the number of people experiencing homelessness increased, further exacerbating the violation of basic rights necessary for human survival. Thus, the study aimed to investigate the inequalities associated with income loss and food insecurity among people experiencing homelessness during the COVID-19 pandemic in Brazil.

Methods

A cross-sectional study was conducted in 24 Brazilian state capitals and the Federal District among the homeless population using a validated instrument. Descriptive analyses and binary logistic regressions were performed.

Results

Among 1512 homeless participants (median age:37, range:18–89), 39.4% had incomplete primary education, 83.7% earned below minimum wage or had no income, 56.5% received government aid, and 87.1% used the Sistema Único de Saúde (Brazilian Public Health System-SUS). During the pandemic, 42.0% faced food difficulties, and 26.3% experienced income loss. Across Brazilian macroregions, lack of employment affected four regions, with high SUS dependency and food scarcity. In the North (72.7%) and Northeast (51.9%), most lacked government aid, while in the Midwest (51.6%), temporary income loss prevailed. In regression analyses, men, black/mixed race, those married or in a stable union, government aid recipients, and SUS users had greater difficulty acquiring food during COVID-19. Men with incomplete high school or higher education and income above minimum wage were less likely to suffer temporary income loss, and black/mixed race individuals and those living on the streets were more likely.

Conclusions

The study showed how socioeconomic factors increased income loss and food acquisition difficulties during COVID-19, exacerbating vulnerability and social inequities for the people experiencing homelessness.

Abstract

Chemical oxidation coupled with microbial remediation has attracted widespread attention for the removal of polycyclic aromatic hydrocarbons (PAHs). Among them, the precise evaluation of the feasible oxidant concentration of PAH-contaminated soil is the key to achieving the goal of soil functional ecological remediation. In this study, phenanthrene (PHE) was used as the target pollutant, and Fe²⁺-activated persulphate (PS) was used to remediate four types of soils. Linear regression analysis identified the following important factors influencing remediation: PS dosage and soil PHE content for PHE degradation, Fe²⁺ dosage, hydrolysable nitrogen (HN), and available phosphorus for PS decomposition. A comprehensive model of “soil characteristics-oxidation conditions-remediation effect” with a high predictive accuracy was constructed. Based on model identification, Pseudomonas aeruginosa GZ7, which had high PAHs degrading ability after domestication, was further applied to coupling repair remediation. The results showed that the optimal PS dose was 0.75% (w/w). The response relationship between soil physical, chemical, and biological indicators at the intermediate interface and oxidation conditions was analysed. Coupled remediation effects were clarified using microbial diversity sequencing. The introduction of Pseudomonas aeruginosa GZ7 stimulated the relative abundance of Cohnella, Enterobacter, Paenibacillus, and Bacillus, which can promote material metabolism and energy transformation during remediation.

Abstract

This article presents a dialogue on the topic of Global Citizenship Education (GCE) in relation to service to society, inclusion, and equity. It features Hans de Wit, professor emeritus, distinguished fellow, and former director of the Center for International Higher Education at Boston College in the United States, and Emiliano Bosio, guest-editor of Prospects and director of the Global Citizenship Education Interview Series. Because of his insights and perspective, Professor de Wit was honored with the A. Noam Chomsky North Star Lifetime Achievement Global Connections Award by Star Scholars (the Society of Transnational Academic Scholars) in 2021. The objective of this discussion is to offer a critical view of GCE, delving into the dynamic interplay among global citizenship, the internationalization of higher education, societal service, inclusiveness, and equity. Professor de Wit sheds light on the intricate origins and historical context of global citizenship and internationalization. This exploration centers on concepts like attaining global learning for all students and the future of internationalization in the post-Covid-19 landscape and offers insights that enrich our understanding of the complex foundations and historical development of GCE.

Abstract  

Under the impact of colonial powers from capitalist nations, modern China has experienced significant transformations in its economic and geographic landscape. Coastal ports have emerged as crucial gateways for China’s engagement with the capitalist world. The Bohai Bay region in contemporary China holds immense importance due to the influence of Beijing as the capital city and the geopolitical dynamics of East Asia. This area witnessed intense competition among various capitalist countries to establish ports and trade markets. Eventually, Britain and Japan successively emerged as dominant players in international trade within the Bohai Bay ports. Backed by favorable port conditions and financial and technological support from colonial powers, ports such as Qingdao, Dalian, and Tianjin rapidly ascended and became regional centers within the Bohai Bay region. Through control over customs operations, colonial powers gained mastery over the hinterland trade markets connected to these ports. The development of railway and road networks facilitated a two-way transportation system, connecting colonial countries, ports, railway hubs, traffic nodes, towns, and counties. Consequently, the spatial pattern of intensive agricultural production in modern China began to take shape under the influence of port trade and railway development. Ports served as critical trade transfer stations for capitalist nations to exploit China’s resources and unload industrial products.