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Mobile learning (m-learning) is a crucial educational technology for teacher education due to its significant benefits and the development of mobile technology. This study's objective is to conduct a systematic review and present a recent synthesis of the m-learning literature from 2018 to 2023 in teacher education relating to subject publication year, geographic distribution, matter domains, mobile devices and technologies used, research methodologies used to examine the implementation of m-learning, results for pre-service teachers, as well as benefits and challenges of m-learning adoption. The study used the systematic review methodology and PRISMA guidelines. A list of 27 studies was included from several relevant studies in four, Google Scholar, Mendeley, ScienceDirect, and Scopus, databases using inclusion and exclusion criteria to evaluate the full text after screening the titles and abstracts. The results of this study show that m-learning has garnered interest in numerous nations worldwide, applied in different subject matter domains with the use of various mobile devices and technologies. More significantly, the findings show that using mobile learning to learn positively impacts how pre-service teachers develop their knowledge, skills, and attitudes. Additionally, adopting this learning style recently in teacher education has certain advantages and challenges, requiring lecturers, pre-service teachers, and institutions to have the necessary equipment for knowledge, skills and facilities to achieve efficiency. Consequently, the results of this study can be used as a guide for research on m-learning in the future and contribute to the body of knowledge about this pedagogical strategy for teacher training.

In this study, a nanostructured NdFeO3 material was synthesized via a simple process of the hydrolysis of Nd (III) and Fe (III) cations in hot water with 5% NaOH as a precipitating agent. According to the results of the thermal behaviors of each hydroxide, either containing Fe (III) or Nd (III), the perovskite type of neodymium orthoferrite NdFeO3 was simply synthesized by annealing a mixture of Fe (III) and Nd (III) hydroxides at 750 °C. The nanostructured NdFeO3 was obtained in spherical granules with diameters of around 30 nm. The magnetic properties of the material were a coercive force (Hc) of 136.76 Oe, a remanent magnetization (Mr) of 0.68 emu·g−1, and a saturation magnetization (Ms) of 0.79 emu·g−1.

Abstract The development of soft computing methods has had a significant influence on the subject of autonomous intelligent agriculture. This paper offers a system for autonomous greenhouse navigation that employs a fuzzy control algorithm and a deep learning-based disease classification model for tomato plants, identifying illnesses using photos of tomato leaves. The primary novelty in this study is the introduction of an upgraded Deep Convolutional Generative Adversarial Network (DCGAN) that creates augmented pictures of disease tomato leaves from original genuine samples, considerably enhancing the training dataset. To find the optimum training model, four deep learning networks (VGG19, Inception-v3, DenseNet-201, and ResNet-152) were carefully compared on a dataset of nine tomato leaf disease classes. These models have validation accuracy of 92.32%, 90.83%, 96.61%, and 97.07%, respectively, when using the original PlantVillage dataset. The system then uses an enhanced dataset with ResNet-152 network design to achieve a high accuracy of 99.69%, as compared to the original dataset with ResNet-152’s accuracy of 97.07%. This improvement indicates the use of the proposed DCGAN in improving the performance of the deep learning model for greenhouse plant monitoring and disease detection. Furthermore, the proposed approach may have a broader use in various agricultural scenarios, potentially altering the field of autonomous intelligent agriculture.

The article gives an in-depth understanding of the commitment to corporate social responsibility (CSR) on labour under the new generation of free trade agreements (FTAs) of the European Union (EU), and especially, the EU – Vietnam Free Trade Agreement (EVFTA). Following that, via the EU’s foreign and trade policies, this research clarifies the history as well as the evolution of the CSR commitments in the EU’s new generation FTAs. On that basis, this article also delves into the nature of CSR commitments related to labour in the EVFTA. Last but not least, the article reviews the current legislation of the EU and its member states as partners and suggests different policy implications that Vietnam and other developing nations might adopt to execute their CSR commitments in the context of global sustainable development.

This paper presents a study on behavior of vertical stress profiles in head-enlarged (PF) and conventional CDM columns obtained from field static load test (SLT) and numerical analyses. Results from the numerical SLT on PF and CDM columns installed in ideally elastoplastic soil show that, due to the configuration of the column head, plastic failure points happen around the CDM column at lower load levels than that for the case of the PF column, and the load on PF column is showed to transfer to the soil more pronouncedly than that on the CDM column. The stress distribution and load – settlement curves obtained from the numerical analyses on the instrumented PF columns in general agree well with that obtained from the field tests. The induced stress profiles obtained from both numerical analyses and measured data show that there exists a major peak point of concentrated stress at the bottom of the cone section. This concentrated stress point may make the columns severely cracked and failed and this must be well aware in the design of bearing capacity of the PF columns.

Abstract Pangenome inference is an indispensable step in bacterial genomics, yet its scalability poses a challenge due to the rapid growth of genomic collections. This paper presents PanTA, a software package designed for constructing pangenomes of large bacterial datasets, showing unprecedented efficiency levels multiple times higher than existing tools. PanTA introduces a novel mechanism to construct the pangenome progressively without rebuilding the accumulated collection from scratch. The progressive mode is shown to consume orders of magnitude less computational resources than existing solutions in managing growing datasets. The software is open source and is publicly available at https://github.com/amromics/panta and at 10.6084/m9.figshare.23724705 .

AbstractCareer guidance, which strongly influences the world labor market and social structure, is studied in many countries on different levels. This study aims to explore the number, growth trajectories, and geographic distribution of studies on Career guidance in general schools and identify prominent influential authors, sources, publications, and new research issues. Bibliometric analysis is employed to analyze all 382 publications on the topic of Career guidance in general schools in the Scopus database. The data shows that the volume of scientific publications in this field remained steady from 1964 to 2010, while the period from 2010 to 2022 experienced a significant increase. There is a total of 2412 authors participating in research on this topic, from 1609 institutions, in 98 different countries and regions. Besides the studies of American, Italian, and Russian scholars, researchers in the Netherlands, the United Kingdom, and Australia also play a notable role in this topic. The most cited and notable journal on this topic is the British Journal of Guidance and Counseling. Recently, the most prevalent research topics include influencing general school students to prepare for their career readiness, and the opportunity to create social justice in understanding and deciding on career options.

Background Subtraction (BgS) is a widely researched technique to develop online Change Detection algorithms for static video cameras. Many BgS methods have employed the unsupervised, adaptive approach of Gaussian Mixture Model (GMM) to produce decent backgrounds, but they lack proper consideration of scene semantics to produce better foregrounds. On the other hand, with considerable computational expenses, BgS with Deep Neural Networks (DNN) is able to produce accurate background and foreground segments. In our research, we blend both approaches for the best. First, we formulated a network called Convolutional Density Approximation (CDA) for direct density estimation of background models. Then, we propose a self-supervised training strategy for CDA to adaptively capture high-frequency color distributions for the corresponding backgrounds. Finally, we show that background models can indeed assist foreground extraction by an efficient Neural Motion Subtraction (NeMos) network. Our experiments verify competitive results in the balance between effectiveness and efficiency.

The escalating challenge of antibiotic resistance has driven the innovation of new antibacterial and antifouling materials. Recent developments focus on nature-inspired topographical engineering and nanostructured surfaces to combat resistant bacteria. This review discusses these advances, emphasizing the potential of nanoantibiotics and biopolymers. Nanoantibiotics revitalize drug effectiveness by encapsulating them in nanoparticles, presenting a new strategy to fight pathogens. Biopolymers, eco-friendly and biodegradable, emerge as a sustainable alternative, with applications in food safety and beyond. The exploration of these materials signifies a leap in design, fabrication, and the possibility of cost-effective, large-scale production, highlighting a promising avenue for commercial applications to tackle antibiotic resistance and biofouling effectively.