Research In Field Research Articles

The Impact of Integrating Various Digital Games in EFL Classrooms on Soft Skills Development

It is axiomatic that research in the broader field of educational technology has highlighted the capacity of digital games to engage students actively in the learning process, promoting motivation and facilitating knowledge retention. However, the specific impact of digital games on soft skills development within EFL contexts remains an underexplored area. Hence, this article investigates the impact of integrating diverse digital games into EFL classrooms on the development of soft skills among Moroccan EFL students. This quasi-experimental longitudinal study aimed to investigate the relationship between participation in an online gaming club and the development of soft skills, specifically English communication skills and critical thinking skills, over a six-month period. The hypothesis posited that engaging in online gaming activities would lead to improvements in both English communication skills and critical thinking skills among participants. The findings suggested that engagement in online gaming has a positive impact on the development of soft skills such as English communication skills and critical thinking skills. The study contributes to the broader discourse on innovative pedagogical approaches in EFL contexts, shedding light on the potential of digital games as effective tools for fostering essential skills beyond language proficiency. The implications of the research extend to educators, curriculum designers, and policymakers seeking to enhance the holistic development of students in EFL classrooms.

Disentangling Glycan-Protein Interactions: Nuclear Magnetic Resonance (NMR) to the Rescue.

The interactions of glycans with proteins modulate many events related to health and disease. In fact, the establishment of these recognition events and their biological consequences are intimately related to the three-dimensional structures of both partners, as well as to their dynamic features and their presentation on the corresponding cell compartments. NMR techniques are unique to disentangle these characteristics and, indeed, diverse NMR-based methodologies have been developed and applied to monitor the binding events of glycans with their associate receptors. This protocol outlines the procedures to acquire, process and analyze two of the most powerful NMR methodologies employed in the NMR-glycobiology field, 1H-Saturation transfer difference (STD) and 1H,15N-Heteronuclear single quantum coherence (HSQC) titration experiments, which complementarily offer information from the glycan and protein perspective, respectively. Indeed, when combined they offer a powerful toolkit for elucidating both the structural and dynamic aspects of molecular recognition processes. This comprehensive approach enhances our understanding of glycan-protein interactions and contributes to advancing research in the chemical glycobiology field.

Determinants on the Successful Investment of Venture Capital

A successful venture capital investment means investors put money into projects or enterprises with high risks but potentially high returns. Academic staffs have been calling for further research on relevant information about venture capital, but there is no specific research about the determined factors of successful venture capital investment in the relevant literature. This paper may make you more aware of the influencing factors of venture capital, so as to help you make more comprehensive investment decisions or inspire you to conduct in-depth research in related fields. This paper finds the conclusion mainly through searching the relevant literature on venture capital and comparing it to the investment examples of individual companies. By analyzing the advantages of investors and the positive impact to the company, the paper continuously compares and evaluate and finally find out the three determined factors that have the greatest impact on venture capital investment. These are management team, valuation and deal structure respectively. Venture capital investment is an important way for small companies to further expand their scale, and it also reflects the changing trend of the world economy to a certain extent.

Evolutionary trends and research hotspots in electrochemical machining: A bibliometric analysis from 2010 to 2023

Electrochemical Machining has played an important role in solving manufacturing scenarios with difficult-to-machine materials, complex shapes, special requirements, and many other aspects. To systematically grasp the evolutionary trends, current status, and hotspots of research in the field of Electrochemical Machining, data analysis and mining of literature big data in the field of Electrochemical Machining from 2010 to 2023 were conducted, and graph visualization analysis was carried out. The research results indicate that: 1) Electrochemical Machining is highly valued by research institutions in various countries, with steady growth in research results and relatively high funding for research projects. China is in a leading position in terms of the number of papers and funding;2) A certain degree of cooperation has been formed globally around the research of Electrochemical Machining, and the cooperation between research institutions has obvious regional characteristics; 3) The research level of Electrochemical Machining has gradually deepened from theoretical research to interdisciplinary application research in materials science, energy, engineering, technology, and other fields. Research on machine learning based Electrochemical Machining has received increasing attention, and artificial neural networks have achieved new results in processing optimization applications. Artificial intelligence is the key technology and cutting-edge direction in the next stage of Electrochemical Machining research.

Advancements in the modification of magnesium-based hydrogen storage materials

Magnesium-based hydrogen storage materials represent a hydrogen storage technology with broad application prospects. As the global energy crisis and environmental pollution issues become increasingly severe, hydrogen, as a clean and efficient energy source, has garnered growing attention. Magnesium-based hydrogen storage, serving as a crucial means for storing and transporting hydrogen, is gaining prominence due to its abundant resources, low cost, low density, and high hydrogen storage density. However, challenges in terms of absorption/desorption rates, temperature, activation energy, and enthalpy during hydrogen application impede its development. To address these challenges, this paper systematically reviews current research on magnesium-based hydrogen storage materials, encompasses their types, characteristics, and hydrogen absorption mechanisms. Furthermore, it delves into the impacts of nanoscale dimensions, alloying, doping, and catalysis on the performance of magnesium-based materials. The aim is to provide valuable insights for research in related fields.

Revolutionary composites of silver and zinc oxide nanoparticles from Hardwickia binata: Unleashing their potent antioxidant and anticancer properties

In the present of the study, was utilized the leaf extract of H. binata to synthesize Ag-ZnO nanocomposites. The nanocomposites were characterized using various techniques, including UV, FTIR, XRD and SEM-EDX. Nanocomposites are known for their promising properties, such as mechanical, thermal, electrical and optical characteristics, which have attracted significant attention from both academia and industry, resulting in active research in various fields. The introduction of silver (Ag) into the nanocomposites enabled them to exhibit UV light absorption at 366 nm, while the presence of zinc confirmed the peak at 431 nm. XRD analysis was carried out to determine the crystal structure and composition of the materials. The antibacterial properties of the Ag-ZnO nanocomposites were evaluated using the disc diffusion method, revealing the highest inhibition against the bacteria strain Bacillus subtilis (19 mm) compared to other strains. Furthermore, the nanocomposites demonstrated strong antioxidant activity against DPPH (56%), ABTS (59%) and exhibited anti-inflammatory activity (62%). Additionally, the Ag-ZnO nanocomposites showed promising anti-cancer properties, as evidenced by their effectiveness against a skin cancer cell line (A431-69%). Moreover, the nanocomposites displayed significant photocatalytic activity, resulting in a 70% degradation rate. Overall, this study successfully synthesized Ag-ZnO nanocomposites that exhibited robust antibacterial, antioxidant, anti-inflammatory and anticancer properties. These findings suggest the potential application of these nanocomposites in cancer treatment and environmental studies, particularly in dye degradation.

Precision gene editing using deep learning: A case study of the CRISPR-Cas9 editor

This article reviews the application cases of CRISPR/Cas9 gene editing technology, as well as the challenges and limitations. Firstly, the application of CRISPR/Cas9 technology based on deep learning in predicting the targeting efficiency of sgRNA is introduced, and the steps of data acquisition, pre-processing and feature engineering are described in detail. It then discusses the non-specific cutting and cytotoxicity challenges of CRISPR/Cas9 technology, as well as strategies for solving these challenges using deep learning techniques. Finally, the paper emphasizes the importance of deep learning techniques to mitigate the cytotoxicity problems in CRISPR/Cas9 technology, and points out that the establishment of these models can improve the safety and efficiency of gene editing experiments, and provide important reference and guidance for research in related fields.

Inspiration4 data access through the NASA Open Science Data Repository

The increasing accessibility of commercial and private space travel necessitates a profound understanding of its impact on human health. The NASA Open Science Data Repository (OSDR) provides transparent and FAIR access to biological studies, notably the SpaceX Inspiration4 (I4) mission, which amassed extensive data from civilian astronauts. This dataset encompasses omics and clinical assays, facilitating comprehensive research on space-induced biological responses. These data allow for multi-modal, longitudinal assessments, bridging the gap between human and model organism studies. Crucially, community-driven data standards established by NASA’s OSDR Analysis Working Groups empower artificial intelligence and machine learning to glean invaluable insights, guiding future mission planning and health risk mitigation. This article presents a concise guide to access and analyze I4 data in OSDR, including programmatic access through GLOpenAPI. This pioneering effort establishes a precedent for post-mission health monitoring programs within space agencies, propelling research in the burgeoning field of commercial space travel’s impact on human physiology.

Comparative Genomics Unveils Functional Diversity, Pangenome Openness, and Underlying Biological Drivers among Bacillus subtilis Group.

The Bacillus subtilis group (Bs group), with Bacillus subtilis as its core species, holds significant research and economic value in various fields, including science, industrial production, food, and pharmaceuticals. However, most studies have been confined to comparative genomics analyses and exploration within individual genomes at the level of species, with few conducted within groups across different species. This study focused on Bacillus subtilis, the model of Gram-positive bacteria, and 14 other species with significant research value, employing comparative pangenomics as well as population enrichment analysis to ascertain the functional enrichment and diversity. Through the quantification of pangenome openness, this work revealed the underlying biological drivers and significant correlation between pangenome openness and various factors, including the distribution of toxin-antitoxin- and integrase-related genes, as well as the number of endonucleases, recombinases, repair system-related genes, prophages, integrases, and transfer mobile elements. Furthermore, the functional enrichment results indicated the potential for secondary metabolite, probiotic, and antibiotic exploration in Bacillus licheniformis, Bacillus paralicheniformis, and Bacillus spizizenii, respectively. In general, this work systematically exposed the quantification of pangenome openness, biological drivers, the pivotal role of genomic instability factors, and mobile elements, providing targeted exploration guidance for the Bs group.

Mapping the Global Spread of T. indotineae: An Update on Antifungal Resistance, Mutations, and Strategies for Effective Management.

The global spread of Trichophyton indotineae presents a pressing challenge in dermatophytosis management. This systematic review explores the current landscape of T. indotineae infections, emphasizing resistance patterns, susceptibility testing, mutational analysis, and management strategies. A literature search was conducted in November 2023 using Embase, PubMed, Scopus, and Web of Science databases. Inclusion criteria covered clinical trials, observational studies, case series, or case reports with T. indotineae diagnosis through molecular methods. Reports on resistance mechanisms, antifungal susceptibility testing, and management were used for data extraction. A total of 1148 articles were identified through the systematic search process, with 45 meeting the inclusion criteria. The global spread of T. indotineae is evident, with cases reported in numerous new countries in 2023. Tentative epidemiological cut-off values (ECOFFs) suggested by several groups provide insights into the likelihood of clinical resistance. The presence of specific mutations, particularly Phe397Leu, correlate with higher minimum inhibitory concentrations (MICs), indicating potential clinical resistance. Azole resistance has also been reported and investigated in T. indotineae, and is a growing concern. Nevertheless, itraconazole continues to be an alternative therapy. Recommendations for management include oral or combination therapies and individualized approaches based on mutational analysis and susceptibility testing. Trichophyton indotineae poses a complex clinical scenario, necessitating enhanced surveillance, improved diagnostics, and cautious antifungal use. The absence of established clinical breakpoints for dermatophytes underscores the need for further research in this challenging field.

Isolation of Myenteric and Submucosal Plexus from Mouse Gastrointestinal Tract and Subsequent Co-Culture with Small Intestinal Organoids.

Intestinal homeostasis results from the proper interplay among epithelial cells, the enteric nervous system (ENS), interstitial cells of Cajal (ICCs), smooth muscle cells, the immune system, and the microbiota. The disruption of this balance underpins the onset of gastrointestinal-related diseases. The scarcity of models replicating the intricate interplay between the ENS and the intestinal epithelium highlights the imperative for developing novel methods. We have pioneered a sophisticated tridimensional in vitro technique, coculturing small intestinal organoids with myenteric and submucosal neurons. Notably, we have made significant advances in (1) refining the isolation technique for culturing the myenteric plexus, (2) enhancing the isolation of the submucosal plexus-both yielding mixed cultures of enteric neurons and glial cells from both plexuses, and (3) subsequently co-culturing myenteric and submucosal neurons with small intestinal organoids. This co-culture system establishes neural innervations with intestinal organoids, allowing for the investigation of regulatory interactions in the context of gastrointestinal diseases. Furthermore, we have developed a method for microinjecting the luminal space of small intestinal organoids with fluorescently labeled compounds. This technique possesses broad applicability such as the assessment of intestinal permeability, transcytosis, and immunocytochemical and immunofluorescence applications. This microinjection method could be extended to alternative experimental setups, incorporating bacterial species, or applying treatments to study ENS-small intestinal epithelium interactions. Therefore, this technique serves as a valuable tool for evaluating the intricate interplay between neuronal and intestinal epithelial cells (IECs) and shows great potential for drug screening, gene editing, the development of novel therapies, the modeling of infectious diseases, and significant advances in regenerative medicine. The co-culture establishment process spans twelve days, making it a powerful asset for comprehensive research in this critical field.

Methods Showcase—Using PLSF-SEM in Business Communication Research

Structural equation modeling (SEM) is a data analysis method that is widely used in business communication research, as well as research in many other fields, when scholars need to test complex models with multiple outcomes, interactions, or operations across different situations. To date, however, researchers have had to choose between using covariance-based SEM, and dealing with convergence problems; or composite-based SEM, and facing serious methodological issues. This article describes a way to combine strong aspects of both SEM types through PLSF-SEM. By utilizing this novel method, empirical researchers can employ several of the same tests traditionally used in covariance-based SEM, as well as new tests that rely on latent variable estimates, in a succinct and scholarly way. PLSF-SEM builds on partial least squares (PLS) algorithms to generate correlation-preserving factors; the F refers to it being factor-based, as opposed to composite-based. A primer on the use of PLSF-SEM in business communication research is provided, based on an illustrative model inspired by motivating language theory, and where simulated data was analyzed with the software WarpPLS.

Circular economy in supply chain management: a framework for database tool development to enhance sustainability

ABSTRACT The study surveys circular economy (CE) theoretical building blocks, conceptualisation, and practical implementation within the supply chain. A narrative review synthesises the knowledge and maps research in the CE field. There are six major building blocks: waste management, industrial ecology, bioeconomy, cradle to cradle, green supply chain management, and product-service system. The paper examines their role and their overlapping activity within the supply chain. This study also generates a database containing 43 circular strategies, based on different stages in the supply chain. Each strategy is marked by its contribution towards CE, such as resource conservation, narrowing the loop, slowing the loop, or closing the loop. The database can assist actors to implement possible circular actions and policy makers to formulate supportive legislation. This study contributes to the contention of CE and its implementation.

Exploring Hybrid Learning Formats in a Nordic Master’s Programme in Visual Studies and Art Education

This paper reports from a project in progress that aims to develop a hybrid learning format that is sufficiently flexible to accommodate collaborative situations between students and teachers on two campuses and in collaborative situations off-campus. The project explores a current co-teaching opportunity that AAU and Aalto University are facing due to the start-up of the new Nordic joint degree master's program Visual Studies and Art Education (NM NoVA). A new physical space has been established at the AAU Copenhagen campus, that digitally facilitates a video-mediated, synchronous learning environment and a similar facilitation is accessible at Aalto University. Teachers and researchers join into a collaboration and conduct the project drawing theoretical knowledge generated in the field of synchronous learning formats and from research in the particular subject related field of digital technology and visual arts education. Furthermore, the project uses the participants’ practical experience from engaging in different synchronous learning formats shared and informed by discussions with peers. Inspired by Design-Based Research (DBR), researchers actively engage in the problem analysis, design, implementation, and analysis of the hybrid learning format. Rich descriptions of the program's evolution are obtained through participant observations, interviews, and a "photo-safari" capturing interactions among students, teachers, and technology.Analytically, the study explores the hybrid format as an enactment of space beyond geographical constraints, drawing inspiration from the concept of "apparatus" within new materialism. Three analytical themes emerge: knowledge-building practices, community-building practices, and longitudinal aspects, providing insights into how the hybrid format impacts collaboration, engagement, and the evolution of educational spaces. Preliminary findings reveal the diverse orientations and pedagogical discussions that emerge within these hybrid spaces, challenging the traditional notion of a "third space" and paving the way for new conceptualisations. The project anticipates further exploration of interdependencies within third spaces and the dynamics of student and teacher bodies within this evolving educational landscape.

Intelligent Environment using Extended Reality and Machine Learning to Improve Healthcare

Intelligent Environments (IEs) represent a paradigm shift in the way we interact with our surroundings, leveraging Ambient Intelligence (AmI) to create responsive and adaptive spaces that seamlessly integrate technology into the built environment. By harnessing sensors, actuators, and artificial intelligence, IEs have the potential to enhance human experience, improve efficiency, and promote sustainability across various domains, including smart homes, workplaces, cities, and healthcare facilities. This paper provides an overview of IE concepts, discusses key technologies and applications, explores design considerations, and outlines future directions for research and development in this dynamic field. This paper focuses on the study of the potential use of Extended Reality technologies in order to promote Green Health as well as their use in Smart Hospitals, one of the variants of Smart Environments, incorporating Machine Learning and Computer Vision, as a tool to support and improve healthcare, both from the point of view of the health professional and the patient, through a literature review and analysis of the current situation. Resulting in the elaboration of a conceptual model with the suggestion of technologies that can be used to achieve this scenario selected for their potential, and then the development of prototypes of parts of the conceptual model for Extended Reality Headsets as concept validation.

Effects of Multidimensional Carbon-Based Nanomaterials on the Low-Carbon and High-Performance Cementitious Composites: A Critical Review.

Cementitious composites are ubiquitous in construction, and more and more research is focused on improving mechanical properties and environmental effects. However, the jury is still out on which material can achieve low-carbon and high-performance cementitious composites. This article compares the mechanical and environmental performance of zero-dimensional fullerenes, one-dimensional carbon nanotubes (CNTs), two-dimensional graphene oxide (GO), and three-dimensional nano-graphite platelets (NGPs) on cementitious composites. The literature review shows that two-dimensional (2D) GO has the best mechanical and environmental performance, followed by 3D NGPs, 1D CNTs, and 0D fullerenes. Specifically, GO stands out for its lower energy consumption (120-140 MJ/kg) and CO2 emissions (0.17 kg/kg). When the optimal dosage (0.01-0.05 wt%) of GO is selected, due to its high specific surface area and strong adhesion to the matrix, the compressive strength of the cementitious composites is improved by nearly 50%. This study will help engineers and researchers better utilize carbon-based nanomaterials and provide guidance and direction for future research in related fields.

On the Low-Pressure Hysteresis (LPH) in Gas Sorption Isotherms of Porous Carbons.

This study investigates the origin of low-pressure hysteresis (LPH) in the adsorption and desorption of three different probe molecules: carbon dioxide, nitrogen, and argon, across various adsorption temperatures (from cryogenic to room temperature), and within five different carbon materials: synthetic carbons (pristine and one post-synthetically oxidized) and natural coal. Significant attention is dedicated to elucidating LPH in oxidized samples outgassed at various temperatures (120-350°C). Experimental results show that insufficient outgassing temperature can lead to unreliable data due to artificial LPH and significantly underestimated textural properties, primarily caused by porosity blockage from substances like moisture. Conversely, in samples where heteroatoms have a stabilizing effect on texture, such as natural coal, careful consideration of outgassing temperature is crucial due to the risk of thermal degradation. Other factors contributing to LPH are adsorption temperature, and especially, kinetic limitations at cryogenic temperatures for cellulose-based carbons. Minor factors responsible for LPH are the physical state of the sample (monolith vs powder) and the flexibility of the porous system, both studied by carbon dioxide sorption. This study constitutes an important piece in the evaluation of LPH, providing practical recommendations and underlining the importance of experimental design, with implications for further research in this complex field.

Error Function Optimization to Compare Neural Activity and Train Blended Rhythmic Networks.

We present a novel set of quantitative measures for "likeness" (error function) designed to alleviate the time-consuming and subjective nature of manually comparing biological recordings from electrophysiological experiments with the outcomes of their mathematical models. Our innovative "blended" system approach offers an objective, high-throughput, and computationally efficient method for comparing biological and mathematical models. This approach involves using voltage recordings of biological neurons to drive and train mathematical models, facilitating the derivation of the error function for further parameter optimization. Our calibration process incorporates measurements such as action potential (AP) frequency, voltage moving average, voltage envelopes, and the probability of post-synaptic channels. To assess the effectiveness of our method, we utilized the sea slug Melibe leonina swim central pattern generator (CPG) as our model circuit and conducted electrophysiological experiments with TTX to isolate CPG interneurons. During the comparison of biological recordings and mathematically simulated neurons, we performed a grid search of inhibitory and excitatory synapse conductance. Our findings indicate that a weighted sum of simple functions is essential for comprehensively capturing a neuron's rhythmic activity. Overall, our study suggests that our blended system approach holds promise for enabling objective and high-throughput comparisons between biological and mathematical models, offering significant potential for advancing research in neural circuitry and related fields.

Assessing the Efficacy of ChatGPT Versus Human Researchers in Identifying Relevant Studies on mHealth Interventions for Improving Medication Adherence in Patients With Ischemic Stroke When Conducting Systematic Reviews: Comparative Analysis.

ChatGPT by OpenAI emerged as a potential tool for researchers, aiding in various aspects of research. One such application was the identification of relevant studies in systematic reviews. However, a comprehensive comparison of the efficacy of relevant study identification between human researchers and ChatGPT has not been conducted. This study aims to compare the efficacy of ChatGPT and human researchers in identifying relevant studies on medication adherence improvement using mobile health interventions in patients with ischemic stroke during systematic reviews. This study used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Four electronic databases, including CINAHL Plus with Full Text, Web of Science, PubMed, and MEDLINE, were searched to identify articles published from inception until 2023 using search terms based on MeSH (Medical Subject Headings) terms generated by human researchers versus ChatGPT. The authors independently screened the titles, abstracts, and full text of the studies identified through separate searches conducted by human researchers and ChatGPT. The comparison encompassed several aspects, including the ability to retrieve relevant studies, accuracy, efficiency, limitations, and challenges associated with each method. A total of 6 articles identified through search terms generated by human researchers were included in the final analysis, of which 4 (67%) reported improvements in medication adherence after the intervention. However, 33% (2/6) of the included studies did not clearly state whether medication adherence improved after the intervention. A total of 10 studies were included based on search terms generated by ChatGPT, of which 6 (60%) overlapped with studies identified by human researchers. Regarding the impact of mobile health interventions on medication adherence, most included studies (8/10, 80%) based on search terms generated by ChatGPT reported improvements in medication adherence after the intervention. However, 20% (2/10) of the studies did not clearly state whether medication adherence improved after the intervention. The precision in accurately identifying relevant studies was higher in human researchers (0.86) than in ChatGPT (0.77). This is consistent with the percentage of relevance, where human researchers (9.8%) demonstrated a higher percentage of relevance than ChatGPT (3%). However, when considering the time required for both humans and ChatGPT to identify relevant studies, ChatGPT substantially outperformed human researchers as it took less time to identify relevant studies. Our comparative analysis highlighted the strengths and limitations of both approaches. Ultimately, the choice between human researchers and ChatGPT depends on the specific requirements and objectives of each review, but the collaborative synergy of both approaches holds the potential to advance evidence-based research and decision-making in the health care field.

How well do decision support systems help decision makers? An examination of adopting lean manufacturing processes

Choice is crucial for industrial enterprises as their success or failure may depend on it. Consequently, emerging technologies, especially Industry 4.0 (I4.0) are making precision decisions and enabling enriching collaborations with the industry’s computer-assisted decision support systems (DSSs). However, the arrival of Industry 4.0 poses challenges to this emerging application in terms of data variations and interconnectivity. A thorough search across Scopus, Web of Science, and Emerald Science databases yielded 2023 relevant documents. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), the top 250 academic documents were meticulously filtered and analysed in this study. The analysis resulted in the classification of the four main purposes of a DSS as data evaluation, optimisation, scheduling, and selection. The research also investigated the impact of DSSs on the performance of lean manufacturing (LM). Next, this research discussed the controversies with regard to the confidence, prejudice, and discrimination of users, discipline-based DSS application bias, as well as criticisms and suggestions for the future development of DSS, especially in the manufacturing industry. It is believed that, based on its novel findings, this work will pave the way for future research in the same field.