Research In Field Research Articles

The social media sentiment analysis framework: deep learning for sentiment analysis on social media

Researching public opinion can help us learn important facts. People may quickly and easily express their thoughts and feelings on any subject using social media, which creates a deluge of unorganized data. Sentiment analysis on social media platforms like Twitter and Facebook has developed into a potent tool for gathering insights into users' perspectives. However, difficulties in interpreting natural language limit the effectiveness and precision of sentiment analysis. This research focuses on developing a social media sentiment analysis (SMSA) framework, incorporating a custom-built emotion thesaurus to enhance the precision of sentiment analysis. It delves into the efficacy of various deep learning algorithms, under different parameter calibrations, for sentiment extraction from social media. The study distinguishes itself by its unique approach towards sentiment dictionary creation and its application to deep learning models. It contributes new insights into sentiment analysis, particularly in social media contexts, showcasing notable advancements over previous methodologies. The results demonstrate improved accuracy and deeper understanding of social media sentiment, opening avenues for future research and applications in diverse fields.

Taxonomic Evaluation of Zingiberaceae

The order Scitaminales is generally accepted as a well-knit assemblage of closely related and highly advanced monocotyledons and forms a natural group. Among the families of the order, the Zingiberaceae are the largest and are best represented in India, there being 21 genera including more than 200 species.The family is of special interest from the taxonomic stand point. On the basis of morphological differences, Schumann (1904) divided the Zingiberaceae into two subfamilies : the Zingiberoideae with many genera and Gostoidae with four genera namely, Costus (tropics), Monocostus (Peru), Dimerocostus (tropical America) and Tapeinochilus (Tabutu). The Costoideae differs from the rest of the family in showing vegetative and floral organization that is unique among the flowering plants. Therefore, morphologically, they form a natural group that deserves the status of a subfamily.It is accepted that taxonomy should be based on evidences from as many disciplines as possible in the final evaluation of its units. But among the Costoideae, only Costus has been studied in detail. As the other genera of the Costoideae agree with Costus in anatomy, Tomlinson (1956) considers it reasonable to assume that the features that serve to distinguish Costus from the Zingiberaceae are equally diagnostic for the Costoidae as a whole. A list of diagnostic characters obtained from various disciplines are given in table I. These evidences also reveal that Costoideae is distinct and forms a natural group deserving the rank of a subfamily.Certain-outstanding feature of the Costoideae are unique not only to the family, but also to the order Scitaminales as a whole. Perhaps, because of these Tomlinson (1956) has suggested that the Costoideae may possibly be given a family rank. However, although Tomlinson’s view is really encouraging, it is necessary to study the features of other genera of the Costoideae, with respect to various disciplines, in order to decide such an issue, instead of depending on the results obtained only on Costus.The Zingiberaceae is further subdivided into three tribes Globbeae, Hedychieae and Zingibereae on the basis of the nature of lateral staminodes and the ovary. For the taxonomic consideration of tribes, there are no adequate data. However, it can be said that further research in the divergent fields on many more genera may only help in solving the taxonomic problems.To a systematist the four families of the Scitaminales show an interesting floral organization, although they form a natural group because of certain morphological features common to all the families. A line of reduction in the number of stamens and ovules can be recognised in this order. The data available from various disciplines are too inadequate to verify the prevailing systematic arrangement of the families of the order. Nevertheless, the available embryological data consolidated in the table II unmistakably speak of a common ancestry of the families and their grouping in the order Scitaminales. At the same time certain distinguishing features of each family are probably suggestive of a family rank for each of them.

Constructing Ionic Interfaces for Stable Electrochemical CO2 Reduction.

The electrochemical CO2 reduction reaction (CO2RR) has emerged as a promising approach for sustainable carbon cycling and valuable chemical production. Various methods and strategies have been explored to boost CO2RR performance. One of the most promising strategies includes the construction of stable ionic interfaces on metallic or molecular catalysts using organic or inorganic cations, which has demonstrated a significant improvement in catalytic performance. The stable ionic interface is instrumental in adjusting adsorption behavior, influencing reactive intermediates, facilitating mass transportation, and suppressing the hydrogen evolution reaction, particularly under acidic conditions. In this Perspective, we provide an overview of the recent advancements in building ionic interfaces in the electrocatalytic process and discuss the application of this strategy to improve the CO2RR performance of metallic and molecular catalysts. We aim to convey the future trends and opportunities in creating ionic interfaces to further enhance carbon utilization efficiency and the productivity of CO2RR products. The emphasis of this Perspective lies in the pivotal role of ionic interfaces in catalysis, providing a valuable reference for future research in this critical field.

Light Intensity of Phosphorescent-Netting Pots and Determining Their Visibility to Snow Crab (Chionoecetes opilio) Using Visual Modeling Techniques

This study explores the visibility of phosphorescent-netting pots to snow crab (Chionoecetes opilio) using visual modeling techniques. Light emitted from such pots increases catch per unit effort, yet little is understood about the factors driving these higher catch rates. In this study, we measure pot light emission and snow crab visual acuity. Combining these data with estimates obtained in the literature for other biotic and abiotic factors, we model snow crab vision in relation to the pots. Utilizing these factors and environmental conditions, we derive a contrast ratio between the pot light and the ambient light. Findings reveal that the visibility of pot lights at 200-m depth depends primarily on solar angle (time of day) and time elapsed post-deployment. Additional factors influencing the vision of the pots include water column quality and benthic boundary layer turbidity. This study is the first to model the visual ecology of snow crab and the first to estimate snow crab visual acuity. These insights into snow crab visual ecology can potentially enhance fishing techniques, promote catch efficiency and sustainability, and help provide a path forward for visual ecology research in the fisheries science field.

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.

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.

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.

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.

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.

Flavonoids as Potential Natural Compounds for the Prevention and Treatment of Eczema.

Eczema is a systemic autoimmune disease characterized by inflammation and skin manifestation with a range of comorbidities that include physical and psychological disorders. Despite recent advancements in understanding the mechanisms involved in atopic dermatitis, current marketed products have shown varying results with more side effects. The present ob-jective of the research studies is to develop new agents for eczema that cut down the cost of the novel drugs available and also improve the efficacy with the least adverse effects. Natural compounds and medicinal plants have been traditionally used since ancient civilizations. Now-adays, research in the herbal field is at its peak. One such natural compound, flavonoid, was found to be beneficial for the treatment of eczema. This review describes the use of certain flavonoid products to prepare preparations suitable for the treatment of prophylaxis or eczema. This is especially true for prophylaxis or atopic eczema treatment. These compounds exhibit anti-inflammatory, anti-inflammatory, anti-inflammatory, and anti-inflammatory properties and are, therefore, used in treatments to prevent allergies, inflammation, and irritation to the skin. We also dock the flavonoid derivatives used with the protein associated with the inhibi-tion of eczema for better lead optimization. These preparations appear to be used for cosmetic, dermatological, or herbal remedies as a local application.

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.

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.

Enhanced Surface Properties of TiO2-Based Coatings via Stevia-Assisted Spark Suppression: Insights from Density Functional Theory Calculations

This study investigates the enhancement of surface properties in TiO2-based coatings on the Ti-6Al-4V alloy through micro-arc oxidation (MAO), employing stevia sugar as a novel additive. By incorporating stevia sugar into acetate–glycerophosphate–tetraethoxysilane solutions used in MAO treatment, the porous morphology of TiO2-based oxide layers is regulated. The incorporation of stevia moderates plasma discharge intensity, facilitating the formation of a uniform silicon-rich structure characterized by reduced porosity and pore size. This effect is attributed to the interaction between stevia and tetraethyl orthosilicate (TEOS), which modifies the TEOS hydrolysis process, thereby enhancing structural uniformity and stability while concurrently reducing plasma discharge intensity. Additionally, theoretical calculations offer a valuable understanding of the reactivity and interactions of stevia, TEOS, and their complex during the MAO process, laying the groundwork for further research and optimization in this promising field.

A Scholarly Dialog on Recent Trends in National Institutes of Health's Funding for the Thyroid Field.

Background: The National Institutes of Health (NIH) is the major funding agency for biomedical research in the United States. To initiate a scholarly dialog about research and career development in the thyroid field, here we reviewed recent trends in NIH funding for this area. We used the Research Portfolio Online Reporting Tool database to estimate the level of NIH extramural support during 2013-2022 (number of active grants/year and $amount/year weighed by the total number of active grants/year and $amount/year), provided by the NIH to the thyroid field. We determined that in 2013, the NIH supported ∼140 grants/year, totaling almost $50 million/year, the majority in the form of R01 grants. Within the thyroid field, support was evenly split between thyroid cancer and thyroid hormone metabolism and action subareas. In the subsequent years (2014-2022), the total number of active grants peaked at 150/year ($55 million) in 2014 but progressively decreased to about 100 active grants/year ($30 million) in 2022. This trend occurred while the NIH budget increased from $29 to $46 billion/year. Globally, the number of thyroid-related publications increased by ∼70% during the study period, and the fractional contribution of several countries remained relatively stable, except for China which increased by ∼600%. Remarkably, the fraction of thyroid-related publications in the United States sponsored by the NIH decreased from 5.5% to 3.1% of the global number. Conclusion: These results constitute a very concerning scenario for research and education in the thyroid field. We appeal to the NIH, the professional societies in endocrinology and thyroidology, and all other relevant stakeholders such as thyroid-related professionals and thyroid patients to engage in further discussions to identify the root causes of this trend and implement an action plan to stabilize and eventually reverse this situation.

Determining geo-disaster chains probabilities and disaster mitigation mode: A meta-analytical perspective

In a harsh geological environment, different geo-disasters might co-occur or be in a chain sequence. Currently, existing research in related fields documents numerous instances of disaster chains and responses, which provided this study with important materials for determining disaster chains probability and summarized the disaster prevention and mitigation (DPM) mode. In this study, through the depth of the geo-disaster chains cases and the DPM cases, enabled a mitigation mode analysis summary. We take the case of geo-disaster chains as an example for this study, and represent 20 typical geo-disaster chains by data fusion and structuring of 139 geo-disaster chains cases.The probability assessment of geo-disaster chains is crucial for risk management and regional resilience. We introduced a Bayesian network (BN) model to describe the process of geo-disaster chains formation to map the structure and assess the joint probability of geo-disaster chains. The BN results showed that the longer the disaster chains length, the smaller the probability of the chains occurring. Among these, the probability of “Collapses-landslides-debris flows–barrier lakes–flash floods” is the lowest (0.069). Through sensitivity analysis, it is found that precipitation is the most sensitive factor in the development of geo-disaster. Interpretive structural modeling (ISM) is employed to examine the DPM index system across three key aspects: disaster emergency preparation, disaster response, and disaster recovery, and the conceptualized interaction among these components. ISM results showed that government policy intervention and scientific DPM measures can effectively reduce the losses caused by the development of geo-disaster chains. This research combined the occurrence process of the disaster chains with the disaster response process, and determined the entire chains of characterization from disaster understanding to disaster response, and helped reduce the vulnerabilities caused by disasters.

Using Reference Publication Year Spectroscopy (RPYS) to analyze the research and publication culture in immunology

AbstractReference Publication Year Spectroscopy (RPYS) is a method to reveal the historical roots and landmark papers of a research field based on cited references data. RPYS visualizes how frequently cited references occur in the cited reference lists of papers published in the research field. Previous RPYS studies revealed various historical aspects of the research and publication culture in various fields of study (e.g., economy and philosophy of science). Employing RPYS, we examine a dataset of citing publications in immunology. We identify and analyze 20 cited publications occurring very frequently in the cited reference lists of these citing papers. The 20 publications shed light on the epistemic culture and the development of the field of immunology. Whereas 65% of the publications were contributions to the theoretical and empirical understanding of immunology, 35% were contributions to methodology.

Classification of Vascular Dementia on magnetic resonance imaging using deep learning architectures

Vascular Dementia is a severe disease that results from dead nerve cells’ accumulation in blood vessels. This affects the blood flow and impairs memory and decision-making abilities. Machine learning and deep learning have been used in detecting this disease. Nevertheless, their accuracy has been inconsistent, explaining why their utilization in diagnosing patients has led to poor performance. We developed several transfer learning architectures that improve classification accuracy and diagnosis performance in assessing vascular dementia. The process first entails the preprocessing of the dataset where a random selection ensures data representation is balanced. We used a dataset containing resting-state fMRI scans to split training, testing, and validation into 80%, 10%, and 10%. We employ different Convolutional Neural Network architectures such as VGG16, VGG19, DenseNet121, and InceptionResNetV2 to enhance classification.To, enhance these, we incorporated Rectified Linear Unit and leaky activation functions for the training phase to counteract problems associated with vanishing gradient common in deep learning tasks. Our methodology ensures effective information flow throughout different layers, which is essential for a divergent information hierarchy in medical information. As such, the specifics show that our approach achieved 84.67% in accuracy in the multi-classification, which is better than the current state-of-the-art research in the same field. Therefore, the result shows that transfer learning-based approaches are suitable when combined with strategic pre-processing and activation functions in improving the diagnosis of vascular dementia using MRI images.

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.