Effective Tool Research Articles

Enhancing students' writing paragraphs through Canva Magic AI

This research aimed to investigate the influence of Canva Magic AI on improving students' writing quality and to gauge their perceptions of using the tool. The study utilized a quantitative, pre-experimental One-Group Pretest–Posttest Design. Eight English literature students were selected using convenience sampling from an Intermediate English Grammar and Paragraph Writing class at STBA Persada Bunda during the 2023-2024 academic year. Data were collected via a writing test and a questionnaire. The findings revealed a significant improvement in students' writing quality after using Canva Magic AI, with a p-value of 0.012 from the Wilcoxon signed-rank test. Notable enhancements were observed across various writing components, including better organization, with more students receiving "Excellent to Good" ratings and the complete elimination of the "Not College Level Work" category. Logical development of ideas component showed significant improvement, with more students achieving higher ratings and lower ratings decreasing to zero. Grammar quality also improved markedly, with increases in higher ratings and decreases in lower ones. Progress was observed in punctuation, spelling, mechanics, and the style and quality of expression, with significant boosts in higher ratings and reductions in lower performance levels. Furthermore, students' perceptions of Canva Magic AI were highly positive, with mean ratings consistently above 4.0 on a 5-point scale. They acknowledged the tool's effectiveness in improving their writing skills and content creation, although some reported variability in experiences with errors, indicating a need for further refinement. The research underscores the effectiveness of Canva Magic AI as a supplementary educational tool, demonstrating its potential to enhance writing skills and overall quality. The implications for educational practices suggest integrating AI tools like Canva Magic AI into curricula to support and improve students' writing proficiency, fostering a more engaging and efficient learning environment.

Image Collage on Arbitrary Shape via Shape-Aware Slicing and Optimization.

Image collage is a very useful tool for visualizing an image collection. Most of the existing methods and commercial applications for generating image collages are designed on simple shapes, such as rectangular and circular layouts. This greatly limits the use of image collages in some artistic and creative settings. Although there are some methods that can generate irregularly-shaped image collages, they often suffer from severe image overlapping and excessive blank space. This prevents such methods from being effective information communication tools. In this article, we present a shape slicing algorithm and an optimization scheme that can create image collages of arbitrary shapes in an informative and visually pleasing manner given an input shape and an image collection. To overcome the challenge of irregular shapes, we propose a novel algorithm, called Shape-Aware Slicing, which partitions the input shape into cells based on medial axis and binary slicing tree. Shape-Aware Slicing,which is designed specifically for irregular shapes, takes human perception and shape structure into account to generate visually pleasing partitions. Then, the layout is optimized by analyzing input images with the goal of maximizing the total salient regions of the images. To evaluate our method, we conduct extensive experiments and compare our results against previous work. The evaluations show that our proposed algorithm can efficiently arrange image collections on irregular shapes and create visually superior results than prior work and existing commercial tools.

Computer Simulation for Effective Pharmaceutical Kinetics and Dynamics: A Review.

Computer-based modelling and simulation are developing as effective tools for supplementing biological data processing and interpretation. It helps to accelerate the creation of dosage forms at a lower cost and with the less human effort required to conduct the work. This paper aims to provide a comprehensive description of the different computer simulation models for various drugs along with their outcomes. The data used are taken from different sources, including review papers from Science Direct, Elsevier, NCBI, and Web of Science from 1995-2020. Keywords like - pharmacokinetic, pharmacodynamics, computer simulation, whole-cell model, and cell simulation, were used for the search process. The use of computer simulation helps speed up the creation of new dosage forms at a lower cost and less human effort required to complete the work. It is also widely used as a technique for researching the structure and dynamics of lipids and proteins found in membranes. It also facilitates both the diagnosis and prevention of illness. Conventional data analysis methods cannot assess and comprehend the huge amount, size, and complexity of data collected by in vitro, in vivo</i>, and ex vivo</i> experiments. As a result, numerous in silico</i> computational e-resources, databases, and simulation software are employed to determine pharmacokinetic (PK) and pharmacodynamic (PD) parameters for illness management. These techniques aid in the provision of multiscale representations of biological processes, beginning with proteins and genes and progressing through cells, isolated tissues and organs, and the whole organism.

PaRaVis: An automatic Python graphical package for ensemble analysis of plant beta diversity using remote sensing proxies

The ongoing impacts of climatic changes, coupled with intensified human activities, are leading to a significant loss of plant diversity, prompting urgent calls for comprehensive monitoring of forest ecosystems. This is particularly concerning in regions with the encroachment of human activities into forest zones, and inadequate regulations that primarily view forests as a source of timber without fully accounting for their critical roles in biodiversity. These issues highlight the need for effective geospatial approaches to support conservation strategies and sustained monitoring of plant diversity. The majority of remote sensing studies of plant diversity have focused on alpha-diversity, while beta-diversity plays an important role in providing a comprehensive picture of biodiversity patterns across scales. Among various remote sensing proxies, Rao's Q index stands out as a reliable metric to quantify beta-diversity using multispectral remote sensing indices. However, a holistic approach that can incorporate multiple remote sensing indices and enable comparative analysis using graphical means with computational efficiency is lacking. In response, we developed PaRaVis, an open-source Python-based graphical package for deriving spectral diversity from multispectral remote sensing datasets as a proxy for functional diversity using Rao's Q index. This tool encompasses all the necessary steps for parallelized computation, visualization, and analysis of Rao's index from either single or multiple multispectral indices. It is capable of calculating and visualizing 75 vegetation indices (VIs), from a raster scene, followed by calculating Rao's Q both unidimensionally and multidimensionally in parallel. PaRaVis also offers features for visualizing, analyzing, and comparing Rao's Q outputs using statistical performance diagnostics. It provides a unique means to infer diversity patterns in space and time and is therefore invaluable for researchers or organizations involved with plant diversity monitoring, especially those with limited data analysis or computer programming experience. To demonstrate the tool's effectiveness, we analyzed plant diversity patterns using satellite-derived spectral heterogeneity measures in forest sites within the Hyrcanian Forests of Iran and sites located in Germany. The first case study focused on UNESCO Natural Heritage sites. Our analysis revealed that employing EVI, SR3, and TCI indices as inputs for the multidimensional Rao's Q yielded higher performance in monitoring more heterogeneous forests compared to the unidimensional mode. In the second case study, we utilized field Species Richness and Shannon-Wiener diversity indices to evaluate PaRaVis and our method. We found that using a multi-temporal, multi-index approach enhances the results compared to multi-seasonal and classical Rao based on single time.

Development of Promising Interventions to Improve Human Papillomavirus Vaccination in a School-Based Program in Quebec, Canada: Results From a Formative Evaluation Using a Mixed Methods Design.

Despite the availability of school-based human papillomavirus (HPV) vaccination programs, disparities in vaccine coverage persist. Barriers to HPV vaccine acceptance and uptake include parental attitudes, knowledge, beliefs, and system-level barriers. A total of 3 interventions were developed to address these barriers: an in-person presentation by school nurses, an email reminder with a web-based information and decision aid tool, and a telephone reminder using motivational interviewing (MI) techniques. Here we report on the development and formative evaluation of interventions to improve HPV vaccine acceptance and uptake among grade 4 students' parents in Quebec, Canada. In the summer of 2019, we conducted a formative evaluation of the interventions to assess the interventions' relevance, content, and format and to identify any unmet needs. We conducted 3 focus group discussions with parents of grade 3 students and nurses. Interviews were recorded, transcribed, and analyzed for thematic content using NVivo software (Lumivero). Nurses received training on MI techniques and we evaluated the effect on nurses' knowledge and skills using a pre-post questionnaire. Descriptive quantitative analyses were carried out on data from questionnaires relating to the training. Comparisons were made using the proportions of the results. Finally, we developed a patient decision aid using an iterative, user-centered design process. The iterative refinement process involved feedback from parents, nurses, and experts to ensure the tool's relevance and effectiveness. The evaluation protocol and data collection tools were approved by the CHU (Centre Hospitalier Universitaire) de Québec Research Ethics Committee (MP-20-2019-4655, May 16, 2019). The data collection was conducted from April 2019 to March 2021. Following feedback (n=28) from the 3 focus group discussions in June 2019, several changes were made to the in-person presentation intervention. Experts (n=27) and school nurses (n=29) recruited for the project appreciated the visual and simplified information on vaccination in it. The results of the MI training for school nurses conducted in August 2019 demonstrated an increase in the skills and knowledge of nurses (n=29). School nurses who took the web-based course (n=24) filled out a pretest and posttest questionnaire to evaluate their learning. The rating increased by 19% between the pretest and posttest questionnaires. Several changes were made between the first draft of the web-based decision-aid tool and the final version during the summer of 2019 after an expert consultation of experts (n=3), focus group participants (n=28), and parents in the iterative process (n=5). More information about HPV and vaccines was added, and users could click if more detail is desired. We developed and pilot-tested 3 interventions using an iterative process. The interventions were perceived as potentially effective to increase parents' knowledge and positive attitudes toward HPV vaccination, and ultimately, vaccine acceptance. Future research will assess the effectiveness of these interventions on a larger scale.

To establish a prognostic model of epidermal growth factor receptor mutated non-small cell lung cancer patients based on Least Absolute Shrinkage and Selection Operator regression.

There is currently a shortage of effective diagnostic tools that are used for identifying long-term survival among non-small cell lung cancer (NSCLC) patients with epidermal growth factor receptor (EGFR) mutations. This research utilized the development of a prognostic model to assist clinicians in forecasting the survival over 24 months. In Phase III and IV those patients who were diagnosed with EGFR mutation from January 2018 to June 2022 were enrolled into the lung cancer group of Thoracic Surgery Department of Hebei Provincial People's Hospital. Long-run survival was stated as survival for 24 months after being diagnosed. A multivariate prognostic pattern was constructed by means of internal validation and binary logistic regression by bootstrapping. One nomogram was created with a view to boosting the explanation and applicability of the pattern. A total of 603 patients with EGFR mutation were registered. Elements linked to the whole survival beyond 24 months were age (OR 6.15); female (OR 1.79); functional status (ECOG 0-1) (OR 5.26); Exon 20 insertion mutation deletion (OR 2.08); No central nervous system metastasis (OR 2.66), targeted therapy (OR 0.43); Immunotherapy (OR 0.24). The model has good internal validation. Seven pretreatment clinicopathological variables predicted survival over 24 months. That pattern owns a great discriminative capability. It is hypothesized that this pattern is capable of assisting in selecting the optimal treatment sequence for NSCLC patients with EGFR mutations.

A Blockchain-Based Model for Traceability and Quality Management of Raw Materials in Clothing Products

In this study, we employ blockchain-based traceability technology to construct a comprehensive model for raw material traceability and quality management in clothing products. Additionally, we investigate engineered water nanostructures (EWNS) for their antibacterial, hydrophilic, and anti-pilling effects on textiles. The EWNS can serve as an effective tool for monitoring quality, enabling precise tracking and verification of nanomaterials during clothing production. The research validated the traceability performance of the blockchain-based model for raw material traceability and quality management in clothing products, achieving a remarkable traceability accuracy of 99.92% and an exceptionally low traceability response time of merely 1.83 seconds. Moreover, after being treated with EWNS, antibacterial rates against E. coli and S. aureus in clothing antibacterial quality management reached 99.9% and 99.0%, respectively. In the hydrophilicity test, EWNS was applied to clothing for a duration of 1 to 7 hours, resulting in a significant reduction in the wetting time of clothing from 10.8 seconds to 1.2 seconds. In the anti-pilling tests, there was a notable enhancement in the effectiveness of anti-pilling properties after EWNS treatment. In summary, our model has achieved remarkable outcomes in material traceability and quality management, offering consumers and the clothing industry a reliable method to ensure product quality and traceability, while simultaneously improving performance aspects such as antibacterial efficacy, hydrophilicity, and resistance against pilling.

ToxMPNN: A deep learning model for small molecule toxicity prediction.

Machine learning (ML) has shown a great promise in predicting toxicity of small molecules. However, the availability of data for such predictions is often limited. Because of the unsatisfactory performance of models trained on a single toxicity endpoint, we collected toxic small molecules with multiple toxicity endpoints from previous study. The dataset comprises 27 toxic endpoints categorized into seven toxicity classes, namely, carcinogenicity and mutagenicity, acute oral toxicity, respiratory toxicity, irritation and corrosion, cardiotoxicity, CYP450, and endocrine disruption. In addition, a binary classification Common-Toxicity task was added based on the aforementioned dataset. To improve the performance of the models, we added marketed drugs as negative samples. This study presents a toxicity predictive model, ToxMPNN, based on the message passing neural network (MPNN) architecture, aiming to predict the toxicity of small molecules. The results demonstrate that ToxMPNN outperforms other models in capturing toxic features within the molecular structure, resulting in more precise predictions with the ROC_AUC testing score of 0.886 for the Toxicity_drug dataset. Furthermore, it was observed that adding marketed drugs as negative samples not only improves the predictive performance of the binary classification Common-Toxicity task but also enhances the stability of the model prediction. It shows that the graph-based deep learning (DL) algorithms in this study can be used as a trustworthy and effective tool to assess small molecule toxicity in the development of new drugs.

Randomizing Human Brain Function Representation for Brain Disease Diagnosis.

Resting-state fMRI (rs-fMRI) is an effective tool for quantifying functional connectivity (FC), which plays a crucial role in exploring various brain diseases. Due to the high dimensionality of fMRI data, FC is typically computed based on the region of interest (ROI), whose parcellation relies on a pre-defined atlas. However, utilizing the brain atlas poses several challenges including 1) subjective selection bias in choosing from various brain atlases, 2) parcellation of each subject's brain with the same atlas yet disregarding individual specificity; 3) lack of interaction between brain region parcellation and downstream ROI-based FC analysis. To address these limitations, we propose a novel randomizing strategy for generating brain function representation to facilitate neural disease diagnosis. Specifically, we randomly sample brain patches, thus avoiding ROI parcellations of the brain atlas. Then, we introduce a new brain function representation framework for the sampled patches. Each patch has its function description by referring to anchor patches, as well as the position description. Furthermore, we design an adaptive-selection-assisted Transformer network to optimize and integrate the function representations of all sampled patches within each brain for neural disease diagnosis. To validate our framework, we conduct extensive evaluations on three datasets, and the experimental results establish the effectiveness and generality of our proposed method, offering a promising avenue for advancing neural disease diagnosis beyond the confines of traditional atlas-based methods. Our code is available at https://github.com/mjliu2020/RandomFR.

Tensor Ring Decomposition Guided Dictionary Learning for OCT Image Denoising.

Optical coherence tomography (OCT) is a non-invasive and effective tool for the imaging of retinal tissue. However, the heavy speckle noise, resulting from multiple scattering of the light waves, obscures important morphological structures and impairs the clinical diagnosis of ocular diseases. In this paper, we propose a novel and powerful model known as tensor ring decomposition-guided dictionary learning (TRGDL) for OCT image denoising, which can simultaneously utilize two useful complementary priors, i.e., three-dimensional low-rank and sparsity priors, under a unified framework. Specifically, to effectively use the strong correlation between nearby OCT frames, we construct the OCT group tensors by extracting cubic patches from OCT images and clustering similar patches. Then, since each created OCT group tensor has a low-rank structure, to exploit spatial, non-local, and its temporal correlations in a balanced way, we enforce the TR decomposition model on each OCT group tensor. Next, to use the beneficial three-dimensional inter-group sparsity, we learn shared dictionaries in both spatial and temporal dimensions from all of the stacked OCT group tensors. Furthermore, we develop an effective algorithm to solve the resulting optimization problem by using two efficient optimization approaches, including proximal alternating minimization and the alternative direction method of multipliers. Finally, extensive experiments on OCT datasets from various imaging devices are conducted to prove the generality and usefulness of the proposed TRGDL model. Experimental simulation results show that the suggested TRGDL model outperforms state-of-the-art approaches for OCT image denoising both qualitatively and quantitatively.

The new production strategies employed among contemporary Jordanian music groups

This study aimed to explore the musical production strategies that emerged among new musical groups that emerged in Jordan since the beginning of the 21st century by studying four cases of Jordanian new music bands. The unprecedented emergence of the Internet and social media in the Jordanian daily-life context has freed the cultural scene and changed the dominant role of production companies. To achieve the study's object, the researcher employed observation and personal interviews for information collection. The study results showed that the four music bands use mainly social media and the Internet in production through several strategies developed by the bands and showed a clear success in using them as effective tools in Jordanian independent music production. Keywords: Jordanian new music bands, Independent music production, Music production strategies, Crowdfunding, Social Media.

Design and Build a KIT Humoris (Ohm's Law for Dynamic Electricity) for Science Learning in Junior High School

The role of tools as educational media is crucial in facilitating the learning of scientific concepts, allowing students to master science materials and develop their skills effectively. This research aims to develop a KIT as an experimental approach to learning focused on investigating the relationships among voltage, current, resistance, and power. This study employs a research and development (R&amp;D) approach to design and implement an impactful science learning tool for science experiments. The envisioned impact of this learning tool is to captivate students' interest and concept understanding in science education, especially in Ohm's law for the dynamic electricity topic. The design of the KIT Humoris is specifically structured to establish and illustrate the relationships between voltage, current, and resistance, as well as to calculate power based on voltage and current values. The user-friendly design and visually appealing features of the KIT Humoris make it a practical tool that enhances the tangible understanding of science concepts. Integrating this tool with the Merdeka Curriculum allows teachers to customize lessons according to the unique needs of their students. The design process involves meticulous planning, prototype development, and continuous evaluation to ensure the tool's effectiveness. Results from the trial phase indicate heightened student engagement, improved conceptual understanding, and a profound practical learning experience. KIT Humoris is a valuable science learning tool, providing an engaging and practical approach aligned with the Merdeka Curriculum. Its successful implementation increases student enthusiasm and a deeper comprehension of scientific principles. The implications are contributing to the innovation of teaching methods, especially in the context of the Merdeka Curriculum. From a practical standpoint, the tool has a positive impact on students and their understanding of science concepts.

A critical reflection on using the Patient Engagement In Research Scale (PEIRS) to evaluate patient and family partners' engagement in dementia research.

Research involvement of people with lived experiences is increasing. Few tools are designed to evaluate their engagement in research. The Patient Engagement In Research Scale (PEIRS) is one of the few validated tools. Our team employed the PEIRS with patient and family partners with lived experiences of dementia every 6 months in a two-year telepresence robot project. This reflection paper reports our self-study on key learnings and proposes practical tips on using the PEIRS to evaluate patient and family partners' engagement in dementia research. It is the first to document a case using the PEIRS multiple times in a dementia research project. Guided by Rolfe et al.'s reflective model, we conducted three team reflective sessions to examine the team's experiences using the PEIRS to improve and evaluate patient and family partners' engagement in the research. We also reviewed our meeting notes and fieldnotes documented in the research journal. A reflexive thematic analysis was performed. The team identified three key learnings: the values of using the PEIRS survey, the adaptations, and the factors influencing its implementation as an evaluation tool. Using the PEIRS provided significant benefits to the project, although some patient and family partners felt it was burdensome. The evaluation tool was enhanced with emojis and comment boxes based on suggestions from patient partners. The emojis introduced an element of fun, while the comment boxes allowed for personalized responses. Several factors influenced the PEIRS tool's effectiveness: the interviewer's identity, the confidentiality of responses and follow-ups, the timing and frequency of using the tool, and the presentation of the evaluations. These learnings led to the development of six practical tips,-"ENGAGE": Enjoyable and fun process, Never impose, Get prepared early, Adapt to the team's needs, Give people options, and Engage and reflect. With the emerging trend of including people with lived experiences in dementia research, there is a need for ongoing assessment of engagement from both patient and family partners and the research team strategies. Future research can further explore survey logistics, co-development of evaluation tools, and the use of tools with people living with dementia.

Normative value of hyperopia reserve and myopic shift in Chinese children and adolescents aged 3–16 years

BackgroundThis research aims to generate normative values of hyperopia reserve and refractive progression as effective tools to estimate the risk of myopia.MethodsA 1-year follow-up study was conducted among Chinese children...

Enhancing nursing students' clinical competency using a multi-patient simulation learning model: A randomized controlled study

BackgroundFor nurses, clinical competency is paramount in ensuring that patients receive safe, high-quality care. Multi-patient simulation (MPS) in nursing education is gaining attention, and evidence shows its suitability for real-life situations. MPS can be an effective solution for nurses' continuing clinical education. ObjectivesThis project compares the effectiveness of MPS (involving both a standardized patient and a high fidelity simulator) and a single high-fidelity simulation (single HFS; only involving a high fidelity simulator) for enhancing the clinical competency of nursing students. DesignA stratified, permuted, block randomized controlled study design was used.Settings and participants.Sixty undergraduate nursing students in years 3, 4, and 5 were selected to participate. Subgroups with each comprising three undergraduate nursing students from different years were formed. MethodsThe participants were randomized to receive either an MPS (intervention group) or single HFS (control group) for 1 day; they later received the same intervention after a 30-day washout period. One objectively measured questionnaire and two self-reported questionnaires were used to measure clinical competency: the Creighton Competency Evaluation Instrument (CCEI), Clinical Competence Questionnaire (CCQ), and Simulation Effectiveness Tool – Modified Questionnaire (SET-M). ResultsThe results revealed significant between-group differences. Specifically, the intervention group showed greater improvement than the control group in both the CCQ (linear contrast [d] = 71.4; 95 % confidence interval [CI] = 53.407, 89.393; P < 0.001) and CCEI total scores (d = 7.17; 95 % CI = 5.837, 8.503; P < 0.001). The SET-M results indicated that 85 % of the participants (n = 51) strongly agreed that they felt more confident about performing a patient handover to the healthcare team after the simulation. ConclusionsThe study findings indicated that both the MPS and single HFS effectively enhanced students' clinical competency. However, MPSs have superior educational outcomes relative to traditional single HFSs.

Generating Python Type Annotations from Type Inference: How Far Are We?

In recent years, dynamic languages such as Python have become popular due to their flexibility and productivity. The lack of static typing makes programs face the challenges of fixing type errors, early bug detection, and code understanding. To alleviate these issues, PEP 484 introduced optional type annotations for Python in 2014, but unfortunately, a large number of programs are still not annotated by developers. Annotation generation tools can utilize type inference techniques. However, several important aspects of type annotation generation are overlooked by existing works, such as in-depth effectiveness analysis, potential improvement exploration, and practicality evaluation. And it is unclear how far we have been and how far we can go. In this paper, we set out to comprehensively investigate the effectiveness of type inference tools for generating type annotations, applying three categories of state-of-the-art tools on a carefully-cleaned dataset. First, we use a comprehensive set of metrics and categories, finding that existing tools have different effectiveness and cannot achieve both high accuracy and high coverage. Then, we summarize six patterns to present the limitations in type annotation generation. Next, we implement a simple but effective tool to demonstrate that existing tools can be improved in practice. Finally, we conduct a controlled experiment showing that existing tools can reduce the time spent annotating types and determine more precise types, but cannot reduce subjective difficulty. Our findings point out the limitations and improvement directions in type annotation generation, which can inspire future work.

Model based on single-nucleotide polymorphism to discriminate aspirin resistance patients

BackgroundAspirin is widely used for preventing ischaemic events. About 20%–40% of patients have aspirin resistance (ASR), which prevents them from benefiting from aspirin medication. This study aimed to develop and...

Detection of sensitivity and vestigiality of presumptive tests for swabbed blood stains.

It is a common practice in forensic casework to use presumptive tests for blood stains before DNA extraction and testing. Stains are usually swabbed and then the swabs are sent for analysis. The Kastle-Meyer (KM) and Leucomalachite green (LMG) presumptive tests for blood are widely used, and their sensitivities have been thoroughly tested in the literature in solution and directly on stains, but not on swabbed stains to mimic casework. In this study, the sensitivity of the KM and LMG tests was tested on eight blood dilutions on cotton fabric and ceramic tile that were stained and subsequently swabbed. Both tests showed sensitivity up to 1:5000, which is slightly lower than reported values in solution or directly on stain but still highly effective in most cases. Stains were also cleaned with common agents, then swabbed and re-tested. Stained ceramic tiles cleaned with soap/water or bleach gave mixed positive and negative results for the 1:10 dilution, presumably due to variance in how thoroughly each investigator cleaned the stain, and other dilutions were undetectable after cleaning. The LMG test gave false positives for bleach cleaned stains, due to reagent reactivity with bleach. Surprisingly, blood was detectible up to the 1:100 dilution with both tests on stained cotton fabric that was cleaned in a washing machine with detergent and dried. Ultimately the KM and LMG presumptive tests remain effective tools for swabbed blood stains, and their practicality for cleaned stains is dependent on material containing the stain, cleaning agent and processing.

Antiviral Compounds to Address Influenza Pandemics: An Update from 2016-2022.

In recent decades, the world has gained experience of the dangerous effects of pandemic events caused by emerging respiratory viruses. In particular, annual epidemics of influenza are responsible for severe illness and deaths. Even if conventional influenza vaccines represent the most effective tool for preventing virus infections, they are not completely effective in patients with severe chronic disease and immunocompromised and new small molecules have emerged to prevent and control the influenza viruses. Thus, the attention of chemists is continuously focused on the synthesis of new antiviral drugs able to interact with the different molecular targets involved in the virus replication cycle. To date, different classes of influenza viruses inhibitors able to target neuraminidase enzyme, hemagglutinin protein, Matrix-2 (M2) protein ion channel, nucleoprotein or RNAdependent RNA polymerase have been synthesized using several synthetic strategies comprising the chemical modification of currently used drugs. The best results, in terms of inhibitory activity, are in the nanomolar range and have been obtained from the chemical modification of clinically used drugs such as Peramivir, Zanamivir, Oseltamir, Rimantadine, as well as sialylated molecules, and hydroxypyridinone derivatives. The aim of this review is to report, covering the period 2016-2022, the most recent routes related to the synthesis of effective influenza virus inhibitors.

MADM framework based on interval-valued neutrosophic aczel-alsina power average operator and application to translation teaching quality evaluation of college English

The huge demand for translation talents has prompted the universities to focus on improving the quality of translation teaching. As a necessary means to improve the quality of teaching, translation teaching quality evaluation plays a role in guiding, regulating and managing translation teaching, which is an important part of translation teaching. The translation teaching quality evaluation of college English is viewed as the multiple attribute decision making (MADM). The interval-valued neutrosophic sets (IVNSs) are used as an effective tool for characterizing uncertain information during the translation teaching quality evaluation of college English. In this paper, interval-valued neutrosophic number (IVNN) Aczel-Alsina power average (IVNNAAPA) operator is proposed based on the classical Aczel-Alsina operator and power average (PA) operator. Then, IVNNAAPA operator is employed to cope with MADM problem. Finally, the numerical decision example for translation teaching quality evaluation of college English is employed to illustrate the produced method and some comparative models are used to prove the effectiveness of the IVNNAAPA method.