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Against the backdrop of increasing smartwatch usage and the dynamic landscape of evolving features, a nuanced understanding of consumer opinions and preferences is vital for tailoring features and crafting effective marketing strategies. This study addresses this imperative by conducting a comprehensive analysis of customer reviews on smartwatches, aiming to determine the pivotal factors guiding consumer purchasing decisions. By employing word clouds to visually represent sentiments, the study uncovers notable trends. Positive reviews prominently highlight the term "quality", suggesting a strong emphasis on product excellence. In contrast, negative reviews were characterized by the prevalence of the term "fake", indicating concerns related to authenticity. Additionally, a comparative assessment of two machine learning algorithms, namely support vector machines and Naive Bayes, demonstrates that support vector machines exhibit superior accuracy in classification. These findings offer valuable insights for industry practitioners navigating the competitive landscape of the smartwatch market, providing actionable information for optimizing product features and refining marketing strategies to meet consumer expectations.

Purpose: The future of management studies is invariably steering towards online and hybrid modes of course delivery. Therefore, assessing the effectiveness of online course delivery is exceptionally crucial. This study attempts to evaluate the effectiveness of online course delivery in management education involving the instructor, participant and technological component. This study contributes to the body of knowledge in three ways. First, the study proposes an approach to assess the effectiveness of online courses in management education. The study demonstrates this by taking a case study of a business school (B-school) in southern India. Second, the study identifies the shortcomings and areas that need improvement to enhance the overall effectiveness further. Third, the study outlines suggestive measures to improve the effectiveness of online course delivery by addressing technical, infrastructural, instructor and student behavioral components. Design/methodology/approach: To accomplish the objectives, a case study approach has been adopted and fuzzy logic has been used as a methodology to assess the effectiveness of online course delivery in management education. Findings: The findings suggest that instructors' use of cases and animation during online sessions, use of whiteboards, digital pens and other tools, attempts to draw participant's interest and the users' sense of belongingness in the online cohort, self-discipline and motivation from students' side, easy to use Learning Management System (LMS), audio-visual platforms, active electronic communication and training on the technical aspect of the online platform need to be improved to enhance the effectiveness of online course delivery further. The current effectiveness of online course delivery in the case of B-school was found to be "Fair," which is average in relation to the effectiveness labels. Research limitations/implications: This study doesn't investigate the factors that moderate the effectiveness of online course delivery and how the factors influence each other. Future research endeavors can be extended in this direction to enrich the body of knowledge with new insights. Apart from this, the results outlined in this study are about the status quo of the case B-school and can't be generalized. However, the methodology and approach can be adopted by other B-schools or higher educational institutes to measure the schools' and institutes' current level of effectiveness in online teaching. Originality/value: So far, only a few studies have paid attention to the empirical assessment of the effectiveness of online course delivery consisting of engagement from the technical, instructor and participants' dimensions. This study proposes a novel approach to measure the level of effectiveness and identifies the shortfalls that impede good effectiveness in online course delivery.

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Background: The availability of the COVID-19 vaccine during the pandemic has changed the disease course in the entire world. The current study aimed to compare various hematological parameters among COVID-19 patients with and without vaccination. Methods: The present retrospective study included 26 vaccinated and 26 non-vaccinated COVID-19 patients. Various clinical and biochemical parameters of RT-PCR-positive patients were collected. The values are expressed in Mean±SD or median values IQR. Mann-Whitney U test was used for comparison between the groups. Results: Among the vaccinated individuals, 17 cases (65.4%) were asymptomatic, one patient (3.8%) had moderate, eight cases (30.8%) had mild COVID-19 infection and all 26 patients were completely recovered. Among non-vaccinated COVID-19 patients, 25 cases (96.2%) had severe, one case (3.8%) had moderate COVID-19, and 16 patients (61.5%) recovered but ten cases succumbed to COVID-19. There were statistically significant differences in SpO2, total leucocyte count, and differential counts of neutrophils, lymphocytes, eosinophils, monocytes, and basophils between vaccinated and non-vaccinated patients (P

The synthesis of silver nanoparticles (AgNPs) using environmentally friendly methods has become increasingly important due to its sustainability and cost-effectiveness. This study investigates the green synthesis of AgNPs using gall extracts from the plant Ficus recemosa, known for its high phytochemical content. The formation of AgNPs was verified through multiple analytical techniques, including UV-Vis spectroscopy, Fourier-transform infrared (FTIR) spectroscopy, transmission electron microscopy (TEM), zeta potential analysis, and dynamic light scattering (DLS). The UV-Vis spectroscopy results displayed a distinct surface plasmon resonance peak indicative of AgNP formation. FTIR analysis revealed specific interactions between silver ions and phytochemicals in the gall extract, while TEM images confirmed the nanoscale morphology and size of the synthesized particles. Zeta potential and DLS analyses provided insights into the stability and size distribution of the AgNPs, demonstrating good colloidal stability. Biological properties of the AgNPs were assessed through various assays. Antimicrobial activity was tested using the disc diffusion method against Escherichia coli and Staphylococcus aureus, showing significant inhibitory effects. The anticancer potential was evaluated using the trypan blue exclusion assay on Dalton’s Lymphoma Ascites (DLA) cells, revealing considerable cytotoxicity. Additionally, antimitotic activity was studied in the dividing root cells of Allium cepa, where the AgNPs significantly inhibited cell division. This research highlights the effective use of F. recemosa gall extracts for the green synthesis of AgNPs, presenting an eco-friendly approach to producing nanoparticles with strong antimicrobial, anticancer, and antimitotic properties. The promising results suggest potential applications of these biogenic AgNPs in medical and agricultural sectors, paving the way for further exploration and utilization.

Background: Biomedical knowledge graphs (KG) have become crucial for describing biological findings in a structured manner. To keep up with the constantly changing flow of knowledge, their embedded information must be regularly updated with the latest findings. Natural language processing (NLP) has created new possibilities for automating this upkeep by facilitating information extraction from free text. However, due to annotated and labeled biomedical data limitations, the development of completely autonomous information extraction systems remains a substantial scientific and technological hurdle. This study aims to explore methodologies best suited to support the automatic extraction of causal relationships from biomedical literature with the aim of regular and rapid updating of disease-specific pathophysiology mechanism KGs. Methods: Our proposed approach first searches and retrieves PubMed abstracts using the desired terms and keywords. The extension corpora are then passed through the NLP pipeline for automatic information extraction. We then identify triples representing cause-and-effect relationships and encode this content using the Biological Expression Language (BEL). Finally, domain experts perform an analysis of the completeness, relevance, accuracy, and novelty of the extracted triples. Results: In our test scenario, which is focused on the KG regarding the phosphorylation of the Tau protein, our pipeline successfully contributed novel data, which was then subsequently used to update the KG leading to the identification of six additional upstream regulators of Tau phosphorylation. Conclusion: Here, it is demonstrated that the NLP-based workflow we created is capable of rapidly updating pathophysiology mechanism graphs. As a result, production-scale, semi-automated updating of pre-existing, curated mechanism graphs is enabled.

Although hundreds of datasets have been published since the beginning of the coronavirus pandemic, there is a lack of centralized resources where these datasets are listed and harmonized to facilitate their applicability and uptake by predictive modeling approaches. Firstly, such a centralized resource provides information about data owners to researchers who are searching datasets to develop their predictive models. Secondly, the harmonization of the datasets supports simultaneously taking advantage of several similar datasets. This, in turn, does not only ease the imperative external validation of data-driven models but can also be used for virtual cohort generation, which helps to overcome data sharing impediments. Here, we present that the COVID-19 data catalogue is a repository that provides a landscape view of COVID-19 studies and datasets as a putative source to enable researchers to develop personalized COVID-19 predictive risk models. The COVID-19 data catalogue currently contains over 400 studies and their relevant information collected from a wide range of global sources such as global initiatives, clinical trial repositories, publications, and data repositories. Further, the curated content stored in this data catalogue is complemented by a web application, providing visualizations of these studies, including their references, relevant information such as measured variables, and the geographical locations of where these studies were performed. This resource is one of the first to capture, organize, and store studies, datasets, and metadata related to COVID-19 in a comprehensive repository. We believe that our work will facilitate future research and development of personalized predictive risk models for COVID-19.

Oxidation of aggregates containing iron sulfide minerals has recently been linked to severe degradation in housing foundations in the northeast United States and the Trois-Rivières area of Quebec, Canada. Existing performance-based approaches mainly rely on the use of oxidizing solutions, which may create harsh environments and lead to unexpected results. This work evaluated the effectiveness of a mortar test by using atmospheric oxygen (a more realistic exposure condition) as an oxidizing agent and employed a design of experiments approach to investigate the effects of relative humidity (50% and 95%), oxygen content (20.9% and 35%), temperature (5°C and 60°C), and water-to-cement ratio (0.45 and 0.65) on the oxidation potential of iron sulfide-bearing aggregates. Results show that length changes of the mortar samples are mainly attributed to drying shrinkage within the experimental duration (more than 400 days), which is highly dependent on the relative humidity levels, whereas minimal to no expansion was observed under laboratory conditions. Recent efforts to simulate iron sulfide deterioration in laboratories by performance-based tests are then reviewed. Their advances and challenges as well as comparison with the proposed test are summarized, leading to a call for further development of experimental methods.

Abstract Background There has been considerable investment and strategic planning to introduce genomic testing into Australia’s public health system. As more patients’ genomic data is being held by the public health system, there will be increased requests from researchers to access this data. It is important that public policy reflects public expectations for how genomic data that is generated from clinical tests is used. To inform public policy and discussions around genomic data sharing, we sought public opinions on using genomic data contained in medical records for research purposes in the Australian state of Queensland. Methods A total of 1494 participants completed an online questionnaire between February and May 2019. Participants were adults living in Australia. The questionnaire explored participant preferences for sharing genomic data or biological samples with researchers, and concerns about genomic data sharing. Results Most participants wanted to be given the choice to have their genomic data from medical records used in research. Their expectations on whether and how often they needed to be approached for permission on using their genomic data, depended on whether the data was identifiable or anonymous. Their willingness to sharing data for research purposes depended on the type of information being shared, what type of research would be undertaken and who would be doing the research. Participants were most concerned with genomics data sharing that could lead to discrimination (insurance and employment), data being used for marketing, data security, or commercial use. Conclusions Most participants were willing to share their genomic data from medical records with researchers, as long as permission for use was sought. However, the existing policies related to this process in Queensland do not reflect participant expectations for how this is achieved, particularly with anonymous genomics data. This inconsistency may be addressed by process changes, such as inclusion of research in addition to clinical consent or general research data consent programs.

A differential phase sensor based on a narrowband perfect absorber uses the phase singularity at the point of darkness (100% absorption). Herein, a structure with a top SiO2 layer is proposed that is index matched with a glass flow cell that shows the point of darkness for both s‐ and p‐polarizations. Detailed numerical results to optimize the structure, study the role of fluctuation in thickness and roughness of analyte layer show that the proposed structure has refractive index sensitivity of 147.3° RIU−1. Experimental results of launch angle independent ≈99.9% absorption in the 45°–73° range are reported, with the highest reported till date quality factor in planar narrowband absorbers of ≈42, and high figure of merit of 556 for p‐polarization. The structure also exhibits the generalized Brewster effect with a perfect absorption (≈99.99%) for s‐polarization at ≈74° angle. Experimental results are presented to demonstrate the sensor operation for two different biomolecules at the p‐polarization resonance. The configuration presented is ideal for reusable and cost‐effective biosensors.

The ability to deliver foreign molecules into a single living cell with high transfection efficiency and high cell viability is of great interest in cell biology for applications in therapeutic development, diagnostics, and drug delivery towards personalized medicine. Various physical delivery methods have long demonstrated the ability to deliver cargo molecules directly to the cytoplasm or nucleus and the mechanisms underlying most of the approaches have been extensively investigated. However, most of these techniques are bulk approaches that are cell-specific and have low throughput delivery. In comparison to bulk measurements, single-cell measurement technologies can provide a better understanding of the interactions among molecules, organelles, cells, and the microenvironment, which can aid in the development of therapeutics and diagnostic tools. To elucidate distinct responses during cell genetic modification, methods to achieve transfection at the single-cell level are of great interest. In recent years, single-cell technologies have become increasingly robust and accessible, although limitations exist. This review article aims to cover various microfluidic-based physical methods for single-cell intracellular delivery such as electroporation, mechanoporation, microinjection, sonoporation, optoporation, magnetoporation, and thermoporation and their analysis. The mechanisms of various physical methods, their applications, limitations, and prospects are also elaborated.