METHODS: The sensor was tested with three kinds of samples, namely Pseudomonas aeruginosa, tuna, and tuna that was contaminated with P. aeruginosa bacteria. During the process of collecting sensor data, all samples were placed in a vacuum so that the gas or aroma produced was not contaminated with other aromas. Eight sensors were used which were designed and implemented in an electronic nose (E-nose) device that can withstand aroma. The data collection process was carried out for 48 h, with an interval of 6 h for each data collection. Data processing was performed by using the principal component analysis and support vector machine (SVM) methods to obtain a plot score visualization and classification and to determine the aroma pattern of the fish.
RESULTS: The results of this study indicate that the E-nose system is able to smell fish based on the hour with 95% of the cumulative variance of the main component in the classification test between fresh tuna and tuna fish contaminated with P. aeruginosa.
CONCLUSION: The SVM classifier was able to classify the healthy and unhealthy fish with an accuracy of 99%. The sensors that provided the highest response are the TGS 825 and TGS 826 sensors.
METHOD: This cross-sectional study was conducted among pre-clinical medical and dental students using convenience sampling. Questions regarding sociodemographic profile and responses to the Smartphone Addiction Scale Short Version (SAS-SV) and Depression, Anxiety and Stress Score questionnaire (DASS-21) were collected. Multiple logistic regression testing was used to analyse the factors associated with smartphone addiction.
RESULTS: We invited 409 pre-clinical medical and dental students to participate voluntarily, resulting in a response rate of 80.2%. The prevalence of high-risk smartphone addiction among the participants was 47.9%. Male participants, participants who used smartphones mainly for social media, and participants with depressive symptoms were more likely to have a high risk of smartphone addiction. Medical students, participants who spent less than 3 hours per day on a smartphone, and participants who used smartphones for education-related activities were less likely to have a high risk of smartphone addiction.
CONCLUSION: Smartphone addiction prevalence among pre-clinical medical and dental students was high. Therefore, the authorities should overcome this problem by implementing early measures.
MATERIALS AND METHODS: Four online databases (PubMed, ScienceDirect, Scopus, and Web of Science) were searched using different MeSH terms and Boolean Operators to retrieve the articles (until June 2021), followed by a hand-search of the reference list of the included articles. All full-text, original studies in English that evaluated SDF staining and at least one SDF modification/alternative were included.
RESULTS: Among the assessed studies, nine studies explored the stain-minimization effect of potassium iodide (KI) post-application following SDF treatment. Among these, eight concluded that KI application after SDF treatment significantly reduced tooth staining, while one showed marginal staining following glass ionomer restoration of the SDF-treated dentine. Additionally, one study applied potassium fluoride (KF) and silver nitrate (AgNO3 ) concurrently to mitigate SDF-mediated staining. One study compared SDF staining with polyethylene glycol (PEG)-coated nanoparticles containing sodium fluoride (NaF), and another used nanosilver fluoride (NSF) for staining comparison with SDF.
CONCLUSIONS: Within the limitations of this study, the addition of different materials to SDF has proven to be a beneficial strategy for overcoming tooth staining associated with SDF. Future studies are warranted, particularly clinical trials, to validate these findings.
CLINICAL SIGNIFICANCE: SDF-mediated tooth staining is a serious concern that limits its clinical use. A review of various strategies to overcome this problem will help clinicians enhance its clinical use and patient acceptance.
METHODS: Administrative and field workers from different divisions across Malaysia's six regions were randomly sampled to collect data for this study. The workers were given a total of 500 questionnaires, of which 345 were returned to the team of researchers. Based on the data analysis, there is an effective interaction between the factors tested toward safety performance.
RESULTS: According to findings, psychological capital positively and significantly affected workers' work engagement. Also, work engagement greatly impacted both workers' safety performance outcomes. Also, as expected, worker pressure significantly and negatively affected workers' safety performance.
DISCUSSION: Insights gained from this research have helped us better organize work and involve employees in safety activities/policies to boost workplace safety performance. The study also suggested that firms should reduce their employees' workloads because doing so would not lower their Psychological Capital but would instead fortify them to better carry out their duties in a risk-free manner.