METHODS: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos.
RESULTS: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models.
CONCLUSION: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.
METHOD: Several research methodologies were incorporated into the current study, and a review was carried out using PRISMA as a guide. The publications for this study were chosen from two prominent databases, Scopus and Web of Science. All of the studies were assessed, reviewed, and evaluated independently by two reviewers. The meta-analysis tool, Review Manager (RevMan Copenhagen Version 5.4.1), was used to record the extracted data for the meta-analysis. Moran's I 2 and a funnel plot were utilized to measure heterogeneity, and publication bias was investigated. A 95% confidence interval (CI) and overall risk difference (RD) were estimated using a random-effects model.
RESULT AND DISCUSSION: As a consequence of the search efforts, a total of 46 articles were selected for inclusion in the systematic review and meta-analysis. This review was divided into five major themes, based on a thematic analysis: (i) hatching rate, (ii) development time, (iii) longevity, (iv) survival rate, and (v) wing morphology. In addition, the development time, survival rate, and wing morphology revealed significantly higher risk differences between the maximum and minimum temperatures (RD: 0.26, 95% CI: 0.16, 0.36; p = < 0.00001; RD: 0.10, 95% CI: 0.05, 0.14; p < 0.0001; and RD: 0.07, 95% CI: 0.02, 0.12; p = 0.006, respectively). This study makes several substantial contributions to the body of knowledge and to practical applications. Finally, a number of recommendations are made at the conclusion of this research for the future reference of researchers.
METHODS: A cross-sectional study was conducted among 111 parents of children with autism who attended Child and Adolescent Psychiatry Unit (CAPU), in a university teaching hospital in Kuala Lumpur, Malaysia. Sociodemographic profiles of both parents and children were gathered. Parental adjustment focusing on parental self-blame, despair and acceptance were assessed using self-reported questionnaires namely Adjustment to the Diagnosis of Autism (ADA).
RESULTS: Higher level of despair was associated with parents who have medical illness (β = 0.214, p = 0.016) and children who received antipsychotic medications (β = 0.329, p
METHODS: A total of 30 eggs were exposed to RF radiation at three frequencies: baseline, 900 MHz, and 18 GHz. Each frequency was tested in triplicate. Several parameters were assessed through daily observations in an insectarium, including hatching responses, development times, larval numbers, and pupation periods until the emergence of adult insects.
RESULTS: This study revealed that the hatching rate for the 900 MHz group was the highest (79 ± 10.54%) compared to other exposures (p = 0.87). The adult emergence rate for the 900 MHz group was also the lowest at 33 ± 2.77%. A significant difference between the groups was demonstrated in the statistical analysis (p = 0.03).
CONCLUSION: This work highlighted the morphology sensitivity of Ae. aegypti eggs and their developments in the aquatic phase to RF radiation, potentially altering their life cycle.
MATERIALS AND METHODS: A total of 480 students from different faculties in a Malaysian public university participated in this study. They were selected by simple random sampling method. They completed self-administered questionnaires including the Malay Version of Internet Addiction Test (MVIAT)) to measure internet addiction and Adult Self-Report Scale (ASRS) Symptom Checklist, Depression Anxiety Stress Scales (DASS) and UCLA Loneliness Scale (Version 3) to assess for ADHD symptoms, depression, anxiety, stress, and loneliness respectively.
RESULTS: The prevalence of IA among university students was 33.33% (n = 160). The respondents' mean age was 21.01 ± 1.29 years old and they were predominantly females (73.1%) and Malays (59.4%). Binary logistic regression showed that gender (p = 0.002; OR = 0.463, CI = 0.284-0.754), ADHD inattention (p = 0.003; OR = 2.063, CI = 1.273-3.345), ADHD hyperactivity (p<0.0001; OR = 2.427, CI = 1.495-3.939), stress (p = 0.048; OR = 1.795, CI = 1.004-3.210) and loneliness (p = 0.022; OR = 1.741, CI = 1.084-2.794) were significantly associated with IA.
CONCLUSION: A third of university students had IA. In addition, we found that those who were at risk of IA were males, with ADHD symptoms of inattention and hyperactivity, who reported stress and loneliness. Preventive strategy to curb internet addiction and its negative sequelae may consider these factors in its development and implementation.