Search

One of the most challenging issues in the world of ship control is automatic ship berthing. In order for the berthing to be completed automatically and safely, the control systems must account for the ship’s dynamics at low speeds. Artificial neural networks (ANNs) are frequently used to address this need because of their capacity to mimic and carry out all the functions of the human brain throughout the ship berthing procedure. However, there are still certain drawbacks when employing this theory to create the automatic system for ship berthing, and this makes it more challenging to develop the control system for practical ship applications. This study reviews the pros and cons of employing ANNs along with some other deep learning algorithms in automatic ship berthing systems. Some directions for further research into automatic ship-berthing systems are also suggested.

This article presents a new nine-level common ground (CG) type transformerless inverter (TL) structure with an integrated boost (IB) cell. The proposed structure eliminates the leakage current due to the CG structure between the input source and the grid neutral. The proposed topology is derived by combining a DC-DC boost converter and switched capacitor (SC) cell, capable of generating the nine-level output voltage over a wide range of input voltage variations. By maintaining the duty cycle of the IB converter switch at 50%, the proposed topology generates the output voltage with a boosting factor of 4. Besides, soft charging of the involved SCs and reduced inrush current is also achieved through the inductor, which ensures the permissible current stresses of the power switches. Furthermore, the voltage across the SCs is self-balanced, eliminating the need for complex independent control techniques. A 725-W laboratory prototype is built to test the performance of the proposed topology under various operating conditions, such as input and load variations. The measured efficiency of 96.9% at 725 W is obtained and is comparable with the simulation efficiency. Finally, the merits of the proposed topology are highlighted using a detailed comparison with the other recent nine-level inverter topologies. [ABSTRACT FROM AUTHOR]

Foraging activity of stingless bees are influenced by unpredictable environmental variables. The study on Influence of weather parameters on foraging activity of stingless bee, Tetragonula iridipennis Smith during winter was conducted from October, 2022 to January, 2023. Irrespective of winter months, peak activity of outgoing bees was observed from 1100-1600 hr, nectar carrying bees from 1100-1500 hr, pollen carrying bees from 1200-1400 hr and resin carrying bees from 1200-1500 hr. Hourly temperature and wind speed had positive correlation with foraging activity, whereas, the hourly relative humidity and precipitation negatively affected the foraging activity of T. iridipennis. Among forage carrying bees, the number of nectar carrying bees were maximum, followed by pollen and resin carrying bees (N>P >R) during winter. [ABSTRACT FROM AUTHOR]

Introduction: New and potentially threatening situations often cause anxiety in children. Distraction, which involves diverting children's attention away from painful stimuli during invasive dental procedures, helps to alleviate the child's fear and anxiety and appears to be safe and inexpensive technique. Aim: Comparative evaluation of anxiety levels in children while administering local anesthesia with two different distraction techniques - An in vivo study. Materials and Methods: Randomized controlled clinical trial. Sample size: 30 (15 in each group). Study groups: Group A - kaleidoscope and Group B - stress ball distraction. Following the standard protocol, inferior alveolar nerve block was given. In Group A, children were asked to see a kaleidoscope for various colors and patterns throughout the inferior alveolar nerve block (IANB) procedure. In Group B, children were instructed to keep pressing the stress ball throughout the IANB procedure. Evaluation: Self-reported anxiety at the time of injection was assessed using the Venham picture test (VPT). Physiological levels of anxiety were measured by recording oxygen saturation and pulse rate with a pulse oximeter at baseline and during the injection. Results: Group A showed significantly lower mean VPT scores and lower mean pulse rates than Group B during the IANB procedure. Oxygen saturation levels remained nonsignificant between the two groups. Conclusion: The findings of the study showed that kaleidoscope was more effective as compared to stress ball in reducing dental anxiety during IANB administration in children [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression. This article offers the salient and current aspects of machine learning (ML) tools and approaches from genome to phenome in miRNA research. First, we underline that the complexity in the analysis of miRNA function ranges from their modes of biogenesis to the target diversity in diverse biological conditions. Therefore, it is imperative to first ascertain the miRNA coding potential of genomes and understand the regulatory mechanisms of their expression. This knowledge enables the efficient classification of miRNA precursors and the identification of their mature forms and respective target genes. Second, and because one miRNA can target multiple mRNAs and vice versa , another challenge is the assessment of the miRNA-mRNA target interaction network. Furthermore, long-noncoding RNA (lncRNA)and circular RNAs (circRNAs) also contribute to this complexity. ML has been used to tackle these challenges at the high-dimensional data level. The present expert review covers more than 100 tools adopting various ML approaches pertaining to, for example, (1) miRNA promoter prediction, (2) precursor classification, (3) mature miRNA prediction, (4) miRNA target prediction, (5) miRNA- lncRNA and miRNA-circRNA interactions, (6) miRNA-mRNA expression profiling, (7) miRNA regulatory module detection, (8) miRNA-disease association, and (9) miRNA essentiality prediction. Taken together, we unpack, critically examine, and highlight the cutting-edge synergy of ML approaches and miRNA research so as to develop a dynamic and microlevel understanding of human health and diseases.

Background: Violence, a notable human rights concern, has a public health impact across the globe. The study aimed to determine the prevalence and determinants of domestic violence among ever-married women aged 18-49 years in India., Methods: Secondary data analysis with National Family Health Survey 5, 2019-21 data (NFHS-5) was conducted. The complex sampling design of the survey was accounted-for during analysis. The primary outcome was domestic violence. Prevalence was reported with 95% confidence interval (CI). Prevalence ratio was reported to provide the factors associated with domestic violence using Poisson regression., Results: About 63 796 ever-married women aged 18-49 years covered under domestic violence module of NFHS-5 survey were included. Prevalence of domestic violence (12 months preceding the survey) was 31.9% (95% CI: 30.9-32.9%). Physical violence (28.3%) was the most common form followed by emotional (14.1%) and sexual violence (6.1%). Women with low education, being employed, husband being uneducated or with coercive behavior had significantly higher prevalence of domestic violence., Conclusions: One-third of the reproductive age-group women were facing some form of domestic violence. Target group interventions like violence awareness campaigns, women supportive services and stringent law enforcement should be implemented to eliminate domestic violence by year 2030., (© The Author(s) 2023. Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Despite being the second most often diagnosed form of cancer, lung cancers are rarely found in the general population. It is proposed in this study to employ a methodology of extracting both global and local features from CT scan images for the identification of lung cancer. Data gathering, globalised and localised training as well as testing the model are all part of this structure. This study makes use of 800 CT scan images. Images are pre-processed by warping and cropping in advance of the global testing step. Each image is represented by a feature vector employing eight distinct types of image characteristics, which are taken from the images. After creating feature vectors, three machine learning methods are employed to create detection models. Every medical image has been partitioned over a series of simple divisions throughout the training and testing process locally. To describe each block, feature vectors are derived from the image features that worked effectively in the general phase of the experiment. Similar extracted features are then used to build detection systems for all picture blocks using the learning strategies that were effective in the global stage. SVM using Haar Wavelet characteristics had an accuracy, sensitivity, and specificity of 89%, 90%, and 89%, respectively. One might get 90%‑accurate results with SVM and 91%‑sensitive and 91%‑specific results using SVM plus HOG features. Finally, the utilisation of SVM with Gabor Filter characteristics achieved the greatest correctness, specificity, and sensitivity values, particularly 87%, 86%, and 87%, respectively (Tab. 3, Fig. 7, Ref. 18). Keywords: feature extraction, support vector machine, lung cancer, classification, machine learning.