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The purpose of this study was to learn about mothers’ experiences with food choices for their pre-school children in underprivileged communities in Greater Western Sydney (GWS). A total of 20 mother-child dyads living in GWS were recruited to a qualitative study from an ongoing birth cohort study. Participants’ houses were visited for semi-structured interviews, which were recorded, transcribed verbatim, and analysed thematically. The interviews yielded five main themes: (i) food choices, nutrition, and health; (ii) accessibility and availability of foods (iii) buying time for parents; (iv) child’s age and their preference on food choices; (v) conditioning certain behaviours by family and cultural factors. Nutrition literacy, child’s preferences, unhealthy food intake by family members, child’s demand, advertising and availability of harmful foods, and time constraints were all mentioned as hurdles to mothers making appropriate meal choices for their children. However, some identified facilitators were promoting parents’ knowledge, increasing access to health educational materials, upskilling mothers to providing healthier alternatives, regulating the marketing of unhealth foods. Although, the present study identified critical factors that influence mothers’ food choices for their young children, making healthy food choices is a complex practice as it is shaped by individual, social and environmental influences.

The “environment” has become one of the important and debatable topics of the world and policymakers identifying the new predictors of CO2 emissions. Therefore, some economies have been promoting fiscal decentralization to encourage environmental quality by granting more financial autonomy to provincial and sub-national governments. Therefore, this study evaluates the dynamic effect of fiscal decentralization on CO2 in selected nine Asian economies using a fresh dynamic panel ARDL model from 1984 to 2017. The empirical findings show that fiscal decentralization has asymmetric effects on CO2 emissions because a positive change in revenue and expenditure decentralization reduced CO2 emissions in Asia. Moreover, a negative change in expenditure decentralization has also enhanced CO2 emissions in the long run. Thus, clean environmental policies and recommendations can be revised and proposed based on nonlinear findings in the modern era.

Sentiment analysis, the process of extracting and classifying opinions expressed in text, has gained significant traction in different fields, such as market research, customer feedback, and social media analysis. However, traditional sentiment analysis methods often struggle with multilingual data due to the complexities of language variations and differences in culture. These limitations can be addressed with hybrid deep learning approaches. This paper proposes an optimized hybrid deep learning model for multilingual sentiment analysis, leveraging the Grey Wolf Optimization (GWO) algorithm to enhance the model’s performance. The proposed approach combines convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to capture both local and contextual sentiment information effectively. Additionally, the approach utilizes language-specific word embedding techniques to address the semantic differences between languages. Unlike previous techniques, our model includes the Grey Wolf Optimization (GWO) algorithm for hyperparameter tweaking, which improves both accuracy and efficiency in multilingual sentiment analysis. It improves sentiment classification accuracy by 9.51% over typical machine learning models and 5.42% above deep learning baselines.

Primary reason of climate reason is release of greenhouse gases into the biosphere which warms the earth’s atmosphere. Climate change is serious issue that affects public sentiment significantly. In this paper, stack ensemble learning based framework is proposed to analyze public sentiment regarding climate change by using text datasets. First step of proposed framework is text preprocessing which includes tokenization, stemming, and stop-word removal to create TF-IDF representations. Next, stack ensemble model is developed for sentiment classification as positive, neutral, or negative. This model utilizes four machine such as support vector machine (SVM), decision tree (DT), random forests (RF), and gradient boosting classifier (GDB) as base models. The predictions given by all four models are combined using XGBoost as a meta-classifier. The four dataset related to climate change are used for experimental analysis. Results show that the proposed model achieves 89.72% precision, 91.65% recall, 90.23% F1-score, and 91.47% accuracy. These results suggest that the stack ensemble learning approach is effective for analyzing public sentiment about climate change.

Background: At present, there is an escalating concern regarding possible role of 5-HT3 receptor in psychopharmacology and the therapeutic potential of their antagonists. Moreover, inclusion of 5-HT3 receptor antagonist may curtail the antidepressant-induced LTP decrease causing memory deficits, thereby improving efficacy of current antidepressants. Aim and Objective: This study aims to evaluate the antidepressant activity of 5-HT3 antagonist, that is, ondansetron (OND) in rodent models of depression. Materials and Methods: Male Swiss albino mice (20-30 g bw) and Wistar rats (100-200 g bw) were divided into five groups. Animals received either OND p.o. (0.1, 0.5 and 1 mg/kg), venlafaxine (10 mg/kg), or vehicle (1 ml distilled water p.o.) in control. Tail suspension and forced swim test were used to evaluate the effects of drugs and control after 60 min of their administration. Furthermore, assessment of locomotor activity (LA) was done by photoactometer after 24 h of drug administration. Results: Ondansetron exhibited significant antidepressants activity (P < 0.05) in rodent models. However, LA was not significantly altered by OND. Conclusion: Ondansetron exhibited significant antidepressant activity in rodent models hence paving the way for exploration of 5-HT3 receptor antagonist in future researches and its therapeutic application in depression.

Introduction: Dental age (DA) assessment is a reliable method for chronological age (CA) estimation used for criminal, forensic, and anthropologic purposes. Age estimation also provides valuable information when the birth date is unavailable, as in case of migrants. Furthermore, the CA of living people is important for employment and marriage purpose. Objectives: The aim of the present study is to check the reliability of the Willems method in the population of Bangalore. Materials and Methods: The study was conducted on 205 (109 boys and 96 girls) participants by their radiographic records in the department of forensic odontology using modified Demirjian's method by Willem et al. Results: A total of 205 subjects were selected for the study, among which 109 (53.2%) were boys and 96 (46.8%) were girls. The mean CA was 12.23 ± 1.75 and the mean DA was 12.20 ± 1.99. Willems method seems to be reliable in estimating DA in Bangalore populations. Conclusion: This study showed that the Willems method gave near precise estimation of the CA.

Periodontal diseases significantly impact about half of the global population, and their treatment often encompasses relieving symptoms as well as regenerating the destroyed tissues. Revolutionary research in the management of periodontal disease includes biomaterials, a boon to re-generative dentistry owing to their excellent biological properties: non-toxicity, anti-inflammatory, biocompatibility, biodegradability, and adhesion. This study aimed to fabricate an injectable fucoidan containing chitosan hydrogel and prove its effectiveness in periodontal bone regeneration. The injectable hydrogel was prepared using the sol-gel method and was subjected to various physical, chemical, and biological characterizations to understand its efficacy in formation of new bone. The effectiveness of the developed hydrogel was assessed in periodontal bony defects to study the soft and hard tissue changes. A total of 40 periodontitis patients with bony defects were recruited and randomized into two groups to receive fucoidan-chitosan hydrogel and concentrated growth factor, respectively. Customized acrylic stents were used to guide the hydrogel placement into the defect site. Post-surgical changes in clinical parameters were assessed at 3, 6, and 9 months to appreciate the soft and hard tissue changes using repeated measures analysis of variance and Bonferroni’s post hoc test. Significance was kept at 5%. The porosity, water uptake of the prepared hydrogel showed good efficacy, with particle size of the fucoidan containing chitosan hydrogel of 6.000 nm. The MG-63 osteoblasts cell line revealed biocompatibility, biodegradability and showed slow and sustained drug release, increased cell proliferation, and enhanced alkaline phosphatase secretion. Mineralization assay was greatest in the fucoidan containing chitosan hydrogel. Clinically, it exhibited significantly lower probing depth values and a higher mean improvement in clinical attachment level as compared to the concentrated growth factor (CGF) group at the end of 3 and 6 months (p < 0.05). The mean of the defect fills in the fucoidan containing chitosan group was 1.20 at the end of 9 months (p < 0.001) as compared with defect fills observed in the CGF group. The presence of fucoidan in the hydrogel significantly contributed to bone regeneration in humans, thus strengthening its potential in tissue engineering. Fucoidan-chitosan will be a promising biomaterial for bone tissue regeneration.

The fact is the stock market has an asymmetric effect on macroeconomic variables. In this study, we examine the nonlinear stock market reaction to the environment. This is the first study that considers the possibility of asymmetric effects of stock market on environmental pollution in Asia. This study considers the experiences of Asia economies by using the panel NARDL methodology over the data period from 1995 to 2019. The long-run panel NARDL results showed that the positive change in stock market increases carbon emissions. In adverse, the negative change in stock market significantly mitigates the carbon emissions in Asia. The short-run stock market asymmetric effects continued into the long-run asymmetric effects on the environment in Asia. Thus, policymakers and authorities should initiate to promote green financial activities in Asian stock markets.

The primary objective of this study is to analyze the dynamic association among the green energy production (GEP), green technological innovation (GTI), and green international trade (GIT). This study uses fully modified least square (FMOLS) and dynamic least square (DOLS) for data analysis. This study uses the panel data set starts from 2000 to 2020 for eight south Asian countries. This study find a negative relationship between GEP and EFP. However, green technological innovation and green international trade have positive significant association. This study recommends several policy implications regarding the economies of south Asia (SA) based on empirical findings: to subsidize the industries for the installation of renewable projects for the production of renewable energies.

Background: Oral lichen planus is a T-cell-mediated chronic inflammatory disease affecting approximately 1% to 2% of the population, the etiology of which is currently unknown. The objectives of this study were to observe if senescence occurs in oral lichen planus, through the assessment of the immunohistochemical expression of a novel marker for senescence called Senescence marker protein-30 or regucalcin, and compare the expression to that in oral lichenoid reaction and non-specific inflammation. Subjects and Methods: The study material consisted of 30 cases of oral lichen planus, 15 cases of oral lichenoid reaction and 15 cases of non-specific inflammation. The number of positive cells in ten randomly selected high power fields were counted in the epithelium and the connective tissue separately and the mean was determined. Results: Mann-Whitney U test was used to statistically analyze if there was any significant difference in the expression of Senescence marker protein-30 between oral lichen planus, oral lichenoid reaction and non-specific inflammation. Even though a greater expression was seen in the oral lichen planus cases than oral lichenoid reaction, the difference in both the epithelium and connective tissue was not statistically significant. Conclusion: This study shows that in addition to the already known mechanisms like apoptosis and increased cell proliferation rates, the activated T-lymphocytes may also trigger a senescent change in the cells of oral lichen planus. As with the other mechanisms, this is also seen only in a small proportion of the cases.