No abstract

Nowadays, environmental protection involves many issues and problems, among which the waste generated by various human activities makes up a significant share, which is becoming newer day by day. Moreover, the production of normal, industrial, special, hospital, and agricultural waste and improper management of these materials has created many health, safety, and environmental problems. Based on this approach, this research study aims to determine the model of waste management and energy efficiency in smart homes using the Internet of Things (IoT). The research method used by this study is estimative-computational. For this purpose, the required data were collected using a computational approach. For this purpose, the required views and data were collected through experts in this field and calculated in MATLAB and STATA software. The data analysis tool was represented by fuzzy calculations and for this purpose MATLAB software was used. The study revealed that energy costs in smart homes using the IoT technology are impressive. The number of home residents in smart homes using the IoT is impressive. Home area in smart homes using the innovative technology of IoT is also impressive.

Aim: The present study aims to observe the reasons for which the participants have chosen to uptake one of the COVID-19 vaccines approved in Romania. Thus, it will help us to determine whether the reasons are medical in nature, with the respondents’ scope to stay healthy, or if there are other motivations. High rates of vaccine acceptance are essential in the struggle against the COVID-19 pandemic, and trust indicators in other inoculations may be vital for the good management of the vaccination campaign. Methods: The research consisted in applying an online questionnaire in the period January–March 2022 during the fifth wave of COVID-19. The individuals in the target group had to comply with three conditions: they should be inoculated, at least 18 years of age and Romanian residents. The questionnaire was administered to 2297 people and structured to obtain socio-demographic data, determine confidence in mandatory and optional vaccines (rotavirus, hepatitis A, meningococcal vaccine, etc.) and extract the reasons why respondents chose to be vaccinated. Results: The data extracted from the questionnaire reveal a high rate of confidence of participants in the vaccines included in the national vaccination scheme (98.6%) and in the optional vaccines other than anti-COVID-19 (97.2%). Of the respondents, 23.4% had at least one positive test for COVID-19. Although the entire sample is vaccinated against the SARS-CoV-2 virus, the reasons behind the decision to vaccinate are not only medical in nature, thus, 18.3% were motivated by “protecting their own health/protection against the virus”, 17% due to “fear of the disease”, 8.8% for getting back to normal life and ending the pandemic and 8.5% due to government restrictions/vaccination certificate. Conclusions: In our study, we were able to validate the research hypothesis that the reasons for vaccine acceptance are multiple and not only medical (health protection, existing co-morbidities, etc.) and to show that although vaccination has been accepted, some participants believe in conspiracy theories, including those that try to convince people of the harmfulness of the vaccine. In addition, by applying Pearson, Kendall and Spearman correlation tests, we observed that indicators showing high confidence in optional vaccines relate strongly with the decision to vaccinate against COVID-19.

The analysis of dental materials in forensic investigations is very important. Identifying the victims of accidents or attacks sometimes requires the analysis of their dentures. In this paper, X-ray spectrometry was used as a method to determine the chemical composition of a dental material. In this case, it was established that it was a dental alloy (Cr-Ni-Mo) with a percentage composition of 23.58% Cr, 64.32% Ni and 10.82% Mo.

Various rubber products are used in the textile industry. Due to increased foreign supply and synthetic rubber production, the price of natural Rubber in India has become more volatile. This paper aims to develop an appropriate model to predict the weekly price using the Box Jenkins methodology. The weekly price for Indian RSS-1 Rubber for the sample period from January 2002 to December 2019 has been collected from the official website of the Indian Rubber Board. ACF and PACF correlograms check the series stationarity and identify the model parameters. A model with the maximum number of significant coefficients, lowest volatility, lowest Akaike's information criterion (AIC), lowest Schwarz criterion and highest Adjusted R-squared is tentatively selected as the appropriate model and for the same model diagnostic check is carried out. An appropriate model to forecast the weekly price for the RSS-1 variety of Rubber is ARIMA (1, 1, 4).