Quality Assessment of Honey in Three Different Geographical Areas from Republic of Moldova

One of the empirical and image products of the Republic of Moldova is bee honey. Its assortment is due to the variety of landforms, as well as the diversity of flora specific to geographical regions. During the Covid-19 pandemic, domestic consumers had limited access to bee honey. This was caused by the restrictions imposed in order to organize fairs and agricultural markets which are one of the main sources for the purchase of bee products in the Republic of Moldova. At the same time, the analysis of trademarks in supermarkets highlighted the preferences of honey consumption as follows: polyfloral honey—28.57%, lime honey—20.40% and acacia honey—14.28%. In order to evaluate the quality of honey from small producers and highlight the specific characteristics of geographical areas, 60 samples were analyzed that included 3 types of honey: polyflora, sunflower and linden collected from 3 different geographic areas: Soroca area (North), area Ungheni (Center), Stefan Voda area (South). Honey samples were declared harvest of 2020. They were analyzed physico-chemically using methods provided by national and EU standards. The results obtained from the analysis of pollen in honey confirm the botanical origin declared by beekeepers and allowed to highlight the types of pollen specific to each area. Following the determination of qualitative indices: reaction with ethyl alcohol, with resorcinol;insoluble matter, cereal flour, gelatin and starch, it was found that the most inconsistencies were found in linden honey. Some indicators were close to the maximum permissible values specified in regulatory documents. The water content found in the honey samples was quite varied, and ranged from 16.05% ± 0.11% to 19.89% ± 0.13%, but these samples were within the limits of the standards. Total acidity ranged from 6.19 ± 0.13 to 27.20 ± 0.03 which falls within the established norms (up to 50.00 cm3 NaOH solution in (milliequivalents) per 100 g of honey). According to the SIE Lab space, all samples have a yellowish tint, this is indicated by positive values on the b* axis. However, honey samples from the southern region of Moldova showed the presence of greenish pollen, as evidenced by the reduced values on the a* axis. Our research reveals the quality level of honey from three different areas of the Republic of Moldova and the variation of quality parameters due to factors such as geographical region, climatic conditions, botanical origin and handling or storage conditions.

Comprehensive Analysis of Gender Classification Accuracy across Varied Geographic Regions through the Application of Deep Learning Algorithms to Speech Signals

This article presents an exhaustive comparative investigation into the accuracy of gender identification across diverse geographical regions,employing a deep learning classification algorithm for speech signal analysis.In this study,speech samples are categorized for both training and testing purposes based on their geographical origin.Category 1 comprises speech samples from speakers outside of India,whereas Category 2 comprises live-recorded speech samples from Indian speakers.Testing speech samples are likewise classified into four distinct sets,taking into consideration both geographical origin and the language spoken by the speakers.Significantly,the results indicate a noticeable difference in gender identification accuracy among speakers from different geographical areas.Indian speakers,utilizing 52 Hindi and 26 English phonemes in their speech,demonstrate a notably higher gender identification accuracy of 85.75%compared to those speakers who predominantly use 26 English phonemes in their conversations when the system is trained using speech samples from Indian speakers.The gender identification accuracy of the proposed model reaches 83.20%when the system is trained using speech samples from speakers outside of India.In the analysis of speech signals,Mel Frequency Cepstral Coefficients(MFCCs)serve as relevant features for the speech data.The deep learning classification algorithm utilized in this research is based on a Bidirectional Long Short-Term Memory(BiLSTM)architecture within a Recurrent Neural Network(RNN)model.

Review of the Water Footprint Project within Geographically Delineated Area

During the last decade, there has been an intensive research activity concerning the concept of the Water Footprint （WF） approach, which was firstly introduced by Arjen Hoekstra in 2002. WF is an indicator of direct and indirect freshwater use of a consumer or producer that takes into account water consumption in every step （intermediate and final） along the production chain and services. The concept can be implemented in various levels such as products, consumers, producers, nations and river basins etc.. The water footprint within a geographically delineated area equals the sum of the process water footprints of all processes taking place in the area. The aim of current research is a review of the most important WF studies, with a special focus on applications within regional, basin and administrative unit level. National and global scales are not included in the current paper. The article presents the most widespread methodologies and approaches that attempt to evaluate water footprints of specific defined areas and highlights their recent advances as well as shortcomings in the constantly evolving research efforts.

Citation versus altmetrics of papers published in PLOS ONE:A comparative analysis of six countries

With the development of open access,more scientific papers show the multi-dimensional academic impact,which makes researchers focus on the comparison between altmetrics and citations.By the use of statistical analysis,we compare the citation and altmetrics of open access papers published in PLoS in past 10 years by 6 countries which are selected in terms of regional distribation,scientific level,native language,etc.,and find the following conclusions:Firstly,the level of scientific development and publication content in different countries have more effect on the 4 indicators of"citation","save","view"and"share"than the native language.Second,there is a significantly positive correlation between"citation"and"save"in the 6 countries,so as the"citation"and"view",while the altmetrics of"share"is just opposite.Therefore,to some extent,the altmetrics of"view"and"save"could be used to evaluate the scientific influence as a complement measurement of traditional citation metrics.Moreover,correlation coefficients between citations and part of altmetrics of the 6 countries are strong.Finally,the curve peaks of the 6 countries occurred in different years,papers published by developed countries have been active for slightly longer than that by developing countries.In detail,the"citation","save"and"view"peaks occurred later in developing countries such as China and Brazil than in some developed countries.Besides,the"share"peak occurred after 6 or 7 years,which is similar for the 6 countries.