Open Access Sources Research Articles

An operational urban air quality model ENFUSER, based on dispersion modelling and data assimilation

An operational urban air quality modelling system ENFUSER is presented with an evaluation against measured data. ENFUSER combines several dispersion modelling approaches, uses data assimilation, and continuously extracts information from online, global open-access sources. The modelling area is described with a combination of geographic datasets. These GIS datasets are globally available with open access, and therefore the model can be applied worldwide. Urban scale dispersion is addressed with a combination of Gaussian puff and Gaussian plume modelling, and long-range transport of pollutants is accounted for via a separate regional model. The presented data assimilation method, which supports the use of AQ sensors and incorporates a longer-term learning mechanism, adjusts emission factors and the regional background values on an hourly basis. The model can be used with reasonable accuracy also in urban areas, for which detailed emissions inventories would not be available, due to the data assimilation capabilities.

Improving Generalization of Deepfake Detection With Data Farming and Few-Shot Learning

Recent advances in automated video and audio editing tools, generative adversarial networks (GANs), and social media allow creation and fast dissemination of high quality tampered videos, which are generally called deepfakes. Typically, in these videos, a face is swapped with someone else’s using GANs. Accessible open source software and apps for the face swapping led to a wide and rapid dissemination of the generated deepfakes, posing a significant technical challenge for their detection and filtering. In response to the threat, which deepfake videos can pose to our trust in video evidence, several large datasets of deepfake videos and several methods to detect them were proposed recently. However, the proposed methods suffer from a problem of overfitting on the training data and the lack of the generalization across different databases and the generative models. Therefore, in this paper, we investigate the techniques for improving the generalization of deepfake detection methods that can be employed in practical settings. We have selected two popular state of the art deepfake detectors: based on Xception and EfficientNet models, and we use five databases: from Google and Jigsaw, FaceForensics++, DeeperForensics, Celeb-DF, and our own publicly available large dataset DF-Mobio. To improve generalization, we apply different augmentation strategies used during training, including a proposed aggressive ‘data farming’ technique based on random patches. We also tested two few-shot tuning methods, when either a first convolutional layer or a last layer of a pre-trained model is tuned on 100 seconds from a training set of the test database. The experimental results clearly expose the generalization problem of deepfake detection methods, since the accuracy drops significantly when a model is trained on one dataset and evaluated on another. However, the silver lining is that an aggressive augmentation during training and a few-shot tuning on the test database can improve the accuracy of the detection methods in a cross-database scenario. As a side observation, we show the importance of database selection for training and evaluation, as FaceForensics++ is found to be better to use for training, while DeeperForensics is found to be significantly more challenging as a test database.

Micro-sized open-source and low-cost GPS loggers below 1 g minimise the impact on animals while collecting thousands of fixes.

GPS-enabled loggers have been proven as valuable tools for monitoring and understanding animal movement, behaviour and ecology. While the importance of recording accurate location estimates is well established, deployment on many, especially small species, has been limited by logger mass and cost. We developed an open-source and low-cost 0.65 g GPS logger with a simple smartphone-compatible user interface, that can record more than 10,000 GPS fixes on a single 30 mAh battery charge (resulting mass including battery: 1.3 g). This low-budget 'TickTag' (currently 32 USD) allows scientists to scale-up studies while becoming a 'wearable' for larger animals and simultaneously enabling high-definition studies on small animals. Tests on two different species (domestic dog, Canis lupus familiaris and greater mouse-eared bats, Myotis myotis) showed that our combination of optimised hardware design and software-based recording strategies increases the number of achievable GPS fixes per g device mass compared to existing micro-sized solutions. We propose that due to the open-source access, as well as low cost and mass, the TickTag fills a technological gap in wildlife ecology and will open up new possibilities for wildlife research and conservation.

MODL-28. Patient-derived, three-dimensional organoid platform for pediatric brain tumor modeling

Brain tumors have become the leading cause of cancer-related death in children. An important hurdle to scientific and clinical progress in the field has been the limited availability of preclinical tumor models. Historically, few pediatric brain tumor cell lines have been established and these often poorly recapitulate the phenotypes of the original tumors. In recent years, the Children’s Brain Tumor Network (CBTN) has accelerated the development of patient-derived cell lines and xenografts, offering these resources to the community through open-source access. While these models are extremely valuable, their development process can be lengthy and result in clonally selected lines which presents a challenge for studying complex tumor biology. To address the need for three-dimensional tissue culture, our group in conjunction with CBTN, utilized organoid culture from fresh tissue specimens obtained directly from surgical resection of various pediatric brain tumor histologies. This resulted in the development and banking of over 30 organoid models, which included ependymoma, high-grade glioma, medulloblastoma, atypical teratoid-rhabdoid tumor, diffuse midline glioma, and low-grade glioma diagnoses. Tissue was processed within an hour post extraction and cultured with universal media composition for each diagnosis. Organoid growth was observed within 2-3 weeks of initiation and continued for up to three months before banking. Banked organoids established growth upon return to culture. Phenotypic analysis revealed organoid cell composition that represented clinical histology. Importantly, organoids returned to culture post-banking demonstrated similar cell composition to those in the original culture, indicating their utility for subsequent preclinical testing. Here we provide a simple and efficient workflow for the generation and characterization of three-dimensional tumor organoids generated from fresh surgical pediatric brain tumor tissue. The platform has the potential to accelerate investigations into tumor biology and empower a diverse array of translational studies for the pediatric brain tumor field.

0001 Associations Between Circadian Factors and Travel Distance with Performance: A retrospective analysis of 2014-2018 National Basketball Association Data

Abstract Introduction Frequent travel across time zones and travelling long distances interferes with healthy sleep and disrupts the circadian system, often degrading athletic performance. National Basketball Association (NBA) players face a demanding travel schedule often requiring multiple games per week, with games spanning the continental United States. This investigation aimed to clarify the influence of circadian factors and travel distance on NBA performance using a dataset from the 2014-2018 seasons. Methods NBA (2014-2018) game data were acquired from an open-access source: (https://www.kaggle.com/ionaskel/nba-games-stats-from-2014-to-2018). Circadian variables of time zone change (TZ∆) and adjusted jet lag (AJL) were formulated, with quadratic versions utilized across analyses. TZ∆ captured circadian delay/advance based on travel for a game, with each TZ going eastward and westward reflected by -1 and +1, respectively. AJL advances TZ∆ by allowing acclimation to a novel TZ, with each day resulting in a 1-unit change towards circadian neutral. AJL is a season-long rolling summation, which was computed using two different travel approaches: Approach1 (AJL1) assumes travel the day before each game, whereas Approach2 (AJL2) was designed to prioritize being home. A standardized flight tracker determined travel distance for each game (GameDistance). Team ability differences, characterized as difference in season win percentages (SeasonWinPerDiff), served as an analytic covariate. Game point differential (PointDiff), defined as a team’s score minus their opponent’s score, and a team’s free throw percentage (FreeThrowPer) served as outcome variables. Linear mixed-effects modeling assessed univariate and multivariate associations, with games nested within both team and year. Results AJL2 (β = -0.63; p = .01) and GameDistance (β = -0.73; p<0.0001) significantly associated with PointDiff. TZ∆ (β = -0.002; p = .03), AJL1 (β = -0.002; p =.04) and GameDistance (β = -0.003; p = 0.007) significantly associated with FreeThrowPer. AJL2 and GameDistance maintained significant relationship with PointDiff in fully adjusted model that included AJL2, GameDistance, and SeasonWinPerDiff. Conclusion Results suggest that both circadian delay/advance and greater distance traveled for games negatively influence NBA performance, even when controlling for differences in team ability. Season travel and flight plans could be constructed to reduce the effects of circadian misalignment and travel distance. Support (If Any) None

SexEst: An open access web application for metric skeletal sex estimation

AbstractSkeletal sex estimation is an essential step in any osteoarcheological study; hence, several metric and morphological methods have been developed for this purpose, employing different skeletal elements. This paper has a dual purpose: (1) test the performance of several machine learning classification models for skeletal sex estimation using worldwide samples of cranial and postcranial measurements and (2) present a free web application for the implementation of the models that exhibit the highest accuracy so that the sex of unknown skeletons can be straightforwardly estimated. Regarding the first objective, using the Goldman database of postcranial metrics and the William W. Howells craniometric database, machine learning classification models were constructed for sex prediction. The models were optimized with respect to their hyperparameters and cross‐validated reaching accuracies ranging from 80.8%–89.5% for the postcranial data and 81.2%–87.7% for the cranial data. The models offering the highest rates of correct sex classification (Extreme Gradient Boosting, Light Gradient Boosting, and Linear Discriminant Analysis) were then selected to construct an open access and open source web application, SexEst, for predicting the sex of unknown skeletons.

BEST PRACTICE KEGIATAN LITERASI DI PUSAT PERPUSTAKAAN DAN PENYEBARAN TEKNOLOGI PERTANIAN

This study aims to determine the literacy process for collecting and managing information in the repository and iTani and the use of electronic information as a source for compiling pathfinder secondary publications and selected latest information. This paper also examines information literacy activities to expand the use of electronic information both onsite and virtual. This study uses a descriptive method to describe the state of the subject or object in the study. PUSTAKA has managed electronic information sourced from publications within the Ministry of Agriculture, open access sources on the internet and also e-journal databases. Electronic information becomes a source of information in the preparation of the pathfinder and the latest collection of information is selected to make it easier for users to search. Literacy on the use of online information is carried out face-to-face and virtual during the pandemic. Promotion of literacy activities continues to be carried out so that users can participate and become literate individuals.

MODELING THE EXCHANGE RATE OF THE KAZAKHSTANI TENGE

The article discusses the role of key factors in the exchange rate formation of the Kazakhstani tenge. The study analyzes which factors are key for short-term and long-term forecasting. A linear regression model is built, with the STEP function on quarterly data from 01/01/2005 to 06/01/2021 using free statistical software with open source access and the R programming language. The STEP function allows you to choose the best model from one dependent and 14 independent variables. An analysis of domestic and foreign studies is carried out to identify the degree of relationship (positive or negative) between the key factors affecting the nominal exchange rate of the national currency of Kazakhstan. The relationship between the nominal exchange rate and direct or indirect state intervention is investigated. Access to data is a key issue for qualitative forecasting of the nominal exchange rate of the Kazakhstani tenge and affects the predictive power of the model. As recommendations, it is proposed to provide economic agents with KASE data. In conclusion, conclusions are drawn about the presence of objective prerequisites that impede the transition to a free-floating exchange rate in Kazakhstan.

Technip Energies gets FEED contract for Encina[StQuote]s commercial PFCC unit

One of the most promising renewable energy sources (RES) is photovoltaic energy system. It generates power by using a photovoltaic (PV) module. The solar illumination and temperature value are the main parameters for the PV module because a small change can affect the entire power generation. The PV module is based on the double diode equation for extracting unknown parameter values and tracking maximum power point using basic analytical methods (Newton Raphson and Lambert W-function), particle swarm optimization (PSO) and artificial neural network (ANN) algorithms are performed. Then, a deep neural network (DNN) algorithm is proposed in this work for alleviating the disadvantages of the existing methods. Particularly, the DNN algorithm is proposed for reducing the mismatching power loss. Firstly, two-parameter values retrieve from a specific location at NASA open-access source for the year 2020. Then, the parameter values optimization through two analytical methods such as Newton Raphson (NR) and Lambert-W function (W-function) methods. Based on these analytical methods, the characteristic curve is executed. It is compared one among one and also the analytical methods evaluates with soft computing algorithms. The soft computing algorithm represents particle swarm optimization and the artificial neural network. Both the analytical methods and the soft computing algorithms compare for the extraction of the unknown parameter value for the PV module. Then, the existing algorithm is compared with advanced soft computing algorithms. In that deep neural network, the algorithm is implemented for the maximum power point tracking (MPPT) process. The DNN algorithm contains two inputs, three hidden layers and one output. The input neuron is defined with the voltage and current value and the hidden layer contains the sigmoid activation function. The result evaluates in terms of percentage of error for maximum power point (PEmpp).

Testicular cancer and YouTube: What do you expect from a social media platform?

To evaluate the quality information on testicular cancer uploaded on YouTube™ videos. YouTube™ videos were searched using "Testicular cancer" as a keyword. The Patient Education Materials Assessment Tool, the Misinformation scale, and the DISCERN tool were used to assess the quality information of YouTube™ videos on testicular cancer. According to the selection criteria, 121 YouTube™ videos were collected for the analysis and stratified according to uploading year (2009-2014 vs 2015-2020). According to the Patient Education Materials Assessment Tool for audio-visual content, the overall Understandability score was 60% (interquartile range 45.5-75) and the overall Actionability score was 100% (interquartile range 66.7-100). According to the Misinformation scale, the lowest median was recorded for item 6 ("Effects on fertility") and the overall median Misinformation score was 2 (interquartile range 1.3-2.8). No statistically significant differences were observed according to uploading year (all P > 0.05). Of all, only 54 (44.6%) videos mentioning treatment were subsequently analyzed. Of these videos, the overall Understandability was 71.4% (interquartile range 56.3-84.6) and the overall Actionability was 100% (interquartile range 66.7-100). The overall Misinformation score was 2.8 (interquartile range 2.2-3.5). The median DISCERN score recorded for question 16 was 5 (interquartile range 3-5). YouTube™ is a fast and open-access source for mass information. The overall quality of the testicular cancer contents provided is sadly unsatisfactory, in the present likewise in the past. However, YouTube™ videos mentioning treatment options showed higher quality content, than the remaining one. Nevertheless, all the videos analyzed underestimated the testicular cancer effects on fertility. Nowadays, YouTube™ cannot be recommended as a reliable source of information on testicular cancer.

A Systematic Review and Research Agenda of Portrayal of Cities in Select Indian English Fiction: Neti Neti: Not This, Not This by Anjum Hasan, The Lives of Others by Neel Mukherjee and Chennaivaasi by T. S. Tirumurti

Purpose: Cities have always found a place in novels, as a setting which wields influence. Much great writing across cultures has been devoted to figuring out this intangible, yet resolute influence. The city is definitely an agent of transformation in many English works of fiction and this paper highlights the works of researchers who have examined the various ways in which cities are being foregrounded to offer them prominence in the narration and make them significant players in the overall presentation. The impact on the lives of the characters, plot and narration of the novels due to the spatial and cultural ethos of the city, the ways in which the city is portrayed and the reflection of this on the reader form the gist of the findings of the research Design/Methodology/Approach: The review of literature is carried out using information from various online open access sources such as peer-reviewed journals, doctoral theses, websites and a range of portals such as Google Scholar. Findings/Result: Optimizing the reading experience to help the reader move beyond a superficially touristy perspective of the space used as a setting and engage with the city’s complex social, political, historical, and cultural milieu requires a deeper study of the different ways in which the city can be foregrounded in novels. Originality/Value: This paper examines the phenomenon of the projection of space in novels and memoirs. A novel concept of the author as a literary cartographer, who makes the work of fiction come alive by not only describing the city as text but also going beyond to offer maps as an aid to the narration and in the process enriching the total reader experience, is presented. Different ways in which the city has been melded into the novel, touching upon its history, architecture and cultural aspects so as to be a significant part of the narrative and wield influence, have been identified. Paper Type: Content analysis and exploratory study.

ARviz: An Augmented Reality-Enabled Visualization Platform for ROS Applications

Current robot interfaces, such as teach pendants and 2D screen displays, used for task visualization and interaction often seem unintuitive and limited in terms of information flow. This compromises task efficiency as interacting with the interface can distract the user from the task at hand. Augmented reality (AR) technology offers the capability to create visually rich displays and intuitive interaction elements in situ. In recent years, AR has shown promising potential to enable effective human–robot interaction (HRI). We introduce ARviz, a versatile, extendable AR visualization platform built for robot applications developed with the widely used robot operating system (ROS) framework. ARviz aims to provide both a universal visualization platform with the capability of displaying any ROS message data type in AR, as well as a multimodal user interface for interacting with robots over ROS. ARviz is built as a platform incorporating a collection of plugins that provide visualization and/or interaction components. Users can also extend the platform by implementing new plugins to suit their needs. We present three use cases as well as two potential use cases to showcase the capabilities and benefits of the ARviz platform for HRI applications. The open access source code for our ARviz platform is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/hri-group/arviz</uri> .

Medical Students and Youth-Led Efforts against Infodemic

PurposeThe IFMSA, voicing the opinion of 1.3 million medical students from 131 countries, acknowledges the importance of health literacy in driving social change. Today, the Pandemic is accompanied by a global epidemic of misinformation, spreading rapidly through social media platforms and other outlets, posing a critical threat to public health due to the COVID-19 outbreak. As this problem continues to mount, it becomes even more evident that a unified approach is required to secure high levels of compliance with public health measures and combat the infodemic.Methods & MaterialsA global study was conducted by IFMSA, in collaboration with the WHO, composed of a survey to get data about all the organizations, institutions, NGOs, and other entities that focus on fact-checking and correcting misinformation about COVID-19. The survey was filled by medical students from the end of April to the end of May who reported name, type, the scope of work, languages, primary funding source, type, and source of information shared by the organization.ResultsWe discovered 182 initiatives from 62 countries worldwide that verified information in 48 languages. Social media, the internet, radio, SMS, printed media, and hearsay were identified as the main sources of misinformation. Video podcasts with experts, regular social media updates and newsletters, were described as best practices, in addition to debunking myths on a regular basis and verifying statements by public figures. Also, the quality of fact-checking differed between initiatives.ConclusionData showed that myths and false information are spreading through different means from public figures to daily social media outlets. Fighting misinformation should use innovative and accessible approaches, There is an urgent need for national initiatives and political engagement for myth-busting. IFMSA and WHO is following up by designing a platform to share fact-checking initiatives and recommendations openly, and by creating an AI system with Amazon to analyze articles in social media. Our surveys identified the need for fact-checking quality and quantity improvement and help provide an open-access source for worldwide and national fact-checking initiatives.

The Technological-Pedagogical Knowledge for In-Service Teachers in Primary Education: A Systematic Literature Review

This article presents the results of a scientific literature analysis based on Kitchenham’s (2004) proposal, regarding the technological-pedagogical knowledge of the content under the TPACK model. Research studies containing data-driven information in primary education were revised. The selection of the 622 articles was conducted in Scopus, WoS (Web of Science), ERIC (Educational Resources Information Center), and Google Scholar databases; considering the period from which this model was initially described to May 2019. The following criteria were also observed: open access sources, referring to social sciences, and full text available. To conclude, there is limited scientific production regarding TPACK model in primary education, with 3,05% of the articles reviewed in this systematic literature review. An increase in using the TPACK model in terms of teacher knowledge and enriched environments with information and communication technology has also been observed. Participation of other members of the school community as students, parents and managers is also recommended.

Internet of Things Platforms for Academic Research and Development: A Critical Review

Tens of different IoT platforms are currently available on the market as a result of the high interest in IoT, characterized by very different characteristics in terms of utilization models, features and availability. This paper provides a review of existing platforms, both adopting a closed source and an open source access model, focusing on five evaluation criteria: communication protocols, data visualization, data processing, integration with external services and security. Afterward, the paper focuses on ten open source platforms, that are deemed more suitable for research and development activities in academia, and provides an evaluation of such platforms according to the five criteria previously defined, combined with two criteria specific to open source platforms: installation procedure and documentation. The evaluation indicates that the FIWARE platform is the best suited platform when taking into account the combination of the seven criteria; other platforms might, however, be preferred, depending on the context, thanks to specific features such as native support for a programming language, or ease and flexibility in the installation procedure.

Which states will lead a just transition for the Arctic? A DeePeR analysis of global data on Arctic states and formal observer states

A fair and equitable low carbon future depends on a just transition which, in turn, requires leadership. Where the Arctic is concerned, this leadership is currently lacking. To gauge which states are most likely to provide leadership in the global energy transition, a quantitative rank-percentile assessment of 21 Arctic Council members and Observer states was conducted, using measures relevant to the just transition. Data from multiple open-access sources were combined, creating a model to ‘evaluate energy and equity aspects of Distributional, Procedural and Restorative’ justice (DeePeR). Results suggest normative leadership on a just transition for the Arctic comprises international climate contributions in line with carbon emission records and a commitment to both fair and green jobs. Reflections are made on the positive and negative effects of a more involved EU for the just transition agenda in the Arctic.

The methodology of food design. Part 2. Digital nutritiology in personal food

Atherosclerosis (the main cause of a wide range of cardiovascular diseases) and other multifactorial diseases depend on several nutrition factors, defined in general by lifestyle that directly and constantly affects the human body. The modern level of science and technology development is able to form a diet, taking into account all personal characteristics in such a way that makes nutrition an effective preventive measure against diseases in order to keep a person healthy. The purpose of this article is to define and study all the limitations (the scope of its coverage in the scientific literature) that arose in the process of research aimed to formation of an integrated personal approach to designing of nutrition to prevent atherosclerosis. The object of the study was scientific literature, which is available in open source and free access databases: PubMed, ScienceDirect, eLIBRARY.RU, www.fips.ru, Patentscope. The language of search is Russian and English, search depth is 12 years. In the course of the research two food design concepts were found that affect process of digitalization in the food sector: the concept “FoodTech” (food technology) and digital nutritiology. It was established that in Russia only one company — LLC “City Supermarket” (Moscow), that works with the brand “Azbuka Vkusa” — acts in the sphere of “FoodTech” on the Russian market. This company selects personalized food, taking into account the results of personal nutrigenetic tests, in cooperation with LLC “Genotech” (Moscow). There is a need to use biological information, statistical information processing (nutrigenetic studies, nutrigenomic research) and machine-aided data processing (machine learning) for further generation of automatic algorithm that compiles personal recommendations. The relevance of generation of a national domestic database on chemical composition of food products (presented in the market) to simplify the preparation of individual personal diets is observed. We underline the necessity to use the test-organisms, i. e. dorio fish / zebrafish (Danio rerio) and nematodes (Caenorhabditis elegans)), which were used to determine the activity of candidate substances — the biologically active substances that feature antiatherosclerotic properties. In the future the authors plan to conduct a nutrigenomic and nutrigenetic study, using digital achievements. To collect information about consumers, it is necessary to apply digital devices, and use biological informatics to process the results; after that it is necessary to generate the algorithm for automatic selection of personalized dietary recommendations.

Entrepreneurship and SMEs under COVID-19 crisis: A literature review

Objective: The article aims to summarize the impact of the COVID-19 crisis on SMEs, considering both its positive and negative effects. Also, the study seeks to summarize the response approaches usable for SMEs to face a crisis. Research Design &amp; Methods: This article uses a critical literature review to summarize the international literature developed on the COVID-19 crisis topic. It considers scientific papers published from 2017 to February 2022 with a focus on specific keywords, English language, from the open access sources on (i) Emerald, (ii) JSTOR, (iii) ScienceDirect, and (iv) Springer, (v) IER, and (vi) EBER. Findings: The paper sheds light on how the uncertainty caused by the COVID-19 crisis can impact SMEs. The paper tries to summarize the literature on how SMEs can reverse the uncertainty caused by a crisis and benefit from it. Also, the paper reviews the fact that some SMEs cannot cope quickly enough with the changes in the environment and forcibly shut down their business. The most damaging impact of the COVID-19 crisis is also reviewed in this paper which is the business closure due to lack of resources or disconnection of SMEs from their partners. Implications &amp; Recommendations: This study provides an overview of the positive and negative effects that the COVID-19 crisis had on SMEs. Three approaches were reviewed, uncertainty, resilience, and opportunity, which the decision-makers can use to plan their strategies more effectively and face impending crises more efficiently. This paper also introduces gaps such as the lack of evidence of how COVID-19 affects the entrepreneurship phenomena, which is worth being studied. Contribution &amp; Value Added: This study summarizes the three approaches mentioned in various research papers that SMEs can take during crises and presents gaps worth researching. While many other studies study entrepreneurship at macro levels, the approach in this study is unique as it considers a particular piece of entrepreneurship area and looks at it in terms of small and medium-sized enterprises, describes why COVID-19 is a crisis to the business environment and contributes to the literature to explain how entrepreneurship and SMEs are influenced by COVID-19 situation. I summarized some of the issues SMEs face during a crisis and mentioned the approaches to be taken by business owners during such times using articles conducted in the last five years.

Сравнительный анализ сохранности водораздельных государственных защитных лесных полос Волгоградской области по данным Sentinel-2 NDVI и информационных продуктов типов земного покрова

In this paper, an analysis of the preservation of watershed state protective forest strips (GZLP) on the territory of the Volgograd region was carried out. Three protective strips were analyzed: “Kamensk–Penza”, “Kamyshin–Volgograd”, “Volgograd–Cherkessk”. The data of remote sensing of the earth (remote sensing) from the Sentinel-2 satellite with a spatial resolution of 10 meters were used and the data obtained were compared with alternative open access sources on the state of the forest coverage of the territory: GlobalLandCover30 (GLC30), ESRI, Global Forest Change (GFC). Mapping of the research object was carried out in the geographic information system QGIS, the reason for the choice is the free usage and the availability of versatile modules for working with remote sensing data of the Earth. With the help of Sentinel-2 A-B RGB images and a public cadastral map, mapping of the project area of state protective forest strips was carried out, after which the forest canopy was classified using the normalized vegetation index NDVI for August-September. The areas of forest strips located on various soil-forming rocks are calculated on the basis of the soil map of the Volgograd region in 1984 M 1:400,000. The studied GZLP occupy an area of 13,094.1 hectares of 9 forest areas of the Volgograd region. The safety of the GZLP was determined as follows: the ratio of the area actually occupied by forest plantations to the projected area of the forest strip based on the classification of NDVI index images and information products of the types of land cover and projective forest cover. Data on the safety of state protective forest strips in the Volgograd region have also been prepared on the basis of official statistics. A comparison of four open access data sources on the coverage of state forest strips with forest plantations was carried out. The results obtained will serve as a basis for further assessment of the causes of the degradation of GZLP, including field studies on areas without forest cover identified using the above methods.

Lung cancer mortality trends in Mexico, 1998–2018: the impact of the General Law on Tobacco Control

ABSTRACT: Objective: The aim of this study was to describe trends in lung cancer (LC) mortality and trends in tobacco use. Methods: This is an ecological time-series study to determine mortality trends due to lung cancer resulting from tobacco consumption, based on secondary open-access sources, such as the National Surveys. Smoking prevalence, tobacco use trends, mortality rates, and percentage were determined by LC. The mortality trend from LC was calculated, and a linear regression analysis was performed to evaluate the impact of the General Law for Tobacco Control. Results: The prevalence of tobacco use decreased steadily between 1988 and 2015, particularly in men. Mortality rates and percentage decreased between 1998 and 2018. During this period, the mortality rate decreased from 6.3 to 5.4 per 100,000 population (−0.032/100,000 each year, p&lt;0.001), with a net decrease of 0.9 per 100,000. We observe increases in mortality in women in the central and southeastern regions. Of 32 states, 18 showed a tendency to loss (p&lt;0.005). The rate of change for men was −0.24, with a total reduction of 2.17 before the introduction of the laws and −0.32 after their introduction, a total reduction of 3.24 (p&lt;0.005). Women showed no reduction. Conclusions: Mortality rates showed a limited decrease. Strategies need to be strengthened, mainly in the central and southeastern regions, and to focus on the control of tobacco use by women.