Some challenges and opportunities for Russia and regions in terms of the global decarbonization trend

This article discusses a possible scenario of energy transition in Russia, taking into account the economic structure, presence of huge oil and gas infrastructure and unique natural resources. All this allows to consider global trends of energy and economic decarbonization not only as a challenge, but also as a new opportunity for the country. Considering developed oil and gas production, transportation, refining and petrochemical infrastructure, as well as the vast territory, forest, water and soil resources, our country has unique opportunities for carbon sequestration using both biological systems and the existing oil and gas infrastructure. It is proposed to use the existing oil and gas production facilities for hydrogen generation in the processes of hydrocarbon catalytic transformation inside the reservoir. It is suggested to create and use large-scale technologies for CO2 sequestration using existing oil and gas production infrastructure. Considering high potential of the Russian Federation for carbon sequestration by biological systems, a network of Russian carbon testing areas is being developed, including one at Kazan Federal University (KFU), – the “Carbon-Povolzhye” testing area. The creation of carbon farms based on the applications at such testing areas could become a high-demand high-tech business. A detailed description of the KFU carbon testing area and its planned objectives are given.

Desenvolvimento de um Gerador de Programas Aleatórios em Java

With great advances in the computer science area where technologicalsystems are becoming more and more complex, tests are hardto perform. The problem is even more serious in critical systems,such as flight control or nuclear systems, where an error can causecatastrophic damage in our society. Currently, two techniques areoften used for software validation: testing and software verification.This project aims the testing area, generating random programs tobe used as input to property-based tests, in order to detect errorsin systems and libraries, minimizing the possibility of errors. Morespecifically, Java programs will be automatically generated from existentclasses and interfaces, considering all syntactic and semanticconstraints of the language.

Automatic landslide detection using the Random Forest classification - the importance of the train-test split ratio

<p>Landslide identification is the fundamental step to reduce the potential damaging effects of landslide activities. A variety of techniques and approaches has been developed to detect landslides. Conventional landslide identification is a complex and laborious task due to a large amount of the field work and materials that have to be investigated. Additionally, the conventional geomorphological mapping mainly provides a subjective representation of landscape complexities at different scales. Sometimes, in certain conditions, such as densely-vegetated terrain, conventional landslide mapping is ineffective or even impossible.</p><p>Therefore, innovative methods that allow for the reduction of subjectivism, time, and effort have increasingly become the subject of interest in landslide research. These methods mainly focus on semi-automated or automatic landslide mapping and include analysis of remote sensing data, such as optical images, Digital Elevation Models (DEMs) derived by Light Detection and Ranging etc. Among them, the pixel-based approach (PBA) and the object-based image analysis (OBIA) methods can be distinguished, for which supervised classification methods are usually utilized.</p><p>The accuracy of supervised classification methods strongly corresponds to the training samples - its quality and amount. Supervised classification methods require the collection of training as well as testing data to generate and assess the accuracy of the classification results. It is a challenging task, especially in forested areas, to capture ground truths of the good quality to train the classifier and to identify landslides. Considering this, we decided to investigate the following research question: What is the appropriate training–testing dataset split ratio in supervised classification to detect landslides in a testing area based on DEMs? Since PBA and OBIA approaches are nowadays widely utilized, we investigated this issue for both methods. The Random Forest classifier was implemented for both methods. The experiments were performed in Poland in the Outer Carpathians.</p><p>Accuracy measures calculated for the region growing validation indicated that the training area should be similarly large to the testing area in DEM-based automatic landslide detection. Additionally, we found that the OBIA approach performs slightly better than PBA when the quantity of training samples is lower. Besides this, we also attempted to increase the detection performance and to generate final landslide inventory. For this purpose, the intersection of the OBIA and PBA results together with median filtering and the removal of small elongated objects were carried out. We achieved the Overall Accuracy of 80% and F1 Score of 0.50.</p>

Using IoT to Dynamically Test Smart Connected Devices

The aim of this paper is to introduce and validate a new concept of testing smart connected devices (SCD) that have inertial measurement units (IMU) or gyroscopic and accelerometers, like smart bracelets, smart watches or even mobile phones after they have been assembled, in their way to the packaging area. The devices will be tested dynamically, while they travel on the conveyor using the IoT (Internet of Things) and some expected values for acceleration and angle variations. Using IoT to dynamically test electronic SCD, while they travel on a conveyor after the assembly process had been completed, will reduce the time to market and will exclude the need for a testing area. Once the SCD arrives at the packing area, the system will know and sort the device that are QA (Quality Assurance) passed or failed (the devices that will not send the expected values to the system). In order to prove this concept, we created a SCD that use an IMU connected to a Raspberry PI, boxed together. The PI is connected to ThingWorx (IoT platform that includes machine learning) and the box travel on different types of conveyor belts in order to validate this new concept.

PECULIARITIES OF MORTALITY IN 2020 OF THE ALTAI TERRITORY RESIDENTS WHO SUFFERED FROM RADIATION EXPOSURE

In the Altai Territory, the Altai Medical Dosimetric Register is functioning, containing information on the health status of the inhabitants of the Territory exposed to radiation. The most numerous contingents are persons exposed to radiation as a result of nuclear tests at the Semipalatinsk testing area. The aim of the study is to obtain up-to-actual data on the health status of persons listed in the register. This research analyzed mortality rates for 2019 and 2020. Preliminary data for 2020 showed significant reductions in overall mortality and mortality from specific causes for the majority of the largest populations in the register.

Machine Learning Strategy for Gut Microbiome-Based Diagnostic Screening of Cardiovascular Disease

Cardiovascular disease (CVD) is the number one leading cause for human mortality. Besides genetics and environmental factors, in recent years, gut microbiota has emerged as a new factor influencing CVD. Although cause-effect relationships are not clearly established, the reported associations between alterations in gut microbiota and CVD are prominent. Therefore, we hypothesized that machine learning (ML) could be used for gut microbiome–based diagnostic screening of CVD. To test our hypothesis, fecal 16S ribosomal RNA sequencing data of 478 CVD and 473 non-CVD human subjects collected through the American Gut Project were analyzed using 5 supervised ML algorithms including random forest, support vector machine, decision tree, elastic net, and neural networks. Thirty-nine differential bacterial taxa were identified between the CVD and non-CVD groups. ML modeling using these taxonomic features achieved a testing area under the receiver operating characteristic curve (0.0, perfect antidiscrimination; 0.5, random guessing; 1.0, perfect discrimination) of ≈0.58 (random forest and neural networks). Next, the ML models were trained with the top 500 high-variance features of operational taxonomic units, instead of bacterial taxa, and an improved testing area under the receiver operating characteristic curves of ≈0.65 (random forest) was achieved. Further, by limiting the selection to only the top 25 highly contributing operational taxonomic unit features, the area under the receiver operating characteristic curves was further significantly enhanced to ≈0.70. Overall, our study is the first to identify dysbiosis of gut microbiota in CVD patients as a group and apply this knowledge to develop a gut microbiome–based ML approach for diagnostic screening of CVD.

On the Importance of Train–Test Split Ratio of Datasets in Automatic Landslide Detection by Supervised Classification

Many automatic landslide detection algorithms are based on supervised classification of various remote sensing (RS) data, particularly satellite images and digital elevation models (DEMs) delivered by Light Detection and Ranging (LiDAR). Machine learning methods require the collection of both training and testing data to produce and evaluate the classification results. The collection of good quality landslide ground truths to train classifiers and detect landslides in other regions is a challenge, with a significant impact on classification accuracy. Taking this into account, the following research question arises: What is the appropriate training–testing dataset split ratio in supervised classification to effectively detect landslides in a testing area based on DEMs? We investigated this issue for both the pixel-based approach (PBA) and object-based image analysis (OBIA). In both approaches, the random forest (RF) classification was implemented. The experiments were performed in the most landslide-affected area in Poland in the Outer Carpathians-Rożnów Lake vicinity. Based on the accuracy assessment, we found that the training area should be of a similar size to the testing area. We also found that the OBIA approach performs slightly better than PBA when the quantity of training samples is significantly lower than the testing samples. To increase detection performance, the intersection of the OBIA and PBA results together with median filtering and the removal of small elongated objects were performed. This allowed an overall accuracy (OA) = 80% and F1 Score = 0.50 to be achieved. The achieved results are compared and discussed with other landslide detection-related studies.

Endemism of Uropodina Mites: Spurious or Real?

Analyzing the data from the existing literature about geographic distribution of mites from the suborder Uropodina (Acari: Mesostigmata), one can get the impression that this group of mites is characterized by an unusual extent of endemism on a global scale. This observation encouraged the authors of this study to ascertain whether endemism in Uropodina mites is a real feature of this group or whether it stems from the current state of affairs in this field of research. The study is based on evidence from the literature on the topic and data obtained from long-term research conducted on extensive materials from all over the globe (over 40,000 samples). The discussion presented in the article is supported by many examples, showing that both hypotheses can in fact be proved right. The major point of reference in this study is the fairly well-known fauna of Uropodina in Europe, whereas South America is the testing area for the two hypotheses.

Evaluating the Vulnerability of Several Geodetic GNSS Receivers under Chirp Signal L1/E1 Jamming

Understanding the factors that might intentionally influence the reception of global navigation satellite system (GNSS) signals can be a challenging topic today. The focus of this research is to evaluate the vulnerability of geodetic GNSS receivers under the use of a low-cost L1/E1 frequency jammer. A suitable area for testing was established in Slovenia. Nine receivers from different manufacturers were under consideration in this study. While positioning, intentional 3-minute jammings were performed by a jammer that was located statically at different distances from receivers. Furthermore, kinematic disturbances were performed using a jammer placed in a vehicle that passed the testing area at various speeds. An analysis of different scenarios indicated that despite the use of an L1/E1 jammer, the GLONASS (Russian: Globalnaya Navigatsionnaya Sputnikovaya Sistema) and Galileo signals were also affected, either due to the increased carrier-to-noise-ratio (C/N0) or, in the worst cases, by a loss-of-signal. A jammer could substantially affect the position, either with a lack of any practical solution or even with a wrong position. Maximal errors in the carrier-phase positions, which should be considered a concern for geodesy, differed by a few metres from the exact solution. The factor that completely disabled the signal reception was the proximity of a jammer, regardless of its static or kinematic mode.