Modern Algorithms Research Articles

Modern algorithm of diagnostics and management of patients with chronic atrophic gastritis and intestinal metaplasia of the stomach

<h3></h3> Эндоскопическое исследование верхних отделов пищеварительного тракта с морфологическим исследованием биоптатов слизистой оболочки желудка является важным этапом диагностики предраковой патологии желудка, начальных стадий рака желудка и отбора пациентов для последующего лечения и динамического наблюдения в рамках программы диспансеризации взрослого населения. Согласно приказу Министерства здравоохранения Российской Федерации от 27.04.21 №404н «Об утверждении Порядка проведения профилактического медицинского осмотра и диспансеризации определенных групп взрослого населения», определен перечень мероприятий скрининга и методов исследований, направленных на раннее выявление онкологических заболеваний. В рамках этих мероприятий обязательным является выполнение в возрасте 45 лет эзофагогастродуоденоскопии (ЭГДС), которая при необходимости может проводиться с применением анестезиологического пособия, в том числе в медицинских организациях, оказывающих специализированную медицинскую помощь в условиях дневного стационара. Основными задачами ЭГДС являются выявление и типирование хронического гастрита, инфекции <i>Helicobacter pylori,</i> предопухолевой патологии желудка, оценка распространенности предопухолевых состояний слизистой оболочки и их стадирование, а также обнаружение ранних форм рака желудка и выполнение стандартизированного протокола биопсии. Однако алгоритм проведения эндоскопического исследования и биопсии, необходимость применения современных оптических технологий диагностики предопухолевых состояний слизистой оболочки и порядок наблюдения пациентов с атрофическим гастритом и предраковыми изменениями слизистой оболочки желудка не определен. Новый алгоритм эндоскопической диагностики и ведения пациентов, основанный на отечественных и международных стандартах, позволит повысить эффективность выполнения ЭГДС и наблюдения пациентов в рамках диспансеризации взрослого населения в Российской Федерации.

Justification of the need to introduce an additional criterion to the assessment of the nutrition status of military personnel

Currently, in accordance with the regulations of the Government of the Russian Federation and the Ministry of Defense of the Russian Federation, the main indicator for assessing nutritional status is the body mass index, which does not take into account the component composition of the body. At the same time, it is necessary to assess in more detail the composition of the body of military personnel, that is, the ratio of its muscle and fat components. The purpose of the study is the scientific and methodological substantiation of the need to introduce an additional criterion for assessing the nutritional status of military personnel, that characterize body composition - the body fat percentage. Material and methods. The study involved 1.373 male military personnel aged 18 to 53 who underwent a medical examination. The following methods were used: anthropometry, bioimpedancemetry, caliperometry. Results. Various methods for determining the fat component of the body have been studied. The analysis of the data obtained showed that all methods for determining the body fat percentage have highly significant correlations among themselves and with bioimpedancemetry data. All coefficients had high reliability p<0.001 at r>0.7. When the values of the body mass index deviate from the normative ones, it is proposed to use the indicator of the body fat percentage to clarify the assessment of the nutritional status of male military personnel. Based on the data obtained, modern algorithms for assessing the nutritional status of military personnel have been developed for use in the practice of military health care at various levels of medical support for military personnel. Conclusion. As a result of a prospective study, the need to introduce an additional criterion for assessing the nutritional status of male military personnel was substantiated and proved, and calculation methods for assessing the fat component of the body, comparable with bioimpedancemetry data, were determined. Algorithms for determining the nutritional status of servicemen for different levels of medical support have been developed. In conditions where special devices for determining the fat component of the body are not included in the supply standards of the medical service, it is recommended to calculate the body fat percentage by girth methods using a centimeter tape.

ECONOMICALLY EFFICIENT CACHE FRIENDLY ADJACENCY LIST FOR GRAPH PROCESSING ALGORITHMS

Graphs are a fundamental part of computer science and various scientific fields. They are used in numerous applications, from social networks to electronic design automation. Efficient graph representation is important for modern algorithms, especially in economical contexts, where resource-efficient implementations play a significant role by reducing the resource consumption. This is essential in environments where computing resources are a high priority. There are many representations of graphs as data structure. One of these types is adjacency list: it is collection of linked lists which shows the connection of source and destination vertices. Adjacency lists are more memory-efficient than adjacency matrices because they only allocate memory for vertices with at least one edge in the graph. However, traditional implementations of adjacency lists do not fully use the features provided by modern CPUs, such as the cache line. Our design of Adjacency List is cache friendly and is keeping the main structure of traditional implementation. The main idea is derived from C#’s dictionary implementation for hash tables, which uses one array combining all data that should be in linked lists. Our results show that cache friendly implementation of Adjacency list outperforms it in lookup and insertion operations.

Image encryption with General Singular Values Decomposition via logistic function by encryption key only real number (method-2)

In this algorithm the development of a previous algorithm was a master’s thesis by researcher Maher AL-Bashkani. Where he used a technique (gsvd) with Logistic function to encrypt the image through a composite key which is two keys. The first is a real number and the second is a picture. In this algorithm we will call it (MK-11) we will dispense with the second key (picture) and with only the first key (real number). We will generate a 3D matrix that represents another key we create through the work of our algorithm. This is a new method that has not been used by previous algorithms. We know nothing about this triple matrix we create by implementing the algorithm. Therefore, this algorithm is considered very excellent and possesses high immunity against hackers who try to break the encryption. The work of this algorithm focuses on three steps: The first is to create an array with the same dimensions as the image we want to encode depending on the numerical components of the image matrix as we will see later. The second step is the first distraction of the image matrix by adopting the first encryption key (real number) and logistic function. Step 3 We apply GSVD between the resulting image matrix after the first encoding contained in the second step above and the matrix that was created in the first step above to perform a second encryption step, the final encryption is two steps of encryption which adds complexity to the image encryption makes it difficult to penetrate by hackers. The results we obtained by implementing this algorithm were excellent and compete readings for modern algorithms. It is superior to accuracy and quality. Therefore, this algorithm can be adopted in information security.

Three-Dimensional Modelling of A Bridge by Integrating Terrestrial and Aerial Photogrammetry Applying an Adapted Capture Method

Three-dimensional (3D) photogrammetric modelling is a contemporary remote sensing method for generating digital models with their specific appearance and texture, which are used in various areas of life. The modelling of complex geometry objects, such as masonry bridges, is not an easy task, because of their specific features (the presence of arches and various niches). This determines the choice of an adapted capture method according to the individual characteristics of the object. The paper presents research aimed at generating a highly accurate three-dimensional model of a bridge, by combining terrestrial and aerial photogrammetry. A low-cost unmanned aerial system (UAS) was used for capturing the upper side of the bridge. This significantly optimised capture time. But its capabilities do not allow to capture the bridge arches from the bottom upwards, nor to obtain an accurate model of the bridge railing. Because of this, a terrestrial survey was made with a digital camera, thus complementing the information required to generate a comprehensive model of the bridge. The integration of aerial and terrestrial capturing using low-cost cameras and systems, along with the application of modern algorithms for processing, allow to create precise, accurate, and detailed digital models. It is all very important for future conservation, restoration, adaptation, and socialisation of such type of objects which are monuments of culture.

RESEARCHING OF EFFICIENT DATA PROCESSING ALGORITHMS TO INCREASE THE QUALITY OF INFORMATION TRANSFER IN INFOCOMMUNICATION SYSTEMS

In the process of data processing for their further efficient transmission through the channels of various infocommunication systems, it is of great technical importance to ensure high reliability and speed of transmission. To do this, modern systems use various encoding methods and methods for converting transmitted data. Each of the methods has its own advantages and disadvantages compared to the others. The principle studied in the work, the algorithm for the formation of "temporal signal structures" (TSC) and their further application for data transmission allows much more information to be transmitted in one time interval compared, for example, with positional coding. Also, classical positional coding is inferior in such important data transmission parameters as information capacity and entropy. At the same time, the results of calculating these parameters are greatly influenced by the language (alphabet) of the transmitted textual information, on the basis of which the calculations are carried out, or the type of text used (literary, technical, etc.). Also, the result of calculations can be affected by the amount of data being analyzed (Big Data), the effect is likely to be noticeable in particular cases, and not in general. Under these conditions, the possibility of using a modern adapted method of coding transmitted data in the relevant infocommunication systems becomes especially relevant. The article substantiates the possibility of improving the efficiency of qualitative indicators of transmitted information through the use of modern algorithms based on the use of the TSC ensemble for data processing. It is shown that by changing the restrictions on the basic duration of the element underlying the construction of the signal structure, an increase in the channel productivity was obtained by reducing the energy distance between the signal structures. At the same time, the information parameter in these structures is not the parameters of current or voltage, but time. The analysis of transmission methods in infocommunication systems using traditional coding methods (for example, positional) with redundant codes is carried out. The main disadvantages of positional coding are considered. A method of data processing using TSC is presented, and the results of a study of choosing the effective length of a code word to increase the data transfer rate are analyzed. The results of studying the influence of the parameters of signal timer structures on the data transfer rate are presented. To fully confirm the reliability of the results obtained in the course of theoretical studies, it is necessary to conduct appropriate practical studies, for example, with simulation on a software model of using a particular transmission system with different coding principles.

Continuous global optimization of multivariable functions based on Sergeev and Kvasov diagonal approach

Abstract. One of modern global optimization algorithms is method of Strongin and Piyavskii modified by Sergeev and Kvasov diagonal approach. In recent paper we propose an extension of this approach to continuous multivariable functions defined on the multidimensional parallelepiped. It is known that Sergeev and Kvasov method applies only to a Lipschitz continuous function though it effectively extends one-dimensional algorithm to multidimensional case. So authors modify We modify mentioned method to a continuous functions using introduced by Vanderbei ε-Lipschitz property that generalizes conventional Lipschitz inequality. Vanderbei proved that a real valued function is uniformly continuous on a convex domain if and only if it is ε-Lipschitz. Because multidimensional parallelepiped is a convex compact set, we demand objective function to be only continuous on a search domain. We describe extended Strongin’s and Piyavskii’s methods in the Sergeev and Kvasov modification and prove the sufficient conditions for the convergence. As an example of proposed method’s application, at the end of this article we show numerical optimization results of different continuous but not Lipschitz functions using three known partition strategies: “partition on 2”, “partition on 2N” and “effective”. For the first two of them we present formulas for computing a new iteration point and for recalculating the ε-Lipschitz constant estimate. We also show algorithm modification that allows to find a new search point on any algorithm’s step.

Biometric technology for personal identification and authentication: fingerprint

This paper presents an efficient fingerprint verification algorithm that improves matching accuracy. Algorithms for image processing for biometric technologies of identification and authentication of a person using fingerprints are considered. They are essential tools for improving performance across a wide range of industries. The recognition system is designed for storing data, further processing it, identifying and displaying fingerprints. In forensics, modern image processing algorithms are used and provide a significant improvement in work results. Fingerprint images are degraded and damaged due to changes in skin and print conditions. Thus, image enhancement techniques are used before feature points are detected and fine details are extracted. Templates are generated based on feature vector extraction and databases are created for verification and identification of fingerprint images. The method of experimental research is carried out, the process of processing the identification results is considered. To solve this problem, a recognition system was proposed, which is designed to store data, further process it, identify and display fingerprints. They are used in places of high secrecy as a kind of password access key based on scanning and verifying fingerprints with the FVC2002 database. The results obtained show that the proposed algorithm is more efficient and reliable.

INCREASING CHARACTERISTICS OF BSNR 9С32 DURING IMPLEMENTATION IN ITS ANTENNA SYSTEM OF THE ELEMENTS ON A MODERN ELEMENT BASE

The military conflicts of the last decade are characterized by the wide use of air assets including small - sized and (or) unmanned ones by the warring parties. In modern conditions one of the effective measures to combat them is to increase the accuracy of coordinate information provided to combat (or fire) means. Increasing the accuracy of coordinate information determines: the possibility of non-searching detection and (or) capture of aerial targets by fire means, increasing the survivability of air defense systems due to the reduction of working time (including the time for "radiation") and rapid change of their positions after completing the assigned task.&#x0D; The modern development of science and technology makes it possible to create effective means of air defense to combat existing and promising means of air attack but their development and commissioning requires large costs. In this regard, the issue of improving the characteristics of existing complexes without significant capital investments is gaining relevance.&#x0D; One of the important characteristics of the detection means and tracking of a multi-channel air assets anti- aircraft missile complex is the resolution and accuracy of measuring the angular coordinates of objects. This is due to the fact that modern air defence anti-aircraft missile complex uses information about the angular velocities of the missile-target line and the missile velocity vector when aiming anti-aircraft guided missiles using the proportional approximation method. Therefore, the accuracy of their measurement depends on the features of the missile’s flight trajectory when firing at the target and as a result the probability of its damage. The resolution and accuracy of angular coordinate measurement depends significantly on the characteristics of the antenna system.&#x0D; It is proposed to use their analogues on a modern element base instead of standard multi-channel missile guidance station 9C32 antenna elements in the work . Estimates of the characteristics of the phased antenna array when introducing promising pass-through ferrite phase shifters (in particular the increase in the accuracy of measuring angular coordinates and the range of aerial targets detection) were obtained using mathematical modeling and calculation methods. Obtained ratios allow for an appropriate assessment.&#x0D; The obtained results can be used in the development of proposals for the introduction of modern antenna elements (ferrite phase shifters) into the antenna systems of multi-channel radio equipment of anti-aircraft missile systems.The last decade is characterized by a number of military conflicts, in most episodes of which the achievement of the goal (reconnaissance and (or) defeat of certain targets) by the warring parties is mostly achieved through the use of aircraft, including small and (or) unmanned. The main difference is to obtain the desired results without a direct collision with the enemy ground component of troops (forces).&#x0D; Most of the recommended measures to combat aircraft in modern conditions can be reduced to: the rational construction of battle order (using the air defense separation), the widespread use of covert communications, radio reconnaissance and electronic warfare, the creation of erroneous and camouflage combat (reserve) air defence means positions, automation of transmission and processing of information about the air situation with the use of modern technologies and increase the accuracy of coordinate information provided to combat (or fire) means.&#x0D; Improving the accuracy of coordinate information causes: the possibility of searchless detection and (or) capture of air targets by fire, increasing the survivability of air defense systems by reducing working hours (including time for “radiation”) and rapid change of their positions after the task. Modern algorithms for processing coordinate information provided by air defense systems take into account the errors of primary coordinates measurement by radio engineering, the delay time of information in communication channels and its possible distortion. At the same time, they do not explicitly take into account the influence of topographic errors and orientation errors of radio equipment.&#x0D; In the work with the use of mathematical modelling methods, an assessment of the characteristics of the phased antenna array was carried out with the introduction of a promising pass-through ferrite phase shifter (in particular, an increase in the accuracy of measuring angular coordinates and the range of detection of aerial targets). The obtained ratios allow to make an appropriate assessment.&#x0D; The obtained results can be used when introducing modern ferrite phase shifters to the antenna system of the multi-channel missile guidance station 9С32.

FORECASTING THE RESULTS OF THE PRESIDENTIAL ELECTIONS IN FRANCE BASED ON TWITTER DATA

This paper presents the study to collect, store and analyze data from Twitter to forecast French presidential election results, compared to sociological polls. The first and probably the most important step of the research is to collect, store and clean data, the whole result depends on the amount and quality of data. In the next step of research, datasets are analyzed. Lastly, complete report and visualizations are provided. In the study, we propose modern technics, mathematical algorithms, and machine learning approaches to analyze big amounts of data from the Twitter social network in order to forecast the 2022 French presidential election results. The determined outcome is compared with sociological polls and the real results of elections.&#x0D; In the conducted research modern types of media are compared to select the best one for election prediction. Selected Twitter social network as the one with the most appropriate data and availability to download big amounts of useful information. The approach based on the usage of Python programming language, Selenium browser emulation and MongoDB database was used to collect, store and clean data about the main French election candidates – Emmanuel Macron and Marine Le Pen. The research was made from August 2021 until the election itself in April 2022. The determined outcome is compared with sociological polls and the results of elections and showed that analysis of social network data could be a good alternative to traditional sociological polls as it shows the same trends month by month and well predicted the win of Emmanuel Macron in elections. Moreover, the proposed approach has its benefits compared to sociological polls such as always being fresh, and close to real-time information, the price of research is much lower and could be reused for the next parliamentary or presidential elections with a small modification.&#x0D; The research could be extended and adapted for other countries. Currently, the proposed algorithms and mathematical models showed good results in the French and Ukraine elections. It works well with English, French, Ukrainian and Russian languages. This allows us to claim that it will also work fine with other Latin or Cyrillic alphabets but for Asian or Arabic languages more research would be needed. Twitter is a good choice for European and American countries. In the future, other social networks should be considered for the countries in which it is not so popular.

Review of the Modern Maximum Power Tracking Algorithms for Permanent Magnet Synchronous Generator of Wind Power Conversion Systems

Wind energy conversion systems (WECSs) are considered green generators, environmentally friendly, and fully suitable energy sources to replace fossil energy sources. WECS’s output power is hugely dependent on the random nature of the wind. There are many solutions to improve the output power for WECSs, such as adjusting the profile of turbine blades, locating installation places, improving generators, etc. Nevertheless, maximum power point tracking (MPPT) algorithms for WECSs are optimal and the most effective because they are flexible in controlling different variable wind speeds and match all types of WECS. The parameters on the generator side control or the grid side control will be adjusted when MPPT algorithms are used, allowing the output power of WECSs to be maximized while maintaining stability in variable-speed wind. There are various MPPT algorithms, but the current problem is their efficiency and whether it requires deep knowledge to select the best MPPT solutions because each method has different advantages and disadvantages. This study has implemented an overview of modern maximum power tracking algorithms applied to permanent magnet synchronous generators in WECS with MPP methods based on speed convergence, efficiency, self-training, complexity, and measurement of wind parameters.

Modern and Lightweight Component-based Symmetric Cipher Algorithms

Information security, being one of the corner stones of network and communication technology, has been evolving tremendously to cope with the parallel evolution of network security threats. Hence, cipher algorithms in the core of the information security process have more crucial role to play here, with continuous need for new and unorthodox designs to meet the increasing complexity of the applications environment that keep offering challenges to the current existing cipher algorithms. The aim of this review is to present symmetric cipher main components, the modern and lightweight symmetric cipher algorithms design based on the components that utilized in cipher design, highlighting the effect of each component and the essential component among them, how the modern cipher has modified to lightweight cipher by reducing the number and size of these components, clarify how these components give the strength for symmetric cipher versus asymmetric of cipher. Moreover, a new classification of cryptography algorithms to four categories based on four factors is presented. Finally, some modern and lightweight symmetric cipher algorithms are selected, presented with a comparison between them according to their components by taking into considerations the components impact on security, performance, and resource requirements.

Algorithms for the prevention and treatment of supraventricular tachycardia in patients with implanted pacemakers: case series

Two clinical cases of prevention and relief of supraventricular tachycardia using modern algorithms in pacemakers of the latest generation in patients with bradyarrhythmia and paroxysmal atrial fibrillation (AF) are presented. In the first clinical case, the patient did not have an episode of AF for six months. In the second clinical case, the patient for 2 years did not have an episode of AF lasting more than 1 minute. Episodes sinus rhythm restoration by antitachypacing algorithms were recorded. These clinical cases demonstrate the effectiveness of modern algorithms in pacemakers of the latest generation for the AF prevention and sinus rhythm restoration.

Optimization of Interval Type-2 Fuzzy Logic System By using A New Hybrid Method of Whale Optimization algorithm and Extreme Learning Machine

The problem of searching for the best values ​​of the fuzzy logic parameters (T1FLS) is consider complex problems, and for type-2 fuzzy logic system (T2FLS) the problem is more complex, in special case interval type-2 fuzzy logic system (IT2FLS). The Researchers have used many methods and algorithms to solve this problem, and among the most important algorithms used in this field are the)Meta-heuristic) algorithms. Because Meta-heuristic algorithms have a high capacity in the practical field, so we used one of the modern algorithms in this field, which is the Whale Optimization algorithm (WOA). We are used the (WOA) algorithm together with the Extreme Learning Machine (ELM) algorithm as a hybrid algorithm to find the best parameters ​​for the IT2FLS. Whereas, the (WOA) algorithm was used to estimate the values of the antecedent for the system, and the (ELM) algorithm was used to find the values ​​of the consequent parts in the system. The simulation results show that the proposed algorithm is effective for a system (IT2FLS).

A Multipopulation Multiobjective Ant Colony System Considering Travel and Prevention Costs for Vehicle Routing in COVID-19-Like Epidemics

As transportation system plays a vastly important role in combatting newly-emerging and severe epidemics like the coronavirus disease 2019 (COVID-19), the vehicle routing problem (VRP) in epidemics has become an emerging topic that has attracted increasing attention worldwide. However, most existing VRP models are not suitable for epidemic situations, because they do not consider the prevention cost caused by issues such as viral tests and quarantine during the traveling. Therefore, this paper proposes a multi-objective VRP model for epidemic situations, named VRP4E, which considers not only the traditional travel cost but also the prevention cost of the VRP in epidemic situations. To efficiently solve the VRP4E, this paper further proposes a novel algorithm named multi-objective ant colony system algorithm for epidemic situations, termed MOACS4E, together with three novel designs. First, by extending the efficient “multiple populations for multiple objectives” framework, the MOACS4E adopts two ant colonies to optimize the travel and prevention costs respectively, so as to improve the search efficiency. Second, a pheromone fusion-based solution generation method is proposed to fuse the pheromones from different colonies to increase solution diversity effectively. Third, a solution quality improvement method is further proposed to improve the solutions for the prevention cost objective. The effectiveness of the MOACS4E is verified in experiments on 25 generated benchmarks by comparison with six state-of-the-art and modern algorithms. Moreover, the VRP4E in different epidemic situations and a real-world case in the Beijing-Tianjin-Hebei region, China, are further studied to provide helpful insights for combatting COVID-19-like epidemics.

Obstacle Avoidance and Path Planning of a Wheeled Mobile Robot Using Hybrid Algorithm

In the mobile robot workplace, the path planning problem is crucial. Robotic systems employ intelligence algorithms to plan the robot's path from one point to another. This paper proposes the fastest and optimal path planning of the wheeled mobile robot with collision avoidance to find the optimal route during wheeled mobile robot navigation from the start point to the target point. It is done using a modern meta-heuristic hybrid algorithm called IPSOGWO by combining Improved Particle Swarm Optimization (IPSO) with Grey Wolf Optimizer (GWO). The principal idea is based on boosting the ability to exploit in PSO with the exploration ability in GWO to the better-automated alignment between local and global search capabilities towards a targeted, optimized solution. The proposed hybrid algorithm tackles two objectives: the protection of the path and the length of the path. During, Simulation tests of the route planning by the hybrid algorithm are compared with individual results PSO, IPSO, and GWO concepts about the minimum length of the path, execution time, and the minimum number of iterations required to achieve the best route. This work's effective proposed navigation algorithm was evaluated in a MATLAB environment. The simulation results indicated that the developed algorithm reduced the average path length and the average computation time, less than PSO by (1%, 1.7%), less than GWO by (1%, 1.9%), and less than IPSO by (0.05%, 0.4%), respectively. Furthermore, the superiority of the proposed algorithm was proved through comparisons with other famous path planning algorithms with different static environments.

Self-Supervised Learning in Remote Sensing: A review

In deep learning research, self-supervised learning (SSL) has received great attention, triggering interest within both the computer vision and remote sensing communities. While there has been big success in computer vision, most of the potential of SSL in the domain of Earth observation remains locked. In this article, we provide an introduction to and a review of the concepts and latest developments in SSL for computer vision in the context of remote sensing. Further, we provide a preliminary benchmark of modern SSL algorithms on popular remote sensing datasets, verifying the potential of SSL in remote sensing and providing an extended study on data augmentations. Finally, we identify a list of promising directions of future research in SSL for Earth observation (SSL4EO) to pave the way for the fruitful interaction of both domains.

Local application of heparin ointment as a trigger factor for the heparin-induced thrombocytopenia

Heparin-induced thrombocytopenia (GIT) is a paradoxical reaction of platelets to heparin (their aggregation and destruction), potentially dangerous by the development of venous and arterial thrombosis, less often bleeding. There are serious problems of timely diagnosis of this complication. They are due to both insufficient awareness of specialists and the presence of a negative psychological attitude. The article presents a clinical case of treatment of a 57-year-old patient who was prescribed local application of heparin ointment for catheter thrombosis of the superficial veins of the left upper limb. After 8 days, the patient developed a clinical and instrumental picture of occlusive thrombosis of the right brachial vein. The patient was prescribed enoxaparin 0.6 x 2 times a day subcutaneously. After 4 days since the appointment of enoxaparin, a sharp deterioration in the patient’s condition: lower paraplegia with impaired pelvic organ function developed. There was a decrease in platelet levels by more than 2 times to 49x109/l. The probability of developing GIT on the «4T» scale is 8 points (high probability of GIT). Despite the treatment, the patient’s condition progressively worsened with the development of thrombosis of the infrarenal aorta, both common iliac arteries, massive trunk thromboembolism, segmental and subsegmental branches of the left pulmonary artery, intracerebral hemorrhage with a breakthrough into the ventricular system. Conclusion: heparin-induced thrombocytopenia, a rare but extremely dangerous complication observed in patients receiving heparin. The key to preventing the catastrophic consequences of GIT is awareness, alertness of clinicians and the use of modern algorithms for the diagnosis and treatment of GIT.

Comparative study of metaheuristic optimization algorithms for image steganography based on discrete Fourier transform domain

Image steganography and watermarking have demonstrated their efficiency in creating a covert communication channel and in digital image authentication respectively. The importance of cybersecurity tasks sets high efficiency requirements for information embedding methods. Many studies in this area focus on finding new ways to improve the embedding quality. One of them is to use metaheuristic optimization algorithms. The use of classical metaheuristics is common enough for a data hiding area. However, a lot of new metaheuristics have been proposed over the past few years. Their applicability has not yet been evaluated in the field of steganography and digital watermarking. In this paper, we present a comparative study on the efficiency of seven metaheuristic optimization algorithms for finding the best embedding options in the phase spectrum of the Discrete Fourier Transform (DFT). One of the contributions of our research is an improved DFT-based data hiding algorithm, which is a development of the error-free embedding algorithm we obtained earlier. The new algorithm also provides error-free extraction of embedded data, and it is distinguished by the high invisibility of embedding through the use of metaheuristic optimization. The use of metaheuristic optimization led to an increase in the PSNR value by an average of 2%–6% and an increase in the capacity value by an average of 16%–25%. Another important contribution of our research is the original formulation of the optimization problem for different classes of metaheuristics. The experimental results demonstrate the efficiency of metaheuristic optimization for solving data hiding problem. The Differential Evolution and the Particle Swarm Optimization showed the best values of embedding indicators among the classical metaheuristic optimization algorithms. The Gradient-Based Optimizer showed the highest efficiency among modern algorithms. Thus, our research indicates the relevance of further studies of other modern optimization algorithms for a data hiding area.

The stochastic pseudo-star degree centrality problem

We introduce the stochastic pseudo-star degree centrality problem, which focuses on a novel probabilistic group-based centrality metric. The goal is to identify a feasible induced pseudo-star, which is defined as a collection of nodes forming a star network with a certain probability, such that it maximizes the sum of the individual probabilities of unique assignments between the star and its open neighborhood. The feasibility is measured as the product of the existence probabilities of edges between the center node and leaf nodes and the product of one minus the existence probabilities of edges among the leaf nodes. First, the problem is shown to be NP-complete. We then propose a non-linear binary optimization model subsequently linearized via McCormick inequalities. We test both classical and modern Benders Decomposition algorithms together with both two- and three-phase decomposition frameworks. Logic-based-Benders cuts are examined as alternative feasibility cuts when needed. The performance of our implementations is tested on small-world (SW) graphs and a real-world protein-protein interaction network. The SW networks resemble large-scale protein-protein interaction networks for which the deterministic star degree centrality has been shown to be an efficient centrality metric to detect essential proteins. Our computational results indicate that Benders implementations outperforms solving the model directly via a commercial solver in terms of both the solution time and the solution quality in every test instance. More importantly, we show that this new centrality metric plays an important role in the identification of essential proteins in real-world networks.