KPI Science News

ON POSSIBILITY OF INVOLVING PHOTOCATALYTIC METHODS IN POST-WAR REMEDIATION OF TERRITORIES OF UKRAINE

2024-11-10T20:23:59+02:00

Background. Photocatalytic methods have prospects for use in cleaning and decontamination of organic compounds polluting the territory of Ukraine as a result of military operations – explosives, chemical weapons, fuel and lubricants, pathogens in water and soil; for the degradation of microplastics, etc.

The purpose of the work was to systematize the available data on the possibility of using photocatalytic methods for post-war remediation and restoration of the territories of Ukraine.

Objective. This work is dedicated to systematic analysis of the available data on the possibility of photocatalytic methods application in post-war remediation and restoration of the Ukrainian territories.

Methods. The state-of-the-art in post-war remediation was investigated together with our own experience to evaluate prospectives of advanced oxidation methods, such as photocatalytic ones, in the restoration of ecosystems after war.

Results. The ability of photocatalysts based on to complete mineralization of trinitrotoluene, nerve agents and their model compounds, polyaromatic hydrocarbons, microplastics, and disinfection of pathogens is shown. The perspective of the technology of soil washing with solutions of surfactants and complexones (chelating agents) with subsequent sequential photocatalytic mineralization of organic pollutants and adsorption removal of heavy metals is highlighted. Other data related to the issue of restoration of territories after military operations were discussed. Another noteworthy method – the electro-Fenton process —has been singled out.

Conclusions. Considerable experimental material has been accumulated on the use of photocatalysis and other advanced oxidation processes in laboratory conditions, but practical application is progressing rather slowly. Need to solve the problem of economy and energy consumption to create radiation. Nevertheless, such photocatalysts as titanium dioxide and composites based on it have proven their effectiveness in the process of neutralizing explosives and poisonous substances of chemical weapons. Among the conditions of successful application is a relatively low degree of pollution (<2 g/l) of wastewater, therefore the use of photocatalytic methods for the remediation of territories is appropriate at the stages of post-treatment. It is necessary to optimize the substrate for photocatalysts. This will help solve the problems of stability and scalability of the technology.

SHAPE OPTIMIZATION PRINCIPLES FOR LONG-RANGE UNMANNED VEHICLES

2024-10-29T18:14:51+02:00

Background. Optimization of unmanned vehicles needs improvement of their shapes in order to reduce aero- or hydrodynamic drag. In particular, long-range vehicles must have high economical efficiency, i.e. large values of the drag-to-weight ratio. This journal had published corresponding ideas and formulae several years before. Nevertheless, high interest in unmanned vehicles needs a brief presentation of basic principles and illustrative examples, which can be available for wide groups of engineers and managers.

Objective. The purpose of the paper is to systematize the author’s contribution to the methods of the commercial efficiency improvement, unseparated bodies of revolution and wings of low drag and to discuss areas of applications for the long-rage terrestrial, airborne and water vehicles.

Methods. Analytic formulae for the commercial efficiency (obtained before and taking into account the aerodynamic drag on hulls and the drag connected with the support of the vehicle weight) are used for the laminar and turbulent flow patterns.

Results. A simple relationship between the speed, volume and the hull length of the optimal vehicle providing the laminar flow and maximum rage was obtained. Froude number ranges for the effective dynamic weight support were determined. Characteristics of optimal wings for corresponding airplanes and gliders were calculated. An example of the optimal underwater vehicle and a perspective boat hull shape are presented.

Conclusions. The hull shapes must be similar to the unseparated bodies of revolution, which were proposed before or can be calculated according to particular needs. The proposed relationship between volume and length of a hull, speed of steady motion and kinematic viscosity of air or water has to be used. The elongation and the angle of attack of optimal wings have to be as large as possible. High values of the commercial efficiency open prospects for achievement of long ranges even with the use of electrical engines, 410–850 km for airplanes, 420–750 km fog gliding bombs released at the attitude of 10 km, 800–1200 km for underwater and floating boats, and practically unlimited flight time for airships using solar energy.

THE METHOD OF INFORMATIONAL INFLUENCE ON POLITICAL PREFERENCES THROUGH THE CREATION OF MEDIA COALITIONS BASED ON C-CORE

2024-10-03T21:47:41+03:00

Background. One of the important ways to achieve certain goals in the modern world is the wide use of cyberspace and information influence. As a result of the high rate of development of digital communications, they have a serious impact on numerous spheres of human life, in particular, economic and political processes. Recently, information influence, for example, in such forms as information injection, sharpening of information drives, etc., is gaining critical importance on political processes inside any country, which can pose problems for the security of the state.

Objective. The purpose of the work is to develop a way to create a coalition among information sources to ensure influence on certain groups of society in order to achieve political goals.

Methods. To achieve the goal, it is proposed to use a simple game as a prerequisite for creating a coalition; profit sharing based on the C-core concept; of the Nash arbitrage decision for solving the multi-criteria optimization problem.

Results. A model of influence on political preferences has been developed, in which participants divided into two groups have gains only if they join a coalition, i.e., the cooperative game is a simple game. The first group includes media sources whose activities influence a small number of people. The second group contains a media resource of a different nature. With the help of a game approach, a multi-criteria problem was formalized with the selection of the maximum results of each of the groups and its solution was found.

Conclusions. A model has been developed for creating informational influence on political processes due to the integration of certain information sources, which makes it possible to solve the problem of optimal distribution of information resources of media sources with a small level of influence on society, based on the Nash arbitration decision.

METOD FOR INCREASING THE SPECTRAL EFFICIENCY OF TROPOSCATTER COMMUNICATION BASED ON THE USE OF COMPOSITE SIGNALS IN THE WALSH BASIS

2024-12-10T22:48:00+02:00

Background. In modern telecommunications, troposcatter communication systems organize long-distance communication. These systems allow communication beyond the line of sight. An important factor that must be considered in these systems is multipath, due to the physical principle underlying the functioning of troposcatter systems. Diversity reception and broadband signals are used to overcome this factor's negative impact. However, broadband signals use an excessive frequency band and are characterized by a low spectral efficiency.

Objective. Development of a method for overcoming the negative impact of multipath by using composite signals on the Walsh basis.

Methods. Parallel composite signals provide simultaneous packet transmission of a group of signals built based on a complete system of mutually orthogonal Walsh-Hadamard functions. Each signal transmits one bit of information, but the parallel transmission of a packet of mutually orthogonal signals avoids decreasing the system's information speed. To counteract the effect of multipath, pilot signals are added to the composite signal, as individual Walsh-Hadamard functions with better auto- and cross-correlation properties are selected. The advantages of composite signals in the Walsh-Hadamard basis include their spectral efficiency, which significantly exceeds the spectral efficiency of broadband signals. The paper describes a method for forming a composite signal on a Walsh-Hadamard basis with pilot signals and a functional diagram of a receiving device that provides optimal pre-detector addition of signals from four independent diversity reception channels.

Results. The method of forming the composite signal with pilot signals based on the Walsh-Hadamard basis is presented as a functional diagram of a reception device that will ensure optimal detection of composite signals from several independent channels of a separated receiver. The spectral efficiency of the composite signals is shown on the size of the Walsh-Hadamard vicor basis. The introduction of the pilot signals makes it possible to ensure the synchronous composition of signals received from several independent receiving channels.

Conclusions. The proposed technical solutions using composite signals in the Walsh-Hadamar basis make it possible to create troposcatter communication systems that provide operation in multi-path conditions, and the spectral efficiency of which significantly exceeds this indicator for systems using M-sequence signals, which allows to increase in the information transmission rate at the same frequency bandwidth or to increase their noise immunity by using additional noise-resistant coding at a fixed information transmission rate.

ANALYSIS OF CERTIFICATION OF ELECTRONIC TRUST SERVICES IN UKRAINE

2024-11-13T12:15:32+02:00

Background. Certification of electronic trust services in Ukraine faces several problems. These challenges include the need to align with international standards, ensure cryptographic infrastructure is resilient against evolving cyber threats, and align practices with the European Union's eIDAS regulation. In addition, issues related to interoperability, legal frameworks and the overall security of electronic trust services pose significant obstacles.

Objective. Contributing to the strengthening of the national cybersecurity system of Ukraine by solving problems related to the certification of electronic trust services, with an emphasis on cryptographic components and the certification process.

Methods. It involves a multifaceted approach. Study of existing conformity assessment systems, global experience and regulatory mechanisms in leading countries with an emphasis on the EU. Assessment of strengths and weaknesses of current electronic trust services in Ukraine. Comparison of the regulatory framework and technical mechanisms operating for electronic trust services in the EU and Ukraine. Offering practical recommendations for accreditation and conformity assessment systems in Ukraine to bring them into line with the EU eIDAS regulation.

Results. The study provides valuable information about the challenges faced by Ukrainian electronic trust services, especially regarding the assessment of the existing certification system. The benchmarking identifies gaps and opportunities for improvement, focusing on aligning practices with the EU eIDAS regulation. Empirical data provide a detailed understanding of the perspectives and concerns of key stakeholders. The study also identifies specific areas for improvement in the legal and technical aspects of electronic trust services.

Conclusions. There is an urgent need for systematic improvement of the certification of electronic trust services in Ukraine, in particular in cryptographic key certification centers. Addressing these challenges requires a coordinated effort to align practices with international standards, strengthen cybersecurity measures, and promote interoperability. And also emphasizes the importance of constant monitoring, adaptation to new threats and cooperation with international partners to ensure the efficiency and security of electronic trust services in Ukraine.

HYBRID HYDROGELS FOR DIAGNOSTICS AND TREATMENT OF CANCER

2024-10-31T20:42:24+02:00

Background. Application of polymer composites and hydrogels filled with functionalized nanoparticles (in particular, acid-modified laponite and gold) allows their use for filling postoperative cavities, especially after tumor resection, and enhances the effectiveness of early diagnosis of cancer.

Objective. Development of methods for the synthesis of acid-activated laponite, study of its basic physico-chemical properties, methods of its incorporation into thermosensitive hydrogels; studying the possibility of using hybrid materials based on thermosensitive hydrogels and gold nanoparticles in oncological surgery.

Methods. The synthesized materials were characterized by the methods of laser correlation spectroscopy, IR and UV spectroscopy, X-ray diffraction analysis, zymography, etc.

Results. Synthesized hybrid polymer systems based on thermosensitive hydrogels with gold nanoparticles can be used to fill postoperative cavities with simultaneous targeted release of the incorporated Albucid as a bacteriostatic. The sodium dodecyl sulfate-polyacrylamide gel system with incorporated nanoparticles of acid-modified laponite demonstrated high efficiency in the electrophoretic separation of peripheral blood plasma proteins for the early diagnosis of colorectal cancer.

Conclusions. Acid modification of laponite can serve as a basis for the development of nanoscale therapeutic and diagnostic systems. Synthesized hybrid polymeric materials based on thermosensitive hydrogels and gold nanoparticles can be used to fill postoperative cavities with simultaneous targeted release of the incorporated bacteriostatic drug Albucid.

INFORMATION SYSTEM FOR FORECASTING NONLINEAR NON-STATIONARY PROCESSES IN FINANCE

2024-10-03T22:36:00+03:00

Background. Financial processes are often characterised by nonlinearity and non-stationarity, which makes them difficult to accurately model and forecast. Traditional methods cannot effectively take into account the complex interrelationships and variability of such processes, which generates increased uncertainty and risks. This leads to the need to develop new information systems and methods to improve the accuracy and sustainability of forecasts.

Objective. To provide a brief overview of the characteristics of nonlinear non-stationary processes, to develop a methodology for their modelling, as well as to build mathematical models based on actual statistical data and to obtain practically useful results of modelling and forecasting selected processes.

Methods. The methodology for modelling and forecasting nonlinear non-stationary processes is applied, models are built using data mining, such as regression models and a neural network, and the main metrics for assessing the adequacy of the model and quality of the forecast are used.

Results. The developed information system for modelling and forecasting nonlinear non-stationary processes is approbated on real statistical data. Based on data mining methods, models of the share price dynamics of a well-known company were built. The study's results demonstrate that using an integrated approach, which includes regression models and neural networks, significantly improves the quality of forecasting variance changing in time and the nonlinear non-stationary process.

Conclusions. The task of high-quality forecasting of processes due to rapid, sometimes hard-to-predict changes in the external environment, i.e. external shocks, which is typical for nonlinear non-stationary financial processes, is still relevant today. The literature provides a sufficient variety of methods for modelling these processes. However, in this research, the methods that have demonstrated their advantages in modelling financial transactions in the stock market were chosen, and therefore it makes sense to expand and improve the perspectives of this approach.

TRANSFORMER VS. MAMBA AS SKIN CANCER CLASSIFIER: PRELIMINARY RESULTS

2024-03-30T16:26:05+02:00

Background: Skin cancer is a deadly disease that takes dozens of thousands of lives yearly. The key element of successful treatment of it is early detection. However, invasive detection methods are not always feasible. Meanwhile, Transformers, the most renowned and researched models keep being computationally heavy. In this paper we investigate Mamba model for such classification problem compared to Transformers.
Objective: This paper compares the effectiveness of two machine learning architectures, Vision Transformer (ViT) and Mamba, for skin cancer classification using dermoscopy images. The goal is to determine if Mamba can provide a computationally efficient alternative to ViT without decrease in diagnostics accuracy.
Methods: We used the HAM10000 dataset, a well-known benchmark in skin cancer classification, with 10015 dermoscopic images. We preprocessed the data to address issues like class imbalance and normalized the images. Both ViT and Mamba models were pretrained on the ImageNet dataset and fine-tuned for skin cancer classification. We evaluated the models based on overall accuracy and F1 scores for specific classes of skin cancer.
Results: The results show that both ViT and Mamba models have similar overall accuracy, with Mamba models performing slightly better in classifying less represented classes like Bowen's Disease and Dermatofibroma. Both models demonstrated high F1 scores for Melanoma, indicating their effectiveness in identifying this severe form of skin cancer.
Conclusions: Our findings suggest that Mamba is a viable alternative to ViT for skin cancer classification, offering similar accuracy while potentially reducing computational costs. This could make non-invasive skin cancer diagnostics more accessible and affordable. Further research is needed to explore other variations of the Mamba model and to fine-tune its performance on larger datasets.

System Approach to the Combined Use of Large Language Models and Classical Models in Foresight Tasks

2024-11-13T13:20:21+02:00

Background. Large Language Models (LLMs) and their associated agents have spread wide technology and represent a significant advancement in recent times. These state-of-the-art models expose valuable potential, but they are not devoid of restrictions, inefficiencies, and limits. This article investigates the exploration of these constraints within specific domain areas and prediction problems as examples.

Objective. The article highlights features offered by GPT-based models and compares the conclusions with classical methods of textual data analysis in classification tasks using the prediction methodology as an example. The purpose of the study is to develop a system approach to the combined use of traditional machine learning approaches as a practical alternative to LLMs in foresight tasks using the example of STEEP analysis, which provides an opportunity to obtain valuable information from textual data.

Methods. The study is structured into four segments, each addressing distinct parts: Data Mining, text pre-processing using LLMs, text pre-processing utilizing Natural Language Processing (NLP) methods, and comparative analysis of results. Data Mining includes data collection and data pre-processing stages for train and test observations. For the utilization of LLMs, chains of thought techniques and prompt engineering were used.

Results. Throughout this study, it was acknowledged that the LLMs can be used in combination with classical machine learning methodologies for domain-specific areas in STEEP analysis under Foresight tasks. The outcome revealed a model that was developed significantly faster and with less complexity compared to LLMs such as GPT and Mistral. Increasing the number of models employed leads to more stable results.

Conclusions. The main result of the proceeding is that the patterns that reveal LLMs under certain settings can also be identified by classical models. Moreover, augmenting the deployment of LLMs during the data preparation stages contributes to heightened stability in outcomes. Using classical models combined with LLMs speeds up response times during inference and reduces operating costs for running models.