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This paper provides an in-depth evaluation of the effectiveness of international climate agreements in addressing the issue of global warming. As concern about climate change increase, global collaboration has become crucial in designing and implementing strategies to reduce emissions of greenhouse gases. The study reviews major international agreements, including the Kyoto Protocol, the Paris Agreement, and the subsequent Kyoto Climate Agreement, exploring their internal mechanisms, goals, and results. Drawing on empirical data, it assesses how well these agreements have performed in real life as regards limiting the global warming. The paper also discusses the challenges and obstacles in the process of implementing the agreements, such as compliance difficulties, political hurdles, and the involvement of various stakeholders. By synthesizing current research and policy insights, the paper aims to enhance public understanding of the effectiveness of these agreements and provides recommendations for improving their impact in tackling the urgent threat of climate change.

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The agroecological movement emerges as an alternative to modern agriculture and the conventionalization of organic farming. It advocates for the development of agri-food systems grounded in the principles of biodiversity, the strengthening of family farming, food sovereignty, and the reconnection between rural and urban areas. Understanding agroecological experiences worldwide is essential for evaluating their progress in building sustainable agroecosystems. This article aims to comparatively analyze the development of agroecology in Brazil and Italy, highlighting similarities and differences. In Italy, 19.68% of agricultural land is dedicated to organic production, compared to only 0.4% in Brazil—a difference of 49 times. Furthermore, 8.31% of Italian farms are organic, while in Brazil, the figure is only 1.28%. Both countries have followed different paths: in Brazil, the agroecological movement became consolidated in the 1990s, driven by the involvement of NGOs and family farmers. In contrast, in Italy, the movement gained momentum in 2015, with greater engagement from the scientific community. In Italy, agroecology developed within a context dominated by organic farming, whereas in Brazil, the agroecological movement preceded the institutionalization of organic agriculture.

This paper shows how human activity through genetic engineering causes pain to non-human animals. It primarily deals with an evaluation of the phenomenon of animal rights (specifically animal conservation) from the view of Singer’s notion of speciesism. Singer’s notion of speciesism frowns at all forms of animal maltreatment and emphasizes that animals are sentients, and they are capable of suffering and feeling pain. The central problem here is whether animal’s life has intrinsic value and whether they can be used for experiment. Among the several ways by which speciesism is practiced, this work identified genetic engineering as a major obstacle to animal conservation because humans will invade the animal space to look for various animal species to carry out experiment on. Using the method of critical analysis, evaluation and prescription, the paper argues that animals have life and belong to the environment and as such, the environment should be conserved also for their sake and if humans continue to exploit the environment by using animals for genetic manipulations, there will be negative consequences to humans and extinction of some species.

Dom stanowi miejsce, w którym młodzi ludzie dorastają, uczą się i rozwijają kompetencje niezbędne do sprawnego funkcjonowania w społeczeństwie. To miejsce, w którym kształtowane są postawy, opinie i zachowania najmłodszych członków rodziny, dotyczące również środowiska naturalnego. Celem artykułu jest przedstawienie miejsca domu rodzinnego - rodziny w kontekście kształtowania świadomości ekologicznej Polaków. Prezentowany w artykule temat oparty został o literaturę przedmiotu, a także wyniki badań dotyczące problematyki odnoszącej się do świadomości ekologicznej Polaków (m.in. CBOS, EcoBarometr, Raport z badania świadomości i zachowań ekologicznych mieszkańców Polski 2022). Autorzy wykorzystali analizę danych zastanych w postaci desk research. Badania (EcoBarometr2023; EcoBarometr 2024) pokazują, iż głównym źródłem informacji dotyczącej środowiska naturalnego są media, nie zaś rodzina. W przypadku młodych ludzi głównym źródłem staje się Internet, a wraz z wiekiem wzrasta w tym zakresie znaczenie telewizji. Wyniki prezentowanych w artykule badań wskazują również na to, że zdecydowana większość Polaków podejmuje działania służące środowisku w obszarze domu rodzinnego (np. segregowanie śmieci). Jednakże motywacje do podejmowania tego typu działań nie są wynikiem jedynie dbałości o środowisko, lecz w równym stopniu wynikają z przyczyn natury ekonomicznej. Rodzina nie stanowi środowiska, w którym rozmowa staje się podstawowym narzędziem kształtowania świadomości ekologicznej Polaków. Ani dom, ani szkoła w opinii badanych nie stanowią głównych źródeł informacji dotyczących środowiska naturalnego. Istotnym staje się zatem zwrócenie uwagi na znaczenie jakie środowisko rodzinne powinno odgrywać w sferze kształtowania postaw i zachowań ekologicznych młodych ludzi.

This work focuses primarily on animal rights in the United States within the context of the specific legal system existing in that country. It argues that animal rights as recognized by state law are limited, in the sense that enforcement can occur only through public prosecution. If horses and cows are beaten and mistreated at a local farm, or if greyhounds are forced to live in small cages, protection will come only if the prosecutor decides to provide it. However, as prosecutors have limited budgets, and animal protection is rarely a priority the result is that violations of state law occur every day. We contend that there is no good reason to accept the level of suffering that is now being experienced by millions of living creatures. Doubts have been raised about the radical idea that animals deserve to have “autonomy”, understood as a right to be free from human control and use. In our view, the primary focus should instead be on animal welfare and suffering. While animals should not be treated merely as tools for human purposes, it is possible for humans to control them in ways that still allow animals to have decent lives. However, emphasis on minimizing suffering and ensuring decent lives for animals has significant implications. It is appropriate to consider human interests in this equation, because most times human interests take precedence over those of animals. Adopting the conversational method of inquiry, we claim that often the interests of animals are entirely disregarded, and if they were considered, many of our current practices would be indefensible.

The article considers an approach to implementing the architecture of a microservice system for processing large volumes of data based on the event-oriented approach to managing the sequence of using individual microservices. This becomes especially important when processing large volumes of data from information sources with different performance levels when the task is to minimize the total time for processing data streams. In this case, as a rule, the task is to minimize the number of requests for information sources to obtain a sufficient amount of data relevant to the request. The efficiency of the entire software system as a whole depends on how the microservices that provide extraction and primary processing of the received data are managed. To obtain the required amount of relevant data from diverse information sources, the software system must adapt to the request during its operation so that the maximum number of requests are directed to sources that have the maximum probability of finding the data necessary for the request in them. An approach is proposed that allows adaptively managing the choice of microservices during data collection and by emerging events and, thus, forming a choice of information sources based on an assessment of the efficiency of obtaining relevant information from these sources. Events are generated as a result of data extraction and primary processing from certain sources in terms of assessing the availability of data relevant to the request in each of the sources considered within the framework of the selected search scenario. Event-oriented microservice architecture adapts the system operation to the current loads on individual microservices and the overall performance by analyse the relevant events. The use of an adaptive event-oriented microservice architecture can be especially effective in the development of various information and analytical systems constructed by real-time data collection and design scenarios of analytical activity. The article considers the features of synchronous and asynchronous options in the implementation of event-oriented architecture, which can be used in various software systems depending on their purpose. An analysis of the features of synchronous and asynchronous options in the implementation of event-oriented architecture, their quantitative parameters, and features of their use depending on the type of tasks is carried out.

The minimally invasive spine surgery, sometimes referred to as MISS, has changed spinal therapy by minimizing the length of time required for recovery, as well as the amount of worry and suffering that patients experience. Before we can consider the surgery to have been successful, there is a critical problem that has to be addressed. The use of augmented reality technology has been gaining traction over the course of the last few years as a method of improving the accuracy of MISS management. This research has a significant focus on the applications of augmented reality in minimally invasive spine surgery as its core investigation. The use of augmented reality (AR) technology, which supports medical professionals in performing difficult spine procedures, allows for the provision of real-time placement suggestions as well as information that is specific to the patient. This has a number of major benefits, some of which include improved vision, more accurate tool placement, and less problems. In order to include augmented reality into MISS, it was necessary to have a user interface that was easy to use, a data integration system that was comprehensive, and recording mechanisms that were reliable. It is necessary to make the necessary modifications to the registration process, delays, and physical issues before bringing it into clinical practice. This procedure must be completed before it can be implemented. In the context of this research project, an application for smartphones that is integrated with augmented reality is currently being created with the purpose of boosting minimally invasive spine surgery. "The innovation of this research is the creation of a mobile AR interface that bridges the gap between accessibility and high-quality surgical visualization tools, offering an alternative to traditional AR systems." This AR smartphone application is the first of its type to combine cost, accessibility, and sophisticated visualization features, resulting in a whole new approach to surgical help that is unlike any other surgical procedure.Using Unity3D, the Vuforia AR camera, and C#, the software is able to create an augmented reality (AR) experience for mobile devices. This objective is realized via the utilization of these three components. Technology that are regarded to be industry standards include HoloLens and head-mounted displays (HMDs), which are examples of augmented reality technology. On the other hand, the vast majority of people are unable to make use of them because of the tremendous cost that they carry. When it comes to visualizing three-dimensional MRI spine pictures, this technology offers an approach that is more efficient and economical. Taking into consideration the results of this study, it would seem that surveys and formal evaluations that make use of MISS and augmented reality might possibly be beneficial. By using augmented reality (AR), medical practitioners may be able to more effectively see important structures, plan surgical operations, and identify the required equipment, which may eventually result in improved patient outcomes. Increasing the capabilities of augmented reality technology, finding new uses for it, and incorporating artificial intelligence-driven decision improvement are the goals of the researchers.

Emotions are fundamental to human interactions, intricately influencing communication, behavior, and perception. Emotion-Cause Pair Extraction (ECPE) is a critical task in natural language processing that identifies clause pairs associating emotions with their corresponding triggers within textual documents. Unlike traditional Emotion Cause Extraction (ECE), which relies on pre-annotated emotion clauses, our study introduces a novel end-to-end model for ECPE. This innovative approach utilizes the extensive NTCIR-13 English Corpus to establish a robust baseline for ECPE in English, showcasing significant performance improvements over conventional multi-stage methods. Central to our model is the incorporation of Bidirectional Long Short-Term Memory (BiLSTM) networks, enhancing the ability to capture both local and global dependencies in textual sequences. By effectively combining contextual and positional embeddings, our model accurately predicts emotion-cause relationships, paving the way for a deeper understanding of emotional dynamics in conversational contexts and facilitating causal inference. Furthermore, our research highlights superior performance metrics, aligning its efficacy with state-of-the-art techniques in the field. This study advances emotion recognition in natural language processing, providing valuable insights for nuanced analyses of human emotions within textual data. Additionally, our findings enhance understanding of emotional intelligence in user interaction modeling and conversational AI applications. Through the public availability of our dataset and model, we aim to foster collaboration and further research in this vital area, ultimately improving the capacity for emotional understanding in applications ranging from sentiment analysis to interactive learning.

This study investigates the integration of Multilayer Perceptron (MLP) architecture in Network Intrusion Detection Systems (NIDS) to strengthen cyber defences against evolving threats. The goal is to explore the potential of MLP in learning complex patterns and adapting to dynamic attack vectors, thereby improving detection accuracy. Key results from 5-fold cross-validation demonstrate model consistency, achieving an average accuracy of 0.97 with minimal standard deviation. Further evaluation across multiple nodes per layer and train-test splits demonstrate model robustness, displaying high metrics such as AUC-ROC and F1-Score. Challenges, such as the scarcity of large labelled datasets and complex model interpretability, are acknowledged. This study provides a comprehensive foundation for future investigations, suggesting potential directions such as integrating advanced neural network architectures and assessing model transferability. In conclusion, this study contributes to the evolving intersection of machine learning and cyber security, offering insights into the strengths, limitations, and future directions of MLP-based NIDS. As cyber threats evolve, continued refinement of MLP methods is critical to effective network defences against sophisticated adversaries.

The article presents a method of implementing a functionally integrated device for temperature measurement, which allows for controlled heating of the primary temperature transducer, measurement of the heating temperature as well as the temperature and differential temperature of the investigated and reference samples. The heating speed is regulated by the selection of the frequency and duration of the control impulses. To measure the temperature and temperature difference, it is proposed to use measuring currents of different polarity, which make it possible to simplify the device design. The methods of linearisation of the conversion function of primary temperature transducer based on the formation of compensating currents in given measurement ranges have been investigated. The conducted studies showed that the temperature measurement error does not exceed 0.11°C and 0.005°C in the control heating mode and in the temperature measurement mode, respectively. The temperature measurement error of the investigated and reference samples and the differential temperature measurement error does not exceed ±0.003°C and 0.001°C, respectively.

A study was conducted on the impact of the most significant threats on the level of network security based on the examination of impulse processes on a fuzzy cognitive map. A topological analysis of the structure of the cognitive map was performed, simplicial complexes were constructed, and their structural vectors were determined. Based on the obtained data, a set of control and target concepts of the fuzzy cognitive map was formed, and the relationships between these concepts within the simplicial complexes were established. Taking this information into account, a study was conducted on the change in the level of computer network security using the propagation of impulses introduced into the control concepts of the fuzzy cognitive map. The results obtained enable an increase in the level of network security by considering the impact of the most significant threats through timely managerial decisions and the implementation of necessary software and technical measures.

Flexible antennas have revolutionized the wireless communication as integral components of modern smart devices. Their unique properties are design flexibility, enhanced performance, and seamless implementation in smart devices. However, when designing antennas, multiple conflicting objectives often need to be considered simultaneously. Incorporating artificial neural networks into optimization strategies has shown promising results in antenna design problems. Neural networks can adapt to different and changeable requirements and constraints. That is why they are valuable tools for customizing antennas to specific operating conditions. The utilization of artificial neural networks for the design of flexible antennas enables researchers to expand the design space, optimize antenna characteristics with greater efficiency, and identify innovative solutions that may not be apparent through traditional design methods. In this study, the authors propose to determine required parameters and characteristics of flexible antennas by using Artificial Intelligence techniques, namely fuzzy logic, neural networks, and genetic algorithms. A matching technique based on neural network for designing flexible antennas has been elaborated. A neural network was developed. To train the neural network, several samples of flexible antenna were manufactured and tested. The developed neural network was simulated. Finally, the obtained flexible antenna was tested.

High critical temperature superconductor power devices have unique characteristics that reduce energy losses during transmission and distribution of electricity. These superconductors allow for high current density, enabling the construction of smaller, lighter, and more transportable installations. A superconducting transformer can significantly reduce the dynamic effects of the short-circuit current during the initial milliseconds of a fault. High temperature superconductor windings, on the other hand, are relatively lightweight and have a low heat capacity. Despite being cryogenically cooled and managing already limited current, the windings made of second-generation high-temperature superconductor can be vulnerable to thermal damage, which may occur in a relatively short timeframe. A numerical study of a short circuit in a three-phase power system with a 10 MVA 115/16.5 kV superconducting transformer is presented in this paper. The superconducting tape's nonlinear electrical and thermal characteristics, as well as heat buildup in the transformer windings during a short circuit, are all taken into consideration by the transformer model.

In machine learning, learning algorithms that learn from other algorithms are called meta-learning. New algorithms called Ensemble algorithms have surfaced as a viable method to improve defect prediction models' accuracy and dependability. In software development defect prediction of software engineering is still a big challenge, and leads to the failure of systems, increases the cost of maintenance, and makes the development process more difficult. Consequently, defect prediction systems have become more popular as a way to foresee possible flaws early on in the development process. Defect prediction is the process that specifies the possible defects in the code written newly or the existing modified code without the use of code testing. This paper introduces ensemble learning ideas, reviews the traditional defect prediction models, and investigates ensemble learning techniques for defect classification and prediction such as bagging, boosting, stacking, and random forests, Case studies and actual experiments illustrate the important role of ensemble algorithms in classifying five severity types of defects and predicting the severity code of defects to improve the software development process by reducing the time and effort needed to determine the type of defect.

The inverse problem concerning the identification of rigid surfaces of scattering objects formulated in the frequency domain is presented in this paper. Differences in the identification of concave objects, such as kite-shaped, and convex objects (circle) are indicated. The reader’s attention is focused on the conventional boundary element method with small number of boundary elements and the small number of sensors, which is significant for inverse problems.

The purpose of this article is to study the impact of different ways of presenting educational content found on websites on the ability to assimilate information among elementary school students. To conduct the study, three websites with interface layouts different in terms of levels of complexity were prepared: a simple, a moderate and a complex one. Each site contained the same educational materials. Seventy-two students from elementary school grades IV-VI participated in the study. All the students were divided into 3 groups, each of which worked with one version of the website. At the first stage, students were tasked with familiarizing themselves with the content of the web pages within a given website. This was followed by a knowledge quiz, checking how much information the subjects had memorized. At the last stage, participants of the experiment completed a questionnaire collecting their opinions on their preferences regarding the form of presentation of educational content made available online. The obtained results and the conducted analysis show the interface with the moderate level of complexity, i.e. with a relatively simple layout and without an excessive number of elements, allows for the assimilation of the most information. The interface was also rated the highest by the participants of the study.

In this article, a polarization selector, which based on waveguides partially filled by dielectric has been developed and investigated. An analysis of the designs of polarization selectors on empty waveguides was carried out. The developed design of the polarization selector is based on square waveguides partially filled by dielectric in the form of an E-tee. The main and side waveguides of the E-tee include metal gratings with mutually perpendicular conductors. The dielectric plates have a square cross-section and extend between the second and third conductors of metal grids, which located in the main and side waveguides. The article presents the normalized conductivity for such an E-tee, through which the standing wave coefficient is determined. The normalized conductivity of the E-tee consists of the reactive conductivity of the joint of the side square waveguide partially filled by dielectric, the reactive conductivity of the joint of the main square waveguide partially filled by dielectric, reactive conductivity of grids in side and main waveguide partially filled by dielectric. These conductivities are determined through the corresponding transformation coefficients, which are combinations of selected coordinate functions and the electrical transverse and magnetic transverse eigenvector functions of a waveguide partially filled by dielectric. The transverse electric and transverse magnetic eigenvector functions of a waveguide partially filled by dielectric are expressed through the Mathieu functions and their derivatives. Numerical results were obtained for the reactive conductivities of gratings from the ratio of the thickness of the grating rod to the size of the wall of a square waveguide for different normalized wavelengths. The results were obtained both for a grid with equal distances between the rods and for unequal spaced rods.

The study aims to optimize wind farm efficiency in low wind speed regions using the HOMER Pro tool to examine the impact of wind turbine ratings on overall efficiency of wind farms. boosting wind farm efficiency is essential for improving economic viability and grid integration. We propose the establishment of three wind farms, each possessing equal capacities but different in individual turbine capacities 1.5 kW, 3.4 kW and 5.1 kW, then optimize their performance in simulation environment. Through employing HOMER Pro optimization algorithm, we assess all wind farms over the period of one year, taking into account wind speed, temperature and geospatial coordinates. Although all wind farms have equal total capacities, simulation results revealed disparities in their generation abilities, reaching up to 22%, favouring the farm with smaller turbines. Furthermore, the results demonstrated that as wind speed decreases, the disparity in power generation between wind farms increases, reaching 51.9% in November, the month with the lowest wind speeds. These findings provide a comprehensive understanding of wind farm behaviour, particularly regarding turbine sizes, and contribute to the research community's efforts to enhance wind farm power production in low wind speed regions. They also help find solutions to enable the embrace of wind energy and decrease fossil fuel consumption in such regions, fulfilling their international sustainability commitments.

Engaging in investment activities plays a crucial and strategic role in fostering the growth of businesses and ensuring their resilience in the market. This involvement entails expenditures on acquiring assets, embracing technological advancements, expanding production capacities, conducting research and development, among various other domains. Collectively, these aspects form the foundation for the sustained success of an organization over the long term. This thesis will delve into an exploration of leveraging machine learning techniques to forecast key parameters in business, including investments and their impact on the financial health of the company. In this research, explored a variety of time series models and identified that both the Random Forest Regressor and Decision Tree Regressor models deliver superior accuracy, showcasing identical RMSE values of 88.36 on the validation dataset. Furthermore, the Cat Boost and Light GBM models exhibited praiseworthy performance, registering RMSE values of 92.47 and 104.69, respectively. These findings highlight the robust performance of Random Forest Regressor and Decision Tree Regressor, emphasizing their capability to provide accurate predictions. It is noted that Random Forest Regressor and Decision Tree Regressor are distinguished by high accuracy in time series forecasting, and the choice between them should take into account the trade-offs between computational efficiency and interpretability of the model. These results allow us to propose practical strategies for managing investment resources to ensure the sustainable development and prosperity of the enterprise in the long term.

The growing complexity and sophistication of cyberattacks on organisational information resources and the variety of malware processes in unprotected networks necessitate the development of advanced methods for detecting malicious processes in network traffic. Systems for detecting malicious processes based on machine learning and rule-based methods have their advantages and disadvantages. We have investigated the possibility of using support vectors to create a rule-based system for detecting malicious processes in an organisation's network traffic. We propose a method for building a rule-based system for detecting malicious processes in an organisation's network traffic using the distribution data of the relevant features of support vectors. The application of this method on real CSE-CIC-IDS2018 network traffic data containing characteristics of malicious processes has shown acceptable accuracy, high clarity and computational efficiency in detecting malicious processes in network traffic. In our opinion, the results of this study will be useful in creating automatic systems for detecting malicious processes in the network traffic of organisations and in creating and using synthetic data in such systems.

The paper proposes a method of improved adaptive integral arithmetic coding. This method is advisable to use in the technology of multi-level processing of video data based on the JPEG method. The technology is based on the detection of key information at several stages of video data processing. To reduce the output volume, the RLE algorithm and integral arithmetic coding are adapted to the new structure of the input data. Thus, the method of linearization of two-dimensional transformants based on zig-zag scanning was further developed. The differences of the method consist in carrying out vector intertransformation zig-zag linearization taking into account the selection of spectral components defined as complementary. The linearized decomposition approach was developed for the first time transformants based on entry into control ranges. In connection with the presence of different types of transformants in the group, the threshold is adapted according to the criterion of the total uneven number of non-equilibrium complementary components. On the basis of taking into account the probability of occurrence of dictionary elements, integrated arithmetic coding (two-dictionary integrated arithmetic coding) has been improved. Determination of current code components according to the decomposed working interval depending on the power of the dictionaries of significant elements and the number of repetitions. This allows you to additionally take into account the statistical features of the components of the RLE-structured linearized transformants and reduce the length of the arithmetic code; for the first time, a transformant compression method was created based on the reduction of various types of redundancy in groups of transformants. Comparative experimental analysis with known methods indicated that the developed technology has a higher compression ratio with reduced processing time. This makes it possible to ensure the necessary level of access and reliability in the conditions of the growth of the original volume of data.

Vehicle uptime is becoming increasingly important as transportation solutions become more complex and the transportation industry looks for new ways to be competitive. Traditional fleet management systems are gradually being extended with new features to improve reliability, such as better maintenance planning. Typical diagnostic and predictive maintenance techniques require extensive experimentation and simulation during development. This is not possible for the entire vehicle as it would require too many engineering resources. The developed diagrams presented in the article reflect the processes that take place at service stations. Various factors affecting quality and speed, such as the availability or ordering of spare parts, were taken into account when developing the service charts. With the help of the proposed diagrams, the client can independently decide whether to use the services of an authorized car service. Before using the services of a car service, it is advisable to familiarize yourself with the information presented on the website. This will help you choose a car service that meets all your expectations in terms of professional assistance. It is worth looking for a service station combined with an inspection station. Then you can fully check the performance of the car.

The current article deals with the implementation of Reinforcement Learning based Field Oriented Control (FOC) for the induction motors (IM). It is pertinent to mention that although conventional controllers like PID are widely used in FOC induction, they are model-based and face problems such as parameter adjustment. PID controllers need to be tuned because of the approximations of the model, variations of the parameters during operation, and the external disturbances that are uncertain and unpredictable. RL is a machine learning approach that is model-free which can adapt to the variations and disturbances. Therefore, these controllers can be an excellent alternative to the conventional controllers. In this study, an RL-based controller was used to control the speed of the induction motor using the FOC and space vector modulation (SVM). Computational simulations were done using the MATLAB/SIMULINK to test the controllers’ performance under different operating conditions. This study highlights the effectiveness of RL in optimizing IM control, offering potential benefits in various industrial and automation applications.

This study suggests an asymmetric multilevel inverter based on DSTATCOM that employs SVPWM techniques to produce higher output levels. There are two steps in the suggested inverter. One full bridge and two half bridges make up the inverter's main stage. A full bridge converter had four switches and a single DC source, while half bridges have separate DC sources with a voltage ratio of 1:3:3. Every cell has a fixed neutral point and is connected in a cascaded fashion. The inverter's performance is not improved by setting the DC source values equally. A folded cascaded H-bridge circuit running at a line frequency makes up the second circuit. Control plan PWM's space vector modulation was used to verify the suggested topology. Each control scheme's specific methodology as well as switching pulses are covered in great detail. In MATLAB/Simulink, the suggested system has been simulated with an output voltage of 350 V and an output current of roughly 3.5 A.

The importance of solar energy is manifested in the growing demand for renewable energy sources around the world, which is fueled by environmental concern and the scarcity of conventional energy. Maximum power point trackers (MPPTS) are necessary in solar energy systems due to atmospheric changes that threaten the efficiency of solar energy systems. This article compares MPPT technologies. Including traditional and modern techniques, and despite the results achieved by classical techniques in maximizing and extracting as much energy as possible, they face a big problem in reaching the bone energy point. These provide modern technologies such as FLC and SMC. They provide exceptional accuracy and excellent response in all environmental conditions but come with additional complexity and higher cost. These technologies are ideal in applications that require high performance under continuously changing conditions or in difficult environments (such as large solar power systems or systems dealing with large fluctuations in illumination). This research aims to conduct a comprehensive study and compare of classical technologies (P&O and IncCond) and modern technologies sliding mode control (SMC, Fuzzy Logic Control – FLC), taking into account factors such as efficiency, complexity and response time. Tests were conducted under different climatic conditions to understand and enhance the efficiency of MPPT technologies. Our study highlights the enhanced performance for methods based on modern technologies. This study provides a comprehensive comparative analysis, and by improving the efficiency and reliability of solar energy systems, our research supports the advancement of sustainable energy solutions.

In this manuscript, we aim to address Ordinary Differential Equations (ODEs) by α-Parameterized Differential Transform Method (α-PDTM). Additionally, we seek to enhance the effectiveness of α-PDTM by incorporating the Dandelion Optimizer (DO). The DO plays a crucial role in optimizing the parameter α, ensuring its adjustment and modification to secure the most favorable value. This refinement results in a more accurate approximation compared to conventional methods. The proposed approach, referred to as (αDO-PDTM), demonstrates a solution distinguished by its reliability and efficiency, as determined through the computation of Maximum Absolute Error (MAE) and the Mean Square Errors (MSE).

The article presents the materials of diagnostic application of the method of differential Mueller-matrix mapping of optically anisotropic architectonics of the layers of soft matter of the female reproductive sphere – histological sections of uterine biopsy. The efficiency and accuracy of differential diagnostics of benign and precancerous conditions of endometrial tissue are considered using statistical analysis of algorithmically reproduced maps of average values of linear and circular birefringence and dichroism parameters of optically anisotropic architectonics of representative samples of native histological sections of the uterine wall. The values of the balanced accuracy of differential diagnostics are presented by using the technique of statistical analysis of coordinate distributions of the mean values of the optical anisotropy parameters.

Recently, the Internet of Things (IoT) has grown to encompass the surveillance of devices through the utilization of Indoor Positioning Systems (IPS) and Location Based Services (LBS). One commonly used method for developing an Intrusion Prevention System (IPS) is to utilize wireless networks to determine the location of the target. This is achieved by leveraging devices with known positions. Location-based services (LBS) play a vital role in many smart building applications, enabling the creation of efficient and effective work environments. This study examines four memoryless positioning algorithms, namely K-Nearest Neighbour (KNN), Decision tree, Naïve Bayes and Random Forest regressor. The algorithms are compared based on their performance in terms of Mean Square Error, Root Mean Square Error, Mean Absolute Error and R2. A comparative analysis has been conducted to verify the outcomes of different memoryless techniques in Wi-Fi technology. Based on empirical evidence, Naïve Bayes has been determined to be the localization strategy that exhibits the highest level of accuracy. The dataset containing the Received Signal Strength Indicator (RSSI) measurements from all the studies is accessed online.

In research work the problem of studying the process of ozone control with the help of electronic sensors is considered. In research work, special sensors were used, which are formed around coronary electrodes in the ozonator and to monitor the concentration of ozone in the room. This is because ozone is known to adversely affect human health if its maximum permissible air concentration exceeds 0.16 mg/m3. A small system of ozonators was developed in a special laboratory, theoretical and experimental tests were carried out. In practice, the obtained data and the electric diagram of the ozonator (on the ARDUINO platform) were collected. "Prana Air" sensors and current sensors were used to accurately determine the ozone (O3) concentration around the ozone nozzle to measure the current at the electrodes.

Access to fresh water has become a headache for many nations around the world today due to water scarcity. Since this aspect is increasing, this paper introduces a system to estimate the households’ water needs in a day taking into consideration, the size of the family, zone, temperature, season, working status, location, and religion. By applying machine learning, the system determines the balance of water distribution between the families by these parameters. The predicted values determine the water requirements for each household hence the distribution of water in the households on a particular day. This includes the use of an Arduino microcontroller, water flow meter, and solenoid valves that make the water supply system to be an automatic system. As a result of this, the system helps to control the wastage of water and also ensures that the amount of water required in each home is assessed accurately.

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In this paper, the challenge of peace education appears as an emergent cultural need in a context of conflictual relationships among human beings and between human beings and their life environment. All sectors of human activity, including agriculture, can potentially contribute to building a cultural barrier to a mounting wave of wars. The ecological approach to agriculture through the ecosystem concept is suitable for unveiling the power of cooperation in developing symbiotic relationships between and among agroecosystem components for attaining a balance of ecosystem services (supporting, provisioning, regulating, and cultural). Enhancement of biodiversity within and between cultivated fields is key for the maintenance of sustainable agriculture, land management, and viable rural community. Scaling up cooperation among hierarchical levels of agriculture organisation (field, farm, and regional, national, international landscapes) appears as the most promising solution for developing a coherent framework consistent with building a peaceful attitude for managing both land, food systems, and friendly relationships among human beings. Organic farming is already a certified and appreciated method of agriculture production that has a potential for improving a peaceful attitude when supported by the core principles of Agroecology.

The growth of anthropogenic impacts on environment rapidly acelerated since 1950s and led to overshooting of several planetary ecological thresholds, decreasing the Earth´s ability to support our civilization. Even the concept of sustainability did not lead to recognition of necessary limits to this growth. These limits define the framework of our study on the roots of the current environmental crisis. Numerous studies have been devoted to environmental degradation, but analyses of its indirect driving forces are often fragmentary and confusing. We have attempted to provide a systemic assessment of three groups of indirect drivers by reflecting new physical ones and adding the paradigmatic drivers – such as the mechanical heritage and low reflection of life´s awesome complexity. A paradigm shift is required, as the current paradigm is incompatible with the reality of the Earth´s life support systems erosion. We also strived to contribute to much debated value-based category analysis. This underlines complexity of the driving forces of the crisis. Moreover, some absurd theories of how to come out of the crisis were mentioned. We stress the opportunity for Christians to put into practice the biblical messages to become caring stewards of nature in the God's image.

Ramsar Convention on Wetlands of International Importance, particularly focusing on waterfowl habitats, signifies a pivotal shift in global attitudes towards wetland conservation. Historically viewed as hindrances to development, wetlands have suffered from drainage and reclamation efforts, resulting in significant biodiversity loss. However, a growing recognition of their ecological significance has spurred international action. The Convention, established in 1971, serves as a landmark agreement to safeguard wetland ecosystems. It mandates member states to designate and conserve wetlands within their territories, emphasizing the importance of international cooperation due to the transboundary nature of wetland ecosystems. The Convention’s objectives encompass sustainable development goals, including water quality, biodiversity, and climate change mitigation. Implementation relies on national policies and collaboration among stakeholders. Furthermore, the Convention aligns with other environmental agreements, fostering synergies to enhance wetland protection globally. Despite these positive developments, there are significant challenges in ensuring the effective implementation of the Convention, particularly when economic interests conflict with environmental conservation. As highlighted in the case of the Bystroe Canal project in the Danube Delta, balancing development with wetland protection remains a critical issue. This article addresses the question of whether the Ramsar Convention can effectively apply its provisions in such cases, arguing that while it provides a strong framework, its effectiveness is often undermined by competing economic priorities. Without stronger enforcement mechanisms and increased international cooperation, the objectives of the Convention risk being compromised.

Air pollution has become a scourge to contend with in India. The recorded concentrations of particulate matter (PM) in the atmosphere, the unabated emission of pollutants from vehicular exhausts, and recurring episodes of extremely poor condition (AQI>300) in the winter months, have rightfully and necessarily, spurred efforts in the industrial, governmental and research spheres to alleviate its detrimental impacts. Various point sources like biomass burning, coal combustion for power generation, and traditional agricultural practices such as stubble burning, collectively contribute to a steady rise in ambient particulate matter (PM) pollution. This study focuses on the utilization of rice straw – an abundant agricultural residue in a country like India – motivated by promoting and contributing to the soil-to-soil circularity paradigm. It encompasses the characterisation of straw ash from the rice, by delineating its physical properties, thermal characteristics, and chemical composition with the help of Thermogravimetric analysis (TGA), X-ray diffraction (XRD), Electron Probe Microanalysis (EPMA), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM) and Dynamic Light Scattering (DLS). The results indicate that rice straw ash (RSA) possesses high silica content and favorable thermal stability. The RSA exhibited a porous structure, which enhances nutrient adsorption and microbial activity. Its incorporation into soil significantly improved soil nutrition and health, promoting a more sustainable agricultural practice. Entrenching this soil-to-soil thinking will contribute directly and indirectly to a host of sustainable development goals in a future Indian circular bioeconomy.

The aim of the article is to present the key elements of the State Forests’ “Spend the Night in the Forest” program as well as the selected results of its evaluation in the view of education for responsible tourism. The article is divided into two parts. The first, theoretical part, presents the assumptions of the idea of Leave No Trace outdoor ethics, which tally with the paradigm of education for responsible tourism. Its task is to develop the skills, knowledge and competences in the field of rational and responsible use of natural resources. At the same time, it lays the grounds for the “Spend the Night in the Forest” program. In the second, empirical part, the author presents selected results of the evaluation of the “Spend the Night in the Forest” program including demographic characteristics of its participants, their motivations, preferences regarding the choice of area, the way of organizing the camping, types of activities undertaken as well as assessment of the principles corresponding to the Leave No Trace idea. In addition, the article presents suggestions regarding the division of respondents into user groups. The cited results indicate that the respondents declared their willingness to take care of the forest as a common good and tried to educate and influence other tourists camping in a way inconsistent with the Leave No Trace principles. It can be concluded that the “Spend the Night in the Forest” program supports education for responsible tourism. By enabling the participants to experience nature in its natural state, it helps rebuild human relationship with nature. Moreover, the program serves the purpose of shaping public awareness that we all influence the environment and that we all are responsible for its current and future state.

The rapid development of new technologies, together with the emergence of new military conflicts and humanitarian crises, creates the need for quick response and the introduction of new methods of effective assistance to the affected population and the restoration of damaged territories. Decentralized charity became one of these methods of assistance. To implement the task, the latest technologies were used, which combine maximum transparency and complete security when forming and working with charitable payments. The main technologies that were used: blockchain and smart contracts on it, protocols of liquidity pools and profitable farming, as well as web development technologies for creating a web application. The research analyzed existing approaches, methods of software solutions for financing decentralized charity; developed and improved the mathematical model and architecture of the software application. A system of smart contracts was created using additional administrative modules, and on its basis, a decentralized charitable platform for assistance to Ukraine was implemented in the form of a web application. Such a decentralized system can be freely used and implemented nowadays to finance the most necessary charitable projects of our country with crypto-assets in various spheres: humanitarian, social or in matters of security and weapons.

The burden of lung diseases on world health is substantial, underscoring the vital necessity of timely detection. The VGG16 architecture with additional convolutional layers is used in this study to provide a hybrid method to lung disease classification. It incorporates the Synthetic Minority Over-sampling Technique (SMOTE) to improve model performance in response to the problem of imbalanced class instances. The subset of the NIH Chest X-ray dataset is used to train and assess the model. The designed model classifies the images into 8 different classes of lung diseases. They are Emphysema, Cardiomegaly, Atelectasis, Edema, Consolidation, Mass, Effusion, Pneumothorax. The proposed model delivered accuracy of 96.42% which demonstrates the efficacy in precise classification of lung diseases. The Graphical User Interface (GUI) is integrated for better interaction between the patient and the model. Through improved diagnostic capabilities, this suggested method not only promotes technological innovation but also shows promise for enhancing patient care and health care outcomes.

Cardiovascular disease such as ischemic heart disease and stroke are the most dangerous diseases in the WHO stats. Myocardial Infarction (MI), an ischemic disease of the heart, occurs due to a sudden blockage in the coronary arteries that supply blood to the heart causing a lack of oxygen and nutrients. The MI patient needs continuous monitoring using electrocardiography, the latter is always at risk of developing complications such as arrhythmias. As a solution, we proposed an internet of things (IoT) based ECG system for monitoring, the application layer was reserved for the detection of MI and arrhythmias using artificial intelligence so that the patients can keep being monitored even outside health facilities. For this purpose, this paper proposed a hybrid Convolutional Neural Network (CNN) – Bidirectional Long Short-Term Memory (BiLSTM) approach to classify ECG signals and evaluates its performance by using raw and preprocessed data, and comparing the results to related studies. Two datasets have been used in this classification. The results were promising, the model has scored 99.00% accuracy on raw data classifying 4 classes, and 99.73% accuracy on a larger preprocessed data for 3 classes classification. The proposed model is suitable to serve in our monitoring task.

In contemporary economic landscapes, effective managerial decision-making requires serious justifications often facilitated by information technologies. The article proposes the application of the foundational principles from information theory and entropy to determine the correlation between education and economic development. This research employs entropy methodology to explore the correlations and implications of these multifaceted domains, drawing insights from a range of global indices. By analyzing data from indices such as the Human Development Index, we investigate the relationship between average years of education and entropy values, shedding light on the diversity and standardization of human capital systems across nations. Moreover, economic indexes, encapsulating factors such as economic freedom and innovation, provide critical insights into the vibrancy and resilience of national economies. Through the lens of the Global Innovation Index, we explore how countries with higher levels of economic freedom and innovation efficiency exhibit less diversity in economic approaches and innovation strategies, leading to a reduction in entropy values. By examining entropy values in conjunction with innovation indicators, such as research and development expenditure and patent applications, we uncover patterns and trends indicative of the diversity or standardization of innovation strategies across countries. Additionally, cybersecurity emerges as a critical dimension of national security and resilience, with implications for economic well-being and societal trust. Through the National Cybersecurity Index, we assess the level of diversity and uncertainty in cybersecurity landscapes, highlighting the importance of robust cybersecurity measures in safeguarding against emerging threats and reducing entropy values. Ultimately, this research contributes to the discourse on sustainable development and resilience in an interconnected world. By identifying specific challenges and opportunities within education, economy, innovation, and cybersecurity domains, it facilitates informed decision-making and strategic interventions to promote inclusive growth and societal well-being on a global scale.

In the process of operation, the simplest elements (hereinafter elastic bodies) of machines and mechanisms under the influence of external and internal factors carry out complex oscillations ‒ a combination of longitudinal, bending and torsion combinations in various combinations. In general, mathematical models of the process of such complex phenomena in elastic bodies, even for one-dimensional calculation models, are boundary value problems for systems of partial differential equations. A two-dimensional mathematical model of oscillatory processes in a nonlinear elastic body is considered. A method of constructing an analytical solution of the corresponding boundary-value problems for nonlinear partial differential equations is proposed, which is based on the use of Ateba functions, the Van der Pol method, ideas of asymptotic integration, and the principle of single-frequency oscillations. For "undisturbed" analogues of the model equations, single-frequency solutions were obtained in an explicit form, and for "perturbed" ‒ analytical dependences of the basic parameters of the oscillation process on a small perturbation. The dependence of the main frequency of oscillations on the amplitude and non-linearity parameter of elastic properties in the case of single-frequency oscillations of "unperturbed motion" is established. An asymptotic approximation of the solution of the autonomous "perturbed" problem is constructed. Graphs of changes in amplitude and frequency of oscillations depending on the values of the system parameters are given.

The health of the supply chain, the company's performance, and the quality of the production as well as the success of the entire enterprise, directly depends on the reliability of the company's existing suppliers. Processing enterprises that depend on suppliers are trying to find the best option that will satisfy all customer requirements. With high-quality and inexpensive raw materials, the products produced by the enterprise will largely determine its economic indicators such as revenue, profit, and profitability. Therefore, this enterprise is especially faced with the issue of choosing the most appropriate supplier of resources. Basically, for processing enterprises it is very important to consider the parameters such as quality of incoming materials, terms of supply of raw materials, price of received raw materials, terms of contracts. The challenge in determining of supplier is how to choose reliable suppliers that can maintain supply chain continuity in an environment of ever-increasing instability and uncertainty. For this purpose, a methodology for selecting suppliers using Z-numbers was proposed. Using fuzzy Z numbers in supplier selection, decision-makers can assign values to various criteria in a way that reflects both the uncertainty and the confidence associated with those values. This can lead to more nuanced and robust supplier selection processes, considering a wider range of factors and uncertainties.

The object of the research is the process of selecting a content recommendation method in information services. The study's relevance stems from the rapid development of informational and entertainment resources and the increasing volume of data they operate on, thus prompting the utilisation of recommendation systems to maintain user engagement. Considering the different types of content, it is necessary to address the problem of data filtration based on their characteristics and user preferences. To solve this task, we analysed content-based and collaborative filtering methods using various techniques (model-based, memory-based, and hybrid collaborative filtering techniques), knowledge-based filtering, and hybrid filtering methods. Considering each method's advantages and disadvantages, we chose a hybrid method using model-based collaborative filtering and content-based filtering for the future development of our universal recommendation system.

Predicting student's academic performance during online learning has been considered a major task during the pandemic period. During the online mode of learning, academic activities have been affected in such a way that the management of educational institutions has planned to design support systems for predicting the student's performance to reduce the dropout ratio of the students and bring improvement in academic activities. During COVID-19, the main challenge is maintaining student's grades by predicting their academic performance using different techniques such as Education Data Mining and Learning Analytics. Different features have been identified related to the teaching mechanisms in online learning, which have a great impact on the improvement of academic performance. A high-quality dataset helps us to generate productive results, which in turn helps us to make effective decisions for promoting high-quality education. In this research, five prediction models for predicting academic performance have been proposed by collecting an imbalanced dataset of 350 students from the same computer science domain. After applying pre-processing techniques for cleaning the data, machine learning models have been applied, including K-Nearest Neighbor Classifier, Decision Tree, Random Forest, Support Vector Classifier, and Gaussian Naive Bayes. Results have been predicted for an imbalanced and balanced dataset after feature selection. Support Vector classifier has produced the best results in a balanced dataset with selected features by giving an accuracy of 96.89%.

The paper presents the analysis of neurobiological data on the existence of the structure of a parallel-hierarchical network. Discussed method of parallel-hierarchical transformation based on population coding and its application for the pattern recognition task. Based on the analysis, we can conclude that using the methods proposed, it is possible to measure the geometric parameters and properties of images, which can significantly increase the efficiency of processing, in particular estimating the center of mass based on moment characteristics. Experimental results demonstrate that due to various destabilizing factors, accurately measuring the energy center coordinates of laser beam spot images is challenging. However, training the PI network and classifying the fragments into "good" and "bad" can considerably enhance the accuracy of these measurements.

The article offers conceptual foundations for formalizing the process of assessing a level of human capital (HC) management at the enterprise using mathematical and computer modeling based on neural network technologies. The methodological approach for assessing the level of human capital management has been improved. This allows the use of neural network tools to identify accurately and reasonably the level of HC management with the help of self-learning multilayer perceptron. The weight coefficients of such a network were calculated. An appropriate artificial neural network – a multilayer perceptron – was built using the mathematical software MatLab and it was successfully diagnosed. The improved mathematical model for assessing the level of HC management at the enterprise makes it possible to display transparently a set of input parameters on a set of output solutions, to decompose such a process, and to simplify the procedure of its formalization. The designed neural network allows us to determine quickly and accurately the level of HC management at the enterprise. The conceptual approach proposed by the authors has several significant advantages over existing alternative methods: accuracy of assessment; taking into account a wide range of various evaluation parameters of impact; high speed of making decisions and self-learning ability. The proposed approach was successfully implemented to assess the level of HC management at 24 domestic enterprises. The information system "HC" developed by the authors allows to calculate the estimated parameters of the evaluation process; to determine the level of HC management based on the mathematical apparatus of the multilayer perceptron. Such estimates correlate with the estimates obtained by the experts of these enterprises which indicates the adequacy of the approach proposed by the authors. Therefore, the proposed information system for assessing the level of management of the HC allows accurate implementation of such a process with minimal time and money costs.

This manuscript explores a two-dimensional hyperchaotic map for generating chaotic oscillations. Hyperchaotic maps are finding increasing applications in various scientific and technological fields due to the unique properties of their generated oscillations. The studied map, based on two interconnected piecewise-linear functions, is one of the simplest for generating oscillations with a predetermined distribution of values across a continuous parameter space. This simplicity allows for wide applicability in various contexts. The paper presents simulation results demonstrating control over the parameters of the dynamic modes. Building upon these modeling results, a two-dimensional hyperchaotic system is implemented using an electric circuit. The chosen map is attractive due to its inherent simplicity and ease of parameter control. By adjusting these parameters, the distribution of the generated signal's values can be manipulated. The circuit consists of two symmetrical sections connected via feedback loops, employing four amplifiers with variable gain. The gain values act as the circuit's implementation of the control parameters. Chaotic oscillations are generated by applying a delayed clock signal from an external square wave generator to circuit elements. The obtained experimental results exhibit excellent agreement with the simulation data.

The work used Maxwell's electromagnetic theory to quantitatively describe the emissivity of solid materials through electrical resistivity and temperature. An equation is proposed for recalculating the emissivity of smooth surfaces into powdery or rough surfaces. The obtained theoretical characteristics of the change in the emissivity of 316L powder steel were compared with experimental ones. As a result of the comparison, it was established that the experimental results obtained correlate with theoretical calculations and do not go beyond the limits of the expanded uncertainty of measurement.