Integrated System Research Articles

Design of ION Thruster

Ion thrusters represent a significant advancement in electric propulsion, enhancing fuel efficiency by accelerating ions for thrust generation. These systems, demonstrated in missions such as Deep Space 1 and Dawn, facilitate substantial velocity changes with minimal propellant, making them suitable for long-duration space missions. This paper outlines the design of an ion thruster aimed at achieving a thrust of 0.2 Newton, focusing on the use of xenon as the propellant and employing highvoltage grids for ion acceleration. Key design aspects include an efficient power supply delivering at least 26 W, effective thermal management, and the integration of control systems. Performance metrics target a specific impulse of 3,000 seconds and a thrust-to-power efficiency ratio greater than 0.5. Through detailed calculations, including arc distance, flow configuration and velocity analyses and testing using different materials, this study identifies optimal configurations for thrust generation. The developed ion thruster is tailored for small satellites (700-800 kg) to enhance trajectory control and station-keeping, underscoring the importance of ion propulsion in modern space exploration

Scalable Parking Management Using Cloud Infrastructure and DevOps

This project presents a scalable parking management system built using PHP and SQL, integrated with cloud infrastructure and DevOps methodologies. The system dynamically scales to handle high traffic using cloud auto-scaling and load balancing techniques, ensuring reliable performance. By leveraging containerization, continuous integration, and monitoring tools, the project automates deployments and ensures minimal downtime. The goal is to provide an efficient and resilient solution to parking management challenges, enabling seamless user experience during peak times while maintaining system integrity and availability.

Assessment of competitive advantages of IT system integrator companies taking industry factors into account

Purpose. To evaluate competitive advantages of IT system integrators in Ukraine. To investigate the influence of industry factors on the formation of competitive advantages using cases of the mining and fuel & energy industries. Methodology. The empirical basis of the study was presented by performance indicators of the leading system integrator companies of Ukraine. The methodological basis of the research was the scoring method using weight coefficients and the graphic method of creating a radial diagram of competitive advantages. During the research, the following methods were used: analysis and synthesis (to study the components of competitive advantages and to form integral indicators), induction and deduction (to form market trends and to identify enterprise strategies), classification (to form indicators that determine competitive advantages). Findings. In the process of research, a model of competitive advantages for system integrator companies was formed. A methodology was developed and an assessment of competitive advantages of the leading system integrators of IT industry of Ukraine was carried out. A radial diagram of competitive advantages was created. It allows performing a comparative analysis of the factors of company competitive advantages with the aim to form a development strategy to improve the competitive position of the company in the market. Features of influence of the industry factors on the formation of competitive advantages of system integrators were considered and this influence was evaluated using cases of the mining and fuel & energy industries. Originality. A model and methodology for assessing competitive advantages for system integrator companies in IT industry were proposed; the main factors of competitive advantages for system integrators have been identified. The necessity of taking industry factors into account when assessing the competitive advantages of these enterprises has been proposed and confirmed by the research results. Practical value. The applied value of the research lies in the possibility of applying the developed model and methodology to assess the competitive advantages of system integrators. The results of such an assessment can be used by system integrators to develop a competitive strategy and improve their competitive positions in the market. Customers (including enterprises in the mining and fuel & energy industries) can use evaluation elements when conducting procurement (tender) procedures for selection of contractors for the implementation of system integration projects. Also, the results of the study can be used for the analysis of the system integration market.

AI-driven monitoring systems for bioremediation: real-time data analysis and predictive modelling

Artificial Intelligence (AI) and Machine Learning (ML) are transforming the field of bioremediation by enabling real-time monitoring and optimization of environmental cleanup processes. This paper explores the integration of AI-driven monitoring systems with bioremediation techniques, focusing on how real-time data analysis and predictive modelling can enhance the effectiveness of pollutant degradation. These AI systems continuously collect data from contaminated sites, such as soil and water, and analyse variables like microbial activity, pollutant concentration, and environmental conditions. By processing this data, AI models can optimize the bioremediation environment, adjusting factors such as pH, temperature, and nutrient levels to maximize microbial efficiency. Predictive modelling plays a crucial role in forecasting remediation outcomes, allowing for proactive adjustments to improve the speed and success of the process. The study also highlights the potential of AI in reducing operational costs by automating data collection and analysis, minimizing the need for manual intervention. Furthermore, it discusses challenges related to data quality, system integration, and the scalability of AI applications in real-world bioremediation projects. By leveraging AI's capability to provide real-time insights and predictive analytics, this research demonstrates its potential to significantly enhance the precision and sustainability of bioremediation efforts, paving the way for smarter environmental management.

AI-driven business analytics and decision making

The rapid advancement of Artificial Intelligence (AI) and Machine Language (ML) has revolutionized business analytics, transforming the way organizations make decisions. This paper explores the integration of AI-driven technologies into business analytics to enhance decision-making across various industries. By leveraging predictive and prescriptive analytics, AI enables organizations to not only analyse historical data but also forecast future trends, allowing for more informed, proactive strategies. Machine learning plays a pivotal role in automating data-driven decisions, offering real-time insights that help businesses respond quickly to changing market dynamics. This automation significantly reduces manual intervention, increases efficiency, and enhances the accuracy of predictions. The paper further discusses the integration of AI with Business Intelligence (BI) tools to deliver deeper insights from complex datasets in real time. These insights empower companies to optimize enterprise resources, improve supply chain management, and drive operational excellence. Case studies from AI-driven analytics within Systems, Applications, and Products in Data Processing (SAP) environments highlight the practical applications of AI in real-world business contexts, demonstrating its impact on decision-making and overall performance. The paper concludes with best practices for implementing AI in business analytics, focusing on data quality, system integration, and workforce readiness to embrace AI-enabled decision-making frameworks. The findings underscore the potential of AI as a game-changer in modern business landscapes, fostering smarter, faster, and more effective decision-making processes.

Navigating ethical challenges of explainable ai in autonomous systems

The rapid integration of autonomous systems, such as vehicles, drones, and robots, into various sectors brings forth significant ethical challenges concerning their decision-making processes. This paper examines the role of Explainable AI (XAI) in addressing these challenges, particularly regarding accountability in the event of accidents and the necessity of human oversight in automated environments. We discuss the critical ethical implications of transparency, emphasizing how XAI can bridge the gap between complex algorithmic decision-making and public understanding, thereby fostering trust in these technologies. The paper also outlines the current regulatory frameworks for AI safety, analysing their effectiveness in promoting responsible innovation. Furthermore, we investigate the consequences of opaque algorithms, especially in life-critical applications where the stakes are exceptionally high. Through an analysis of case studies, we showcase how organizations have successfully implemented XAI to enhance safety measures and uphold ethical responsibility in their autonomous systems. Ultimately, this study advocates for the integration of XAI as a vital component in developing responsible autonomous technologies, ensuring accountability, and safeguarding public trust in an era increasingly defined by automation.

A sales force effectiveness framework for enhancing healthcare access through pharmaceutical sales and training programs

This paper introduces a conceptual framework aimed at enhancing sales force effectiveness within the pharmaceutical industry, specifically focusing on improving healthcare access through optimized sales training programs and customer relationship management (CRM) systems. The proposed framework recognizes the critical role that pharmaceutical sales teams play in facilitating healthcare delivery, particularly during public health crises. It emphasizes the integration of effective training strategies and advanced CRM tools to enhance engagement with healthcare professionals, thereby improving product distribution and patient outcomes. The framework comprises three essential components Sales Force Training Enhancement, which focuses on developing targeted training programs that equip sales representatives with the necessary skills and knowledge to effectively communicate the value of pharmaceutical products to healthcare providers. This includes understanding clinical data, addressing healthcare providers’ concerns, and fostering a consultative selling approach. Secondly, CRM System Implementation, which underscores the significance of adopting robust CRM systems that streamline interactions between sales teams and healthcare professionals. By leveraging data analytics and customer insights, pharmaceutical companies can tailor their approach to meet the specific needs of healthcare providers, thereby enhancing relationship-building and communication. Thirdly, engagement and Outcome Measurement, which highlights the importance of measuring engagement levels and patient outcomes resulting from effective sales strategies. This component advocates for the use of key performance indicators (KPIs) to assess the impact of training and CRM initiatives on product distribution and healthcare access. The framework ultimately aims to foster a more collaborative environment between pharmaceutical sales teams and healthcare professionals, ensuring that patients receive timely access to essential medications and services. By focusing on the strategic integration of training and CRM systems, this framework presents a comprehensive approach to improving sales force effectiveness in the pharmaceutical sector. Future research and practical applications of this framework can further explore its implications for enhancing healthcare access, particularly in times of public health challenges.

Management of Hybrid Hydropower and Floating Solar Systems at Num Ngum 1 in Lao PDR

The integration of a hybrid hydro-floating solar power (HPP-FPV) system is covered in this study with the goal of improving energy management and producing more electricity. The production capacity of the hybrid system is almost twice as high as that of the original HPP system, demonstrating its potential for higher energy generation. Because it forecasts weather and modifies electricity generation accordingly. The analysis highlights how crucial it is to make sure that the hybrid system's electricity generation doesn’t go beyond the current power grid's maximum transmission capacity. This is essential to preserving the consistency and caliber of the power that is provided. The research's overall goal of increasing the output of renewable energy through creative system integration and practical management techniques is captured in the abstract.

Design and Implementation of a Synchronous AHB to APB Bridge in System-on-Chip Applications

The requirement of effectively combining key units in an integrated system is proliferating. SoC system is developed to provide a chip-level integration, which become the inevitable trend of integrated circuit development and is widely used in smartphones, industrial applications, and microcontrollers. The ARM AMBA protocol works as a universally adopted way of interaction between various parts of the system. In AMBA architecture, the AHB to APB bridge significantly contributes to combining the high-performance AHB bus and low-power APB bus in the SoC system. This project is to implement an AHB to APB bridge using Verilog, enabling stable data transfer between these two buses. The proposed AHB to APB bridge is intended to fit different read or write strategies and ensure the proper working of the peripherals on the APB bus. The bridge has been implemented via Verilog Hardware description language (HDL). A test bench was created with a virtual AHB host and an optimized SRAM as the high-speed APB peripheral. Verdi simulation shows the bridge completely meets the design intention.

Machine learning approaches toward an understanding of acute kidney injury: current trends and future directions.

Acute kidney injury (AKI) is a significant health challenge associated with adverse patient outcomes and substantial economic burdens. Many authors have sought to prevent and predict AKI. Here, we comprehensively review recent advances in the use of artificial intelligence (AI) to predict AKI, and the associated challenges. Although AI may detect AKI early and predict prognosis, integration of AI-based systems into clinical practice remains challenging. It is difficult to identify AKI patients using retrospective data; information preprocessing and the limitations of existing models pose problems. It is essential to embrace standardized labeling criteria and to form international multi-institutional collaborations that foster high-quality data collection. Additionally, existing constraints on the deployment of evolving AI technologies in real-world healthcare settings and enhancement of the reliabilities of AI outputs are crucial. Such efforts will improve the clinical applicability, performance, and reliability of AKI Clinical Support Systems, ultimately enhancing patient prognoses.

Integration of lean manufacturing system with novel intuitive fuzzy syncretic lean frame work to improve the overall equipment effectiveness

Lean manufacturing embraces the principle of doing more with less by removing non-value-added activities from manufacturing processes to preserve effectiveness, flexibility, and profitability. Predictive maintenance in production stages are more critical since it involves machines which are prone to failure and a post failure maintenance stagnates the production which creates bottlenecks due to machine failures. Hence in this work a novel intuitive fuzzy syncretic lean frame work has been implemented to incorporate the need of intelligent systems. The framework is divided into phases which employs Fuzzy logic with embedded smart sensors for effective utilization of man machine and materials in order to improve the effectiveness of a production floor. A novel time based forecasting is done in the design phase which implements the lean tool Takt time. The manufacturing phase uses the sensors to determine the predictive maintenance of the machines thereby implementing continuous flow as the lean tool and the inspection phase uses smart sensor system for real time continuous monitoring of the machining process thereby incorporating Poka-yoke as a lean tool. Thus by implementing the framework the overall equipment effectiveness is achieved which helps in achieving continuous flow of products and defect free products in any production firm.

Evaluation of the Structure of Dry Soil, Subjected to Dynamic Load in Different Managements

The structural evaluation of agricultural soil is essential for the performance and quality of production. Soils suffer surface consolidation when subjected to agricultural machinery traffic and alter their physical characteristics according to the management to which it is subjected. Therefore, this work aimed to evaluate the physical indices and surface consolidation of a red oxisol subjected to different loads and types of management. Undeformed soil samples were collected from three areas with different management systems: no-tillage, crop-livestock--forest integrated system (CLFI), and pasture area. Traffic simulations were conducted with application of dynamic loads, using 0 pass, 1 pass, and 2 passes (0, 108, and 216 kpa), evaluating the physical indices of the soil's natural specific weight (yn), dry apparent specific weight (yd), void index (n), and porosity (e). Subsequently, the samples were subjected to uniaxial compression tests using loads (108, 216, 432, 864, and 1500 Kpa) to verify the surface consolidation. The experiment was conducted in a completely randomized design, with a 3x3x5 factorial scheme in sub-subdivided plots. Thus, we verified that the no-tillage management system has a more porous soil due to its characteristics, presenting lower resistance and being better for planting. CLFI was characterized as a management that allowed the soil to be more resistant to the application of loads and, therefore, less porous. Pasture has an intermediate behavior.

Enhancing Aquaculture Productivity through Integrated Farming Systems: A Comparative Study of Fish-Pig and Fish-Duck Systems

Integrated farming systems involving fish-animal are of high relevance in current scenario. Studies like this asses the suitability of such systems in improving the aquaculture productivity and overall benefits to the ecosystem. It plays a crucial role in optimizing the use of small water resources in rural areas to enhance animal production. Given the potential of pig and duck manure as nutrient inputs in fish culture, a grow-out trial was conducted in a 0.1 ha earthen pond located in Elangitampatty, Kezhisenkattupatti, and Asakkattuppatti villages of Kollihills Taluk in Namakkal district, Tamil Nadu. The pond was stocked with four carp species: Catla, Rohu, Mrigal, and Grass carp in a ratio of 40:20:30:10 at a density of 7500 fish per hectare. Three treatments were employed: (1) Control (T0), where fish were fed with commercial feed, (2) Integrated fish-duck farming (T1), and (3) Integrated pig-fish farming (T2). In the control group (T0), fish were fed commercial fish feed (24% crude protein, 4 mm pellet size) at 4% of body weight, while in T1 and T2, fish received 50% of the commercial feed and were supplemented with duck and pig excreta, respectively. After an 8-month grow-out period, fish weights and total production in each group were recorded. Water quality parameters, such as pH, dissolved oxygen, free carbon dioxide, alkalinity, and hardness, were measured using standard methods (APHA, 2019). Results showed that pond fertilization with pig and duck excreta promoted the growth of algae, phytoplankton, and zooplankton, providing a natural food source for the fish. Total fish production was highest in the fish-pig integration system (T2), with a recorded yield of 4960.09 kg. Catla (Catla catla) had the highest individual species yield, with a total harvested weight of 2225.98 kg, followed by Grass carp (Ctenopharyngodon idella) at 1490.37 kg. The final average weight of Grass carp in T0, T1, and T2 was 1047 ± 13.27 g, 1151.10 ± 12.85 g, and 1490.37 ± 13.52 g, respectively. For Catla, the final average weight in T0, T1, and T2 was 910 ± 9.45 g, 960 ± 10.26 g, and 980 ± 12.20 g, respectively. Similarly, Rohu in T0, T1, and T2 weighed 850 ± 7.98 g, 900 ± 11.23 g, and 910 ± 11.16 g, respectively, while Mrigal in T0, T1, and T2 weighed 600 ± 6.04 g, 720 ± 8.50 g, and 730 ± 10.56 g, respectively. The results highlight that the fish-pig system outperformed the fish-duck system in terms of fish productivity, which may be due to the better nutrient composition in pig manure and the genetic potential of the fish species. An economic analysis revealed that the fish-pig integration system generated the highest net income (Rs. 555,353.90) and benefit-cost ratio (2.12), indicating that integrated fish-pig farming is more profitable than both fish-duck farming and the control group without integration.

Sistem Pendistribusian Zakat oleh Baznas Kabupaten Langkat Menurut Undang – Undang No. 23 Tahun 2011 tentang Pengelolaan Zakat: Studi Kasus di Desa Karang Rejo, Kec. Stabat, Kab. Langkat

UU no. 23 of 2011 concerning Zakat Management as a complement to the previous law, namely Law no. 38 of 1999. This law provides institutional strengthening in integrated zakat management into a unified integrated system. In turn, the National Amil Zakat Agency becomes the only institution holding zakat authority, and the Amil Zakat Institution as a partner in assisting the National Amil Zakat Agency, as well as government oversight as a regulator. The research aims to answer the problem of how the distribution of zakat according to zakat law no. 23 of 2011 in Kab. Langkat. How is the zakat distribution system by BAZNAS Langkat Regency in Karang Rejo Village. What are the factors that cause delays in the BAZNAS distribution system in Langkat Regency. How is the strategy carried out by BAZNAS Langkat Regency to increase the collection and effectiveness in distributing zakat in Karang Rejo Village, Langkat Regency. This research is a qualitative research using a legal sociology approach. The types of data used in this study are primary data and secondary data, secondary data as initial data obtained from primary and secondary legal materials, then followed by primary data or field data obtained from documents and interviews with several Amil Agency officials Langkat District National Zakat. The results of the study show that the National Amil Zakat Agency in Langkat Regency has not run optimally in the distribution of zakat due to several obstacles, one of which is the lack of synergy with the local government in terms of regulations that have an impact on non-optimal distribution. Keywords: Distribution of Zakat, BAZNAS Kab. Langkat.

Epidemiology, clinical characterization, and outcomes for heart failure with improved ejection fraction

Abstract Background Contemporary guidelines propose a new classification of heart failure with improved ejection fraction (HFimpEF). The prevalence of HFimpEF is expected to increase with the implementation of guideline-directed medical therapy (GDMT); however, there are limited data on the epidemiology of and outcomes for this novel clinical entity. Purpose We describe the prevalence, GDMT use and outcomes among patients with HFimpEF within a large, integrated healthcare delivery system. Methods We identified adults ≥18 years with incident HF with reduced EF (HFrEF ≤40%) between January 2013 and December 2018 from a large, integrated healthcare system serving >4.5 million members. HFimpEF was defined as known HFrEF with a follow-up EF measurement >40% within 12 months after incident HFrEF. Among HFimpEF patients, we examined GDMT use at discharge after incident HFrEF, incident HFimpEF, and at 6- and 12-months post-HFimpEF. From 12 months post initial HFrEF diagnosis, we calculated incidence rates of subsequent worsening HF (WHF) events (i.e., HF-related hospitalization, emergency department or urgent outpatient visits adjudicated using validated natural language processing-based algorithms) and all-cause death through December 31, 2019. We compared rates between patients with HFimpEF and those with persistent HFrEF (i.e., having all LVEF measurements within 12 months of incident HFrEF ≤40%) using Cox proportional hazards models adjusted for the EF value at incident HFrEF. Results Among 12,639 patients with incident HFrEF, 4,124 (33%) experienced HFimpEF within 12 months. Among patients with HFimpEF, use of β-blockers (82%) and angiotensin-converting enzyme inhibitor/angiotensin II receptor blockers (86%) was high, while angiotensin receptor-neprilysin inhibitor (1.2%) or mineralocorticoid receptor antagonist (28.2%) use was low. β-blockers, ACEi/ARB, and MRA use declined after incident HFimpEF (Figure, Panel A). Rates of WHF were 19.7 (95% Confidence Interval [CI]: 18.7 to 20.6) per 100 person-years among patients with HFimpEF vs. 40.0 (95% CI: 38.5 to 41.5) among patients with persistent HFrEF (hazard ratio [HR]: 0.55, 95% CI: 0.49 to 0.62; Figure, Panel B). Rates of death were 5.2 (95% CI: 4.7 to 5.7) and 8.4 (95% CI: 7.8 to 9.1) per 100 person-years among HFimpEF and persistent HFrEF, respectively (HR: 0.59, 95% CI: 0.52 to 0.67). Among patients with HFimpEF, those with an absolute EF improvement of ≥20% were less likely to experience WHF (HR: 0.82, 95% CI: 0.67 to 0.99) and death (HR: 0.85, 95% CI: 0.69 to 1.04) compared with those with <20% improvement. Conclusions One in three patients experienced improvement in EF within 12 months of initial HFrEF diagnosis. HFimpEF is associated with a reduced risk of WHF events and death, although these patients remain at significant clinical risk. Further research is necessary to assess the potential impact of sustained adherence to GDMT and therapeutic optimization on HFimpEF patient outcomes.

A Drosophila-inspired intelligent olfactory biomimetic sensing system for gas recognition in complex environments

The olfactory sensory system of Drosophila has several advantages, including low power consumption, high rapidity and high accuracy. Here, we present a biomimetic intelligent olfactory sensing system based on the integration of an 18-channel microelectromechanical system (MEMS) sensor array (16 gas sensors, 1 humidity sensor and 1 temperature sensor), a complementary metal‒oxide‒semiconductor (CMOS) circuit and an olfactory lightweight machine-learning algorithm inspired by Drosophila. This system is an artificial version of the biological olfactory perception system with the capabilities of environmental sensing, multi-signal processing, and odor recognition. The olfactory data are processed and reconstructed by the combination of a shallow neural network and a residual neural network, with the aim to determine the noxious gas information in challenging environments such as high humidity scenarios and partially damaged sensor units. As a result, our electronic olfactory sensing system is capable of achieving comprehensive gas recognition by qualitatively identifying 7 types of gases with an accuracy of 98.5%, reducing the number of parameters and the difficulty of calculation, and quantitatively predicting each gas of 3–5 concentration gradients with an accuracy of 93.2%; thus, these results show superiority of our system in supporting alarm systems in emergency rescue scenarios.

From fragmentation to functionality: Enhancing coherence of digital health integration in health systems.

Digital health programs continue to be implemented within Canadian health systems at a steady pace. The effectiveness of digital health initiatives has been rigorously analyzed, with both benefits and drawbacks extensively commented on. While the discussion about digital health continues, both positive and negative perspectives of it are approaching saturation in their themes. Accepting that digital health is here to stay post-pandemic, the focus should shift to strategies and supports needed to avoid the fragmentation of care through digital health implementation. This short article poses three questions which policy-makers and decision-makers should explore as part of a level-setting exercise with involved stakeholders at the outset of a digital health program's consideration. An implementation team should design the digital health program to have equity as its foundational focus, conduct value-based evaluations, and position the program in a learning health system framework to guard against the fragmentation of care.

The INPHET- a global approach to Environmental Public Health Tracking

Abstract Environment and health monitoring and information systems are fundamental infrastructure for evidence provision for policy making. The International Network for Public Health and Environment Tracking (INPHET) was formed by a community of environmental public health practitioners to address common interests and activities that advance environmental public health surveillance (EPHS) and data systems, and their uses to inform public health actions. Its main goal is to address the vast inequalities across countries and communities in their ability to monitor, assess, inform and mitigate environmental and health issues. INPHET supports the relevant role of environmental epidemiology as the pillar science providing evidence for decision making and interventions aiming at reducing and preventing environmental related diseases and contributes to an overall vision for public health. As a “Community of Practice”, INPHET focuses on sharing experiences and expertise; addressing common challenges for implementing and enhancing the science of EPHS/tracking; improving understanding of the link between the environment and health; and providing the appropriate evidence that informs public policy. The network, initiated in conference meetings in 2012-13 and formally launched in Modena, Italy in 2014, has carried out several activities which aim at sharing expertise and learning from different models of environmental and health data integration and surveillance systems that directly inform policy. These include Twinning cooperation with Euro-Asian and Balkan countries and sharing of expertise with African Public Health Agencies. Some experiences are presented in this workshop and highlight the EPHS/EPHT model adopted by several national public health agencies. It promotes that such integration and uses of environmental and health data is key for EPHS, including their accessibility and usability for communities of public health researchers, practitioners and policy makers.

Mathematical Model of a Nonlinear Electromagnetic Circuit Based on the Modified Hamilton–Ostrogradsky Principle

This paper presents a mathematical model of a typical lumped-parameter electromagnetic assembly, which consists of two subassemblies: one includes a magnetic circuit and the other with selected elements of electric circuits. An interdisciplinary research approach is used, which assumes the use of a modified integral method based on the variational Hamilton–Ostrogradsky principle. The modification of the method is the extension of the Lagrange function by two components. The first one reflects the dissipation of electromagnetic energy in the system, while the second one reflects the effect of external non-potential forces acting on the electromagnetic system. This approach allows for the avoidance of the inconvenience of the classical theory, which assumes the decomposition of the entire integrated system into individual electrical subsystems. The state equations of the electromagnetic subassembly are presented solely on the basis of the energy approach, which in turn allows taking into account various latent motions in the system, because the equations are derived based on non-stationary constraints between subsystems. The adopted theory allows for the formulation of the model of the system in a vector form, which gives much more possibilities for the analysis of higher-order electromagnetic circuits. Another important advantage is that the state equations of the considered electrical object are given in Cauchy normal form. In this way, the equations can be integrated both explicitly and implicitly. The results of computer simulations are presented in graphical form, analysed, and discussed.

The problem of knowing future phenomena

The paper is devoted to the problem of epistemic conditions of cognition of future phenomena.The problem is that the cognizable aspects of information are based on the evolutionary mechanisms of adaptation of the body, which are reduced in humans, and on the rational principles of human cognition, which are mixed with irrational prophecies. The solution is to separate the heuristic possibilities of theory from empirical guesses and consider the structures of theoretical knowledge that process information about possible and impossible facts.The aim of the paper is to discover a mental act directed at the future, which cannot be reduced to extrapolating the past and varying alternatives. The conditions of the possibility of knowing about the reality of the future as a mode of time, and the idea of a specific event in the future are split.The following hypothesis is considered: cognition of the future is based on thinking about the possible and necessary relations of an object to an integral system including a subject.The attitude of the mind towards the future is not a cognitive act like receiving information, the source of which is problematic. This is not animal foresight, not divination, not planning. Cognition is understood not as a perception of the objective properties of a phenomenon, but as its construction according to the rules of the organization of consciousness. In general, the future is not knowable, but there are types of phenomena other than the causal concept of cognition that justify information about the future. Theoretical knowledge of the predispositions of a situation involving an object is proactive. The subject of prediction is not only the truth about the future, but also a judgment about what exactly will not happen. This limitation narrows the scope of possible alternatives. The elementary acts of realizing that an event is not yet happening have been investigated by the phenomenology of Buddhism. They are directed at the stages of the same process. The conclusion from content of one temporal interval to content of another interval is based on theoretical constructions that do not have spatiotemporal characteristics. Information about the future is limited by theoretical knowledge of the holistic situation and the properties of the object that arise as a result of its inclusion in this situation.