Indoor air quality plays a crucial role in human health and well-being, with relative humidity (RH) being a key factor influencing respiratory health, indoor comfort, and the interior lifespan of buildings. Poor RH control can exacerbate indoor air pollution, leading to adverse health effects and increased risks of microbial growth. This study created a predictive approach to indoor RH management by developing an intelligent electronic system that proactively regulates a humidifier and dehumidifier to maintain optimal humidity levels. The system integrates a forecasting algorithm based on the ARIMA model, enabling short-term RH predictions and dynamic adjustments before extreme conditions occur. The ARIMA model was selected for its robustness in time-series forecasting, ensuring precise predictions and improved indoor climate regulation. The results demonstrate that this predictive control strategy significantly reduces fluctuations in RH, preventing the effects of indoor air pollution associated with humidity extremes while enhancing energy efficiency. Additionally, the iterative validation process confirms the model’s reliability and adaptability to changing environmental conditions. This study suggests the importance of predictive RH control in mitigating the threat of poor indoor air quality, improving indoor comfort, and promoting energy-efficient and sustainable living environments.

The use of certain energy resources is required to provide the necessary comfort in residential premises. The high cost of electricity for domestic purposes, the management of energy flows using power and timing parameters is of particular importance. The energy flow management is related to the specific thermal parameters of the residential home. For this purpose, the specifics of the room and its energy characteristics are taken into account and the process of the microclimate in the room is modelled. The paper presents an adapted model of energy flows in residential premises, based on physical and empirical dependencies, as well as software to simulate its principle of operation. The main goal is to achieve the desired comfort temperature, with reduced electricity consumption.

In this study, we present the development of an advanced air pollution modeling approach, which incorporates cutting-edge stochastic techniques for large-scale simulations of long-range air pollutant transportation. The Unified Danish Eulerian Model (UNI-DEM) serves as a crucial mathematical framework with numerous applications in studies concerning the detrimental effects of heightened air pollution levels. We employ the UNI-DEM model in our research to obtain trustworthy insights into critical questions pertaining to environmental preservation. Our proposed methodology is a highly convergent quasi-Monte Carlo technique that relies on a unique symmetrization lattice rule. By fusing the concepts of special functions and optimal generating vectors, we create a novel algorithm grounded in the component-by-component construction method, which has been recently introduced. This amalgamation yields particularly impressive outcomes for lower-dimensional cases, substantially enhancing the performance of the most advanced existing methods for calculating the Sobol sensitivity indices of the UNI-DEM model. This improvement is vital, as these indices form an essential component of the digital ecosystem for environmental analysis.

The increasing demand for high-speed internet and advanced digital services necessitates the deployment of robust and scalable broadband infrastructure, particularly in smaller urban and rural areas. This paper presents the design and implementation of a passive optical network (PON) based on a gigabit-capable passive optical network (GPON) standard to deliver fiber-to-the-home (FTTH) services in a small-town setting. The proposed solution prioritizes cost-effectiveness, scalability, and minimal energy consumption by leveraging passive splitters and unpowered network elements. We detail the topology planning, splitter architecture, installation practices, and technical specifications that ensure efficient signal distribution and future network expansion. The results demonstrate the successful implementation of an optical access infrastructure that supports high-speed internet, Internet Protocol television (IPTV), and voice services while maintaining flexibility for diverse urban layouts and housing types.

This paper presents a simulation study and development of a prototype of an integrated electronic system, specifically designed to enhance both the safety and the comfort of passengers and drivers in modern vehicles. The proposed system provides intelligent assistance during parking maneuvers by alerting the driver to nearby obstacles, and it also actively monitors the internal environment of the car cabin to detect the presence of harmful gases such as carbon monoxide, thereby improving occupant safety. In order to accomplish these objectives, a detailed functional algorithm was created and a corresponding structural scheme was designed. Simulation studies were carried out using the Tinkercad platform to validate the theoretical model and to test the behavior of the system components under realistic conditions. After a successful simulation, the physical prototype of the system was assembled and tested in a laboratory environment. The core of the system is based on the Arduino UNO microcontroller, which offers flexibility, ease of programming, and integration capabilities with various sensors and actuators. The study demonstrates the potential of low-cost microcontroller-based solutions for intelligent automotive systems focused on active safety and enhanced user experience.