In this paper, we propose examining Heterogeneous Autoregressive (HAR) models using five different estimation techniques and four different estimation horizons to decide which performs better in terms of forecasting accuracy. Several different estimators are used to determine the coefficients of three selected HAR-type models. Furthermore, model lags, calculated using 5 min intraday data from the Standard & Poor’s 500 (SPX) index and the Chicago Board Options Exchange Volatility (VIX) index as the sole exogenous variable, enrich the models. For comparison and evaluation of the experimental results, we use three metrics: Quasi-Likelihood (QLIKE), Mean Absolute Error (MAE), and Mean Squared Error (MSE). An empirical study reveals that the Entropy Loss Function consistently achieves the best QLIKE results in all the horizons, especially in the weekly horizon. On the other hand, the performance of the Robust Linear Model implies that it can provide an alternative to the Entropy Loss Function when considering the results of the MAE and MSE metrics. Moreover, research shows that adding more informative lags, such as Realized Quarticity for the Heterogeneous Autoregressive model yielding the Realized Quarticity (HARQ) model, and incorporating the VIX index further improve the general results of the models. The results of the proposed Entropy Loss Function and Robust Linear Model suggest that they successfully achieve significant forecasting accuracy for HAR models across multiple forecasting horizons.

The object of study is digital-to-analog converter (DAC). The meaning of DAC, their design and control of various types of switches, as well as some logic elements that can act as a switch, as well as the principles of DAC operation based on various series, microcircuits were considered. The resistance of a 4-bit DAC circuit was calculated and, accordingly, the change in the output voltage when applying the corresponding combined input voltage was studied, and a timing diagram was accordingly developed. Using 1-state and toggle physical switches, schematics are established and side effects are learned. Ladder circuit established using R–2R type dividers as voltage dividers. The object of the research is to find and learn the most technologically advantageous concept in order to provide good communication between all equipments by transferring information from digital to analog in the industrial field as well as in other areas where automation is used. One of the biggest challenges in this research is to determine the smallest binary values. Before the use of electronic logic elements such as counters, bistable physical switches were used to create a break or connect state in the circuit, which caused additional energy and time loss or abrasion because of large voltages occur across the switches. The biggest issue was that when manufacturing integrated digital-to-analog converters, producing the right resistors with very different values is really difficult. As a result of this research, it is seen that the use of physical switches is unnecessary as a demand of today's modern technology, and integrated microcircuits are good to replace them. The programming of digital-to-analog converters on programmable hardware devices such as Arduino and Raspberry Pi were mentioned. If to predict the near future, digital-to-analog transmission will be realized only by only using logic elements, and this will change both their energy efficiency and size for the better.

Industrial automation systems are increasingly cyber-physical, interconnected, and software-dependent, which expands both their operational capability and their cybersecurity exposure. This article reports a systematic literature review, conducted following the PRISMA 2020 guidelines, of cybersecurity requirements and certification standards in industrial automation, with emphasis on Industrial Control Systems (ICS), Supervisory Control and Data Acquisition (SCADA), Programmable Logic Controllers (PLCs), and Industry 4.0 contexts. From 3570 records identified across five academic databases, 75 studies were retained after duplicate removal, title and abstract screening, and full-text eligibility assessment. The included studies were analyzed along three dimensions: cybersecurity requirements, standards and certification, and application context. Quantitative synthesis shows that network segmentation, intrusion detection, secure communication, access control, lifecycle security, and safety–security coordination are the six most frequently emphasized requirement categories, and that ISA/IEC 62443, ISO/IEC 27001, NIST SP 800-82, and NERC-CIP are the four dominant certification frameworks. The review identifies four critical gaps between technical cybersecurity requirements and certification practice and proposes an integrated mapping framework linking requirement categories, standards, and application contexts. The findings indicate that effective industrial cybersecurity assurance depends on a layered compliance architecture rather than on dependence on any single framework.