Effects Of Different Scenarios Research Articles

Integrated analysis of air quality-vegetation-health effects of near-future air pollution control strategies

To explore the effects of air pollution control strategies on air quality, vegetation, and health, we conducted an integrated modeling analysis for a representative industrial city, Zibo in the North China Plain, China. Two air pollution control scenarios for the near future (year 2026) are developed, including basic and strict control scenarios. The integrated modeling system based on Weather Research and Forecasting-Community Multiscale Air Quality Modeling (WRF-CMAQ) is utilized to analyze the effects of different scenarios on air quality improvement, vegetation, and health effects. Results indicate that under the basic (strict) control scenarios, the emissions of sulfur dioxide (SO2), nitrogen oxides (NOx), particulate matter (PM2.5), volatile organic compounds (VOCs), ammonia (NH3) will be reduced by 16% (53%), 14% (24%), 16% (38%), 9% (28%), and 12% (12%) respectively, together with a 15% reduction in emissions of NOx, SO2, VOCs, PM2.5, along with a 5% reduction in NH3 emissions in the vicinity of Zibo in the year 2026, could meet the air quality target for 2026 (with PM2.5 and MDA8 O3_90% at 38 and 185 μg/m³, respectively). In terms of crop yield, under the basic (strict) control scenarios, the ozone-induced yield loss for wheat and corn is expected to increase by 30,000 tonnes (decrease by 80,000 tonnes) and 6000 tonnes (decrease by 4000 tonnes), respectively. In the basic control scenario, the number of deaths due to changes in PM2.5 is 1210 (95% Cl: 950, 1472) and the number of deaths due to O3-related changes is 1042 (95% Cl: 780, 1304). In the strict control scenario, the number of deaths due to PM2.5 changes was 1180 (95% Cl: 992, 1366) and the number of deaths due to O3-related changes was 768 (95% Cl: 581, 955). Our results provide a scientific basis for governments to formulate future air pollution prevention strategies.

Research and Implementation of Indoor Positioning Algorithm Based on Bluetooth 5.1 AOA and AOD.

With the addition of Bluetooth AOA/AOD direction-finding capabilities in the Bluetooth 5.1 protocol and the introduction of antenna array technology into the Bluetooth platform to further enhance positioning accuracy, Bluetooth has gradually become a research hotspot in the field of indoor positioning due to its standard protocol specifications, rich application ecosystem, and outstanding advantages such as low power consumption and low cost compared to other indoor positioning technologies. However, current indoor positioning based on Bluetooth AOA/AOD suffers from overly simplistic core algorithm implementations. When facing different application scenarios, the standalone AOA or AOD algorithms exhibit weak applicability, and they also encounter challenges such as poor positioning accuracy, insufficient real-time performance, and significant effects of multipath propagation. These existing problems and deficiencies render Bluetooth lacking an efficient implementation solution for indoor positioning. Therefore, this paper proposes a study on Bluetooth AOA and AOD indoor positioning algorithms. Through an analysis of the principles of Bluetooth's newly added direction-finding functionality and combined with research on array signal DOA estimation algorithms, the paper ultimately integrates the least squares algorithm to optimize positioning errors in terms of accuracy and incorporates an anti-multipath interference algorithm to address the impacts of multipath effects in different scenarios. Experimental testing demonstrates that the indoor positioning algorithms applicable to Bluetooth AOA and AOD can effectively mitigate accuracy errors and overcome multipath effects, exhibiting strong applicability and significant improvements in real-time performance.

A Brief Discussion on the Overall Classification Algorithm of Airborne LiDAR Point Cloud

Abstract. Point cloud classification is the most important problem in airborne LiDAR point cloud data processing. In recent years, classification strategies with new theoretical background keep emerging, so it is urgent to make a more systematic and detailed summary of existing point cloud filtering algorithms, so that relevant researchers can have a more macroscopic and clear understanding of various algorithms and their advantages and disadvantages. Based on the characteristics of airborne LiDAR point cloud data, this paper combs the general process of point cloud classification. This paper summarizes the current mainstream classification methods and analyses their application effects in different scenarios, aiming at exploring and customizing suitable point cloud classification methods according to specific purpose objectives or industry standards. The point cloud classification is classified into three-level classification strategy. The first-level classification is gross error elimination, the second-level classification is point cloud filtering, which is to distinguish ground points from non-ground points. The third-level classification is to extract thematic point clouds from non-ground points according to application requirements. At present, the primary and secondary classification methods are relatively diverse and mature, reaching a certain level of application, while the tertiary classification is still in the initial stage of exploration, and large-scale application is not widespread.

Research on the connectivity of urban river network-wetland and water quality simulation.

The connectivity of urban river networks plays an important role in cities in many aspects, such as urban water safety, water quality (WQ), and aquatic ecological balance. This study focuses on the river network and the Majiawan Wetland in the Chaoyang District of Beijing by establishing a two-dimensional hydrological WQ model employing various water allocation schemes between the river network and the wetland. Water circulation and WQ are the main indexes, and the effects of different scenarios on improving water circulation and WQ are simulated and compared. This study demonstrates that the addition of water replenishment at the intersection of river network and internal slow-water zones of the wetland (Scheme 2) has greater effectiveness in improving both hydrology and WQ compared to two other schemes. The water area of the Majiawan Wetland has expanded, and water velocity has increased. Using chemical oxygen demand, total nitrogen, and total phosphorus as the index values for determining the water class, the WQ of about 20% of the wetland area was reached Water Class II (domestic drinking water), with Water Class III (general industrial water) accounting for the other 80%. This study provides valuable evaluation and reference for similar areas of urban river network connectivity.

An innovative image encryption algorithm enhanced with the Pan-Tompkins Algorithm for optimal security

This study introduces a cutting-edge image encryption algorithm aimed at elevating security standards. The Pan-Tompkins Algorithm (PTA) for key generation is proposed for the first time in this study. Additionally, employing steganography through the Least Significant Bit (LSB) method for embedding keys within the encrypted image enhances secure key distribution, thereby fortifying the encryption process. On the other hand, the integration of advanced algorithms, such as Zigzag scanning, the Affine Image Encryption Algorithm (AA), and the Vigenere Image Encryption Algorithm (VA), constitutes the fundamental innovation of the proposed image encryption algorithm. The proposed algorithm is named PanAAVA:Affine Algorithm and Vigenere Algorithm Encryption with PTA-Based Key Generation. The PanAAVA algorithm ensures unparalleled security by encrypting the positions and values of pixels using AA and VA. Notably, using PTA for key generation marks a distinctive and new key generation method feature of the algorithm. To assess the effectiveness of the PanAAVA, a comprehensive comparative analysis is conducted against well-established encryption methodologies, including Lena, Baboon, Airplane, and Pepper.The PanAAVA demonstrates exceptional proficiency in histogram analysis. The PanAAVA demonstrates a Unified Average Changing Intensity (UACI) of 33.4044%. Additionally, the Number of Pixels Change Rate (NPCR) is measured at 99.7442%, showcasing the algorithm’s effectiveness in inducing significant pixel changes. The proposed algorithm’s Mean Square Error (MSE) is calculated at 3.20679E5%. The proposed algorithm’s Peak Signal to Noise Ratio (PSNR) is recorded at 9.512475. The Key Space Size of the proposed algorithm is measured at 2209. Regarding correlation analysis, the PanAAVA achieves a high correlation score of 7.9996. The proposed algorithm successfully passes the National Institute of Standards and Technology (NIST) analysis, demonstrating a remarkably strong correlation close to 0 and a Structural Similarity Index Measure (SSIM) of 0.9977. Furthermore, regarding quantum communication, the proposed algorithm maintains stable key rates of 47.5 ± 0.8 kHz during the day and 50.9 ± 0.7 kHz at night. Additionally, PanAAVA achieves low Quantum Bit Error Rate (QBER) values of 4.77 ± 0.02, ensuring reliable and secure communication. The PanAAVA also demonstrates robust asymmetries at 49.81 ± 0.02 and 50.14 ± 0.03 for a crystal length of 20 mm. highlighting PanAAVA’s adaptability and effectiveness in different scenarios. PanAAVA outperforms other encryption algorithms concerning performance measurements and comparisons. In conclusion, the PanAAVA emerges as a beacon of superior security capabilities and innovation in image encryption, showcasing the potential to redefine standards in the field.

Impact of single and combined local air pollution mitigation measures in an urban environment

Urban air pollution is one of the most important environmental problems for human health and several strategies have been developed for its mitigation. The objective of this study is to assess the impact of single and combined mitigation measures on concentrations of air pollutants emitted by traffic at pedestrian level in the same urban environment. The effectiveness of different scenarios of green infrastructure (GI), the implementation of photocatalytic materials and traffic low emission zones (LEZ) are investigated, as well as several combinations of LEZ and GI. A wide set of scenarios is simulated through Computational Fluid Dynamics (CFD) modelling for two different wind directions (perpendicular (0°) and 45° wind directions). Wind flow for the BASE scenario without any measure implemented was previously evaluated using wind-tunnel measurements. Air pollutant concentrations for this scenario are compared with the results obtained from the different mitigation scenarios. Reduction of traffic emissions through LEZ is found to be the most effective single measure to improve local air quality. However, GI enhances the effects of LEZ, which makes the combination of LEZ + GI a very effective measure. The effectiveness of this combination depends on the GI layout, the intensity of emission reduction in the LEZ and the traffic diversion in streets surrounding the LEZ. These findings, in line with previous literature, suggest that the implementation of GI may increase air pollutant concentrations at pedestrian level for some cases. However, this study highlights that this negative effect on air quality can turn into positive when used in combination with reductions of local traffic emissions.

Implementing a digital twin for optimized real-time control of Gothenburg's regional sewage system

Abstract The emerging challenges facing the City of Gothenburg and surrounding municipalities include the impacts of a variable climate, rapid growth and urbanization, aging infrastructure and changing community values and customer expectations. A digital twin (DT), based on the Future City Flow (FCF) platform, was created for the catchment to address many of these challenges in terms of improved real-time sewerage performance and predictability in the face of storms, improved DT visualization of the regional sewage transport system for operators and greater responsiveness to avoid service interruption. The FCF-based DT provides a sound base for modelling, simulating, forecasting and controlling the catchment. The versatility of the FCF DT makes it useful for gaining insight into catchment dynamics and the simulator can be used to study the effects of different scenarios or changes in the catchment. The predictive control imbedded in the FCF DT recommends setpoints for controlling the catchment that can be used by the operators for decision support or even be directly applied in the control system as model-based predictive control. A DT requires good quality online data from the catchment and, perhaps most importantly, high-quality rain forecasts, especially when prognosing long time horizons.

Supply chain behaviour under carbon regulations: an experimental study with system dynamic simulation

Carbon tax and emission trading policies have been introduced under the Kyoto Protocol to reduce greenhouse gas emissions. This paper examines the effect of these policies on supply chain behaviour to determine an optimal strategy for improving environmental performance via system dynamics simulation. For this purpose, a supply chain is considered under the carbon tax policy, where a supplier and a producer have both green and non-green methods for transportation and production. About the carbon tax, the question is what carbon price encourages the supplier and producer to use the green methods, and how each will behave at different carbon prices. Furthermore, to analyse emission trading policy, several supply chains are considered simultaneously and a method is developed to determine the carbon trading price in the market to maximise supply chains’ profit. The supply chains’ behaviour is simulated based on system dynamics and several experiments were conducted to analyse the effect of different scenarios of carbon regulations on the supply chains. Findings emphasise the need for tailored policies, challenging the notion of a one-size-fits-all approach to emission trading. This research provides valuable insights into the dynamic interplay between environmental sustainability and economic considerations in the context of evolving carbon policies.

Exploring Leadership Styles to Foster Sustainability in Construction Projects: A Systematic Literature Review

Background: The construction sector has faced significant challenges in addressing the world’s pressing crises, such as climate change, poverty, and human well-being. Ensuring sustainability in construction projects is pivotal, and leadership plays a crucial role in embedding sustainable practices. Objectives: This research aims to identify an effective leadership style for implementing sustainable practices in construction projects through a systematic literature review (SLR). Methods: A comprehensive SLR was conducted, focusing on leadership in sustainable construction. The review process included identifying, screening, and analysing relevant literature. Thirty-one articles were meticulously selected and evaluated to understand the relationship between leadership styles and sustainable practices in construction. Results: The findings suggest that no single leadership style is universally applicable in all contexts within sustainable construction. However, transformational leadership stands out for its benefits in facilitating sustainable practices. The review provides insights into various leadership approaches and their effectiveness in different scenarios. Conclusions: The study concludes that while a universal leadership style for sustainable construction does not exist, transformational leadership is generally more effective. This finding is significant for construction project managers aiming to implement sustainable practices, providing them guidance on the leadership styles that are most likely to yield successful outcomes in their projects.

NODE-ImgNet: A PDE-informed effective and robust model for image denoising

Inspired by the traditional partial differential equation (PDE) approach for image denoising, we propose a novel neural network architecture, referred as NODE-ImgNet, that combines neural ordinary differential equations (NODEs) with convolutional neural network (CNN) blocks. NODE-ImgNet is intrinsically a PDE model, where the dynamic system is learned implicitly without the explicit specification of the PDE. This naturally circumvents the typical issues associated with introducing artifacts during the learning process. By invoking such a NODE structure, which can also be viewed as a continuous variant of a residual network (ResNet) and inherits its advantage in image denoising, our model achieves enhanced accuracy and parameter efficiency. In particular, our model exhibits consistent effectiveness in different scenarios, including denoising gray and color images perturbed by Gaussian noise, as well as real-noisy images, and demonstrates superiority in learning from small image datasets.

Study on video key frame extraction in different scenes based on optical flow

Key frame extraction is an important component of video analysis, and has gradually become a research hotspot in the computer vision community in recent years. Early key frame extraction algorithms were mostly based on fixed time intervals, and their accuracy could not meet practical application requirements. Thanks to the rapid development of machine learning technology, key frame extraction algorithms based on image quality, motion analysis, and deep learning are gradually becoming mature. Although the above methods significantly improve the accuracy of key frame extraction, few work pay attention to the extraction effect in different scenarios. In this article, selecting the key frame extraction algorithm based on optical flow method, we compared the key frame extraction algorithm based on optical flow method in detail for different video scenarios. Extensive experimental results demonstrate the robuteness of key frame extraction algorithm based on optical flow.

Simulation-supported development for cooperative Multi-UAV Systems with the Mysterio framework

Over the years, UAVs (also known as drones) have been growing in studies and applications to solve diverse problems. Due to the complexity of these problems, dealing with just one UAV may not be enough, but using several UAVs together to work cooperatively increases its capacities, thus boosting innovative solutions. However, developing cooperative Multi-UAV systems is not trivial, and reuse support is usually limited to low-level implementation. This work presents a framework for Multi-UAVs, called Mysterio, which provides an underlying software architecture with essential Multi-UAV components, enabling the reuse of design and code so that engineers can instantiate it to carry out specific missions by making UAVs work in cooperation. We also present four instances of the framework to evaluate Mysterio’s effectiveness in different scenarios. Finally, we discuss the framework’s potential to provide and support design and code reuse to develop Cooperative Multi-UAVs systems for different application scenarios. The results showed the potential to develop multi-UAV systems using the proposed framework. Additionally, we extend our previous work bringing conceptual evolution and advances in the architecture and the framework. Finally, this evolution extends the framework API to support computer simulations of UAV systems based on the OMNeT++ simulator. This API is suitable for Single-UAV and Multi-UAV systems and has already been adapted to communicate with base stations implemented through the Mysterio Framework.

Performance assessment for the integrated green-gray-blue infrastructure under extreme rainfall scenarios

Synergistic allocation of urban stormwater infrastructure is critical to flood risk prevention and control under extreme rainfall events. This study focuses on the interaction regularity of green, gray, and blue infrastructure in the process of runoff retention and discharge under extreme rainfall scenarios. Three strategies, namely, gray infrastructure, green-gray infrastructure, and green-gray-blue infrastructure, are proposed to analyze the effectiveness of different scenarios on flood risk control capabilities of urban drainage systems and to determine the key influencing factors of the three strategies. The results show that green-gray-blue infrastructure demonstrates synergy in improving the efficiency of urban drainage systems. Under gravity drainage conditions, the backwater jacking caused by high river water levels has a certain impact on the smooth discharge of stormwater runoff in urban drainage systems. This case study identified a sensitive range in the influence of water level on runoff control and drainage function and an adaptive range in which the flood increases slightly with the water level increase. These present notable rules in improving the synergistic effect of blue infrastructure in the integrated urban green-gray-blue infrastructure system.

Machine Learning Classifier Evaluation for Different Input Combinations: A Case Study with Landsat 9 and Sentinel-2 Data

High-resolution multispectral remote sensing images offer valuable information about various land features, providing essential details and spatially accurate representations. In the complex urban environment, classification accuracy is not often adequate using the complete original multispectral bands for practical applications. To improve the classification accuracy of multispectral images, band reduction techniques are used, which can be categorized into feature extraction and feature selection techniques. The present study examined the use of multispectral satellite bands, spectral indices (including Normalized Difference Built-up Index, Normalized Difference Vegetation Index, and Normalized Difference Water Index) for feature extraction, and the principal component analysis technique for feature selection. These methods were analyzed both independently and in combination for the classification of multiple land use and land cover features. The classification was performed for Landsat 9 and Sentinel-2 satellite images in Delhi, India, using six machine learning techniques: Classification and Regression Tree, Minimum Distance, Naive Bayes, Random Forest, Gradient Tree Boosting, and Support Vector Machine on Google Earth Engine platform. The performance of the classifiers was evaluated quantitatively and qualitatively to analyze the classification results with whole image (comprehensive feature) and small subset (targeted feature). The RF and GTB classifiers were found to outperform all others in the quantitative analysis of all input combinations for both Landsat 9 and Sentinel-2 datasets. RF achieved a classification total accuracy of 96.19% for Landsat and 96.95% for Sentinel-2, whereas GTB achieved 91.62% for Landsat and 92.89% for Sentinel-2 in all band combinations. Furthermore, the RF classifier achieved the highest F1 score of 0.97 in both the Landsat and Sentinel datasets. The qualitative analysis revealed that the PCA bands were particularly useful to classifiers in distinguishing even the slightest differences among the feature class. The findings contribute to the understanding of feature extraction and selection techniques for land use and land cover classification, offering insights into their effectiveness in different scenarios.

A New Scenario-Based Simulation Model for Cost Management of Healthcare Services through Improving the Efficiency of the Health Centers

There is a need for an appropriate decision-making approach and a precise costing method in order to make appropriate decisions that result in managing the resource utilization, improving the efficiency of healthcare systems and processes, increasing the patient consent, and reducing the costs of healthcare services. Therefore, this study is to develop a new method for estimating and reducing the services’ costs by assessing the scenarios intended for the improvement of departments using time-driven activity-based costing (TDABC) and simulation model with conditions of uncertainty. To investigate the application of this method, the lithotripsy department of a kidney center has been studied and attempted to investigate the effects of different scenarios of scheduling the technicians’ presence and scheduling patients’ appointments, alongside the effects of controlling the factors of patient cancellation in different stages of the treatment process on lowering the healthcare costs and waiting time and on improving the hospital efficiency. The results showed that significant improvements in costs, waiting time, human resource utilization, and overall system efficiency were emerged. The impact of patient appointment scheduling scenarios on the abovementioned indexes was found to be far more than other scenarios. Furthermore, the results from the costs determined indicate a difference of 0.60, 0.61, 0.67, 0.67, and 0.63 percent between these costs and the costs determined by the traditional approaches. Such differences among the costs determined by these two approaches mainly occur due to different methods of allocating indirect costs and existence of uncertainty in the costs and time of therapeutic activities.

The impact of mortality underreporting on the association of ambient temperature and PM10 with mortality risk in time series study

Properly analyzing and reporting data remains a challenging task in epidemiologic research, as underreporting of data is often overlooked. The evaluation on the effect of underreporting remains understudied. In this study, we examined the effect of different scenarios of mortality underreporting on the relationship between PM10, temperature, and mortality. Mortality data, PM10, and temperature data in seven cities were obtained from Provincial Center for Disease Control and Prevention (CDC), China Meteorological Data Sharing Service System, and China National Environmental Monitoring Center, respectively. A time-series design with a distributed lag nonlinear model (DLNM) was used to examine the effects of five mortality underreporting scenarios: 1) Random underreporting of mortality; 2) Underreporting is monotonically increasing (MI) or monotonically decreasing (MD); 3) Underreporting due to holiday and weekends; 4) Underreporting occurs before the 20th day of each month, and these underreporting will be added after the 20th day of the month; and 5) Underreporting due to holiday, weekends, MI, and MD. We observed that underreporting at random (UAR) scenario had little effect on the association between PM10, temperature, and daily mortality. However, other four underreporting not at random (UNAR) scenarios mentioned above had varying degrees of influence on the association between PM10, temperature, and daily mortality. Additionally, in addition to imputation under UAR, the variation of minimum mortality temperature (MMT) and attributable fraction (AF) of mortality attributed to temperature in the same imputation scenarios is inconsistent in different cities. Finally, we observed that the pooled excess risk (ER) below MMT was negatively associated with mortality and the pooled ER above MMT was positively associated with mortality. This study showed that UNAR impacted the association between PM10, temperature, and mortality, and potential underreporting should be dealt with before analyzing data to avoid drawing invalid conclusions.

Developing an Integrated Energy Demand-Supply Modeling Framework for Scenario Analysis of the Low Carbon Emission Energy System in Zambia

This study develops an integrated modeling approach to analyze the effects of different scenarios compared to a baseline (2019) scenario on energy demand and supply in Zambia. Aiming at minimizing costs, the model combines available resources and technologies, adhering to techno-economic and environmental limits. On the demand side, the study uses time series analysis to forecast future electricity demand in Zambia. The supply-side energy model identifies the optimal combination of resources and technologies needed to satisfy exogenously specified electricity demand levels at the least cost. The baseline scenario results revealed that the power outages in Zambia, especially at peak times, have been attributed to reliance on imported fuel for power generation, the slow-paced integration of renewable resources, and not fully utilizing resource potential for the supply side. To overcome the power outages and meet the electricity demand in 2035, a comprehensive scenario analysis was conducted, including the main scenarios of 30% integration of renewable energy, no coal power generation, and low emission targets (10%, 20%, 30%, 40%, and 50% reduction from the baseline). The expected CO2 reductions from the renewable energy scenario, no coal scenario, and 50% low emission target are estimated at 5222 kilotons in 2035, respectively.

MS-YOLOv7:YOLOv7 Based on Multi-Scale for Object Detection on UAV Aerial Photography

A multi-scale UAV aerial image object detection model MS-YOLOv7 based on YOLOv7 was proposed to address the issues of a large number of objects and a high proportion of small objects that commonly exist in the Unmanned Aerial Vehicle (UAV) aerial image. The new network is developed with a multiple detection head and a CBAM convolutional attention module to extract features at different scales. To solve the problem of high-density object detection, a YOLOv7 network architecture combined with the Swin Transformer units is proposed, and a new pyramidal pooling module, SPPFS is incorporated into the network. Finally, we incorporate the SoftNMS and the Mish activation function to improve the network’s ability to identify overlapping and occlusion objects. Various experiments on the open-source dataset VisDrone2019 reveal that our new model brings a significant performance boost compared to other state-of-the-art (SOTA) models. Compared with the YOLOv7 object detection algorithm of the baseline network, the mAP0.5 of MS-YOLOv7 increased by 6.0%, the mAP0.95 increased by 4.9%. Ablation experiments show that the designed modules can improve detection accuracy and visually display the detection effect in different scenarios. This experiment demonstrates the applicability of the MS-YOLOv7 for UAV aerial photograph object detection.

Security testing of modern information systems: selection of effective methods and scenarios for various testing objects

Modern challenges in the field of cyber security require a deep understanding and effective application of various types of security testing. Security testing, as one of the main elements of the cyber security strategy, requires a systematic approach and the selection of optimal test scenarios according to the specific needs and features of the organization's IT infrastructure. With the increasing diversity of cyber threats and attack techniques, it is important to consider and compare different types of security testing to determine their effectiveness in different scenarios. The purpose of the article is to develop recommendations for cyber security specialists regarding the selection of effective methods for assessing the level of protection, by using various scenarios for testing the security of IT infrastructure. The article analyzes and classifies various types of security testing in order to select the most effective methods and scenarios for various systems. A selection matrix has been developed, which helps to systematize the process of selecting test methods and scenarios according to specific test objects. Each object has its own characteristics and forms the corresponding testing requirements, and the choice of methods must be adapted to these unique aspects to ensure the maximum effectiveness of security measures. Application of appropriate methods, such as vulnerability scanning, pentest, vulnerability analysis, Red teaming and social engineering testing, in combination with specific scenarios, allows you to effectively identify and eliminate flaws and vulnerabilities of the IT infrastructure and its components. On the basis of the performed analysis, practical recommendations are provided for choosing optimal strategies for specific testing scenarios. The article is designed for cybersecurity professionals, researchers, and organizations seeking to improve the effectiveness of their security testing programs and ensure a high level of protection in the face of countless cyber threats.

FEATURES OF PREDICTING FAILURES IN A SMART HOME BASED ON MACHINE LEARNING METHODS

Modern smart homes based on the Internet of Things (IoT) offer users a wide range of automated conveniences, but at the same time face potential problems and system failures. This article examines the potential for using machine learning techniques to predict and manage these failures in order to ensure greater reliability and efficiency of the smart home. The article discusses current trends in the development of smart homes, as well as practical problems associated with system failures. The authors analyse various aspects of failures, from network incidents to device failures, taking into account the variety of sensors and detectors used in smart systems. The article discusses the main machine learning methods, such as neural networks, decision trees, and classification, and their application to predict failures in smart homes. The advantages and limitations of each method are analysed and their effectiveness in different scenarios is considered. The reference architecture of the IoT platform and the fault prediction platform are presented. The algorithm of the platform is described in detail. The fault prediction platform assumes the existence of an IoT platform. In particular, it assumes the existence of two data sources, one for operational and the other for historical and static data. The process of data processing is described. The authors of the article provide recommendations on the optimal approaches to predicting failures in smart homes and identify possible areas for further research in this area. It is highlighted that accurate failure prediction can significantly increase the reliability and usability of smart home systems, which is a key aspect for their popularity and market acceptance. Effective prediction of failures in a smart home can improve the safety, energy efficiency, and convenience of users' lives.