Systems Research Research Articles

The intersection of socioeconomic status and wildfire risk: Insights from California

Wildfires in California have increased over the past 20 years, raising serious concerns. Due to the significant damage these fires cause to the environment, infrastructure, and communities, it is essential for policymakers and researchers to find effective ways to reduce their impact. This study aims to improve our understanding of wildfires by examining the relationship between population density, wildfire severity, and socioeconomic factors like average household income. The research uses socioeconomic data from the California State Geoportal (CA.gov) and fire severity zones from ArcGIS Hub. Statistical methods, such as ordinary least squares (OLS) regression and correlation analysis, were applied using ArcGIS Pro software from the Environmental Systems Research Institute. This study confirms earlier findings that there is no correlation between population density and wildfire severity. This conclusion is based on a more detailed analysis that moves from the county level to the census tract level. Additionally, we found no connection between socioeconomic status and fire severity, though we did observe a clear link between socioeconomic status and the likelihood of fires starting. Both OLS and correlation analysis supported these results. However, a hot-spot analysis showed that areas with the lowest-income households are also the regions with the most severe wildfires. This suggests a concerning relationship between lower-income communities and increased vulnerability to wildfires. The findings emphasize the need for targeted interventions and a fair distribution of resources to address the socioeconomic inequalities that contribute to wildfire risk.

Infrastructures of power: Governance by energy systems and infrastructure

This paper suggests a synthetization of three classic approaches to analyze infrastructures as interconnected compositions that subtly distribute power and governance. We draw upon conceptualizations within large technological systems (LTS), Internet governance, and information infrastructures (II), and connect them to our own extensive research in energy infrastructure and systems spanning over a decade. This combination serves as the bedrock for our methodological work, which we establish here as pivotal signposts for the study of governance by infrastructures. Rooted empirically in energy research, we advocate for scholars to expand these methodologies to other infrastructures, such as the Internet and II, to illustrate how governance operates across diverse infrastructural networks and their intersections. Our empirical examples draw from studies in smart grids and smart energy technologies, electricity supply management and markets, and energy computer modeling. These diverse cases are utilized to inform the proposed methodological approach.

A Fault-Tolerant Multi-Agent Reinforcement Learning Framework for Unmanned Aerial Vehicles–Unmanned Ground Vehicle Coverage Path Planning

In this paper, we introduce a fault-tolerant multi-agent reinforcement learning framework called SERT-DQN to optimize the operations of UAVs with UGV central control in coverage path planning missions. Our approach leverages dual learning systems that combine individual agent autonomy with centralized strategic planning, thus enhancing the efficiency of cooperative path planning missions. This framework is designed for high performance in environments with fault uncertainty detected and operational challenges such as interruptions in connectivity and compromised sensor reliability. With the integration of an innovative communication system between agents, our system appropriately handles both static and dynamic environments. Also, we introduce similarity-based shared experience replay to attain faster convergence and sample efficiency in the multi-agent system. The architecture is specially designed to respond adaptively to such irregularities by effectively showing enhanced resilience in scenarios where data integrity is impaired due to faults or the UAV faces disruptions. Simulation results indicate that our fault tolerance algorithms are very resilient and do indeed improve mission outcomes, especially under dynamic and highly uncertain operating conditions. This approach becomes critical for the most recent sensor-based research in autonomous systems.

Morphological analysis of system studies of automatic space applications in a synergetically disturbing environment

The article considers the use of a new methodological cognitive-variable synergetic (CVS) approach to the analysis of the problems of the theory of systems research conducted on board automated spacecraft (AS) [1]. The methodological basis of the CVS approach is based on cybernetic [2]and synergetic [3] theories of dynamic systems management, as well as the methodology of proactive management of the functioning of complex technical objects (STO), based on the concept of integrated modeling of STO [2]. The results of the previously conducted morphological analysis of the problems of the theory of systemic cybernetic studies of the movement of dynamic systems are known, presented in the form of a morphological tree containing four branches (modeling-analysis-observation-choice) and leaves (particular tasks) [4]. The number of tasks is determined by the interaction of the control system (environment) with four types of effects of the disturbing environment – deterministic, stochastic, purposeful and with unknown characteristics - in the formation of input effects on the control system. The CVS approach is based on the use of a new type of environment in the management of dynamic systems – synergetically disturbing. To display the new environment and tasks on the morphological tree of problem analysis, in addition to the existing tasks of systemic cybernetic research, new leaves are introduced - particular tasks of systemic synergetic research. The paradigm of problem analysis is formed on the basis of knowledge, management experience and system research aimed at solving the problem of providing resources for on-board AS systems and the stability of their functioning during flight. The goal–setting of a new methodological approach to the analysis of the problems of the theory of systems research is aimed at the rational use of the main structural and functional resources of the BS, obtaining synergetic resources and preventing failures caused by calculated and non-calculated abnormal processes. Further, synergetic will be called such a type of resources that is formed by spontaneous self-organization of processes in management systems and transformed into purposeful self-organization in objectively existing multi-place relationships describing synergetic interactions and interrelations during the movement of processes in a synergetically disturbing external environment [5, 6].

УПРАВЛЕНИЕ НАДЕЖНОСТЬЮ И РИСКОМ В ХОЗЯЙСТВЕ АВТОМАТИКИ И ТЕЛЕМЕХАНИКИ

the article is devoted to the research of dependability and risk management systems at JSCo “Russian Railways” taking into account the specifics in signaling and interlocking division. On this topic the author reviews the current regulatory framework (domestic and foreign), addressing to the problems of its harmonization and proper adaptation within the company. The state of affairs in automation of dependability and risk management processes is considered, which is implemented in several corporate automated systems: “AS URRAN”, “AS ANSh”, “EKP URRAN-Sh”, “AS ANPSh”, “EK ASUI FA”, “AS FA”. A comparative analysis of these systems made it possible to determine the main directions for their improvement in order to maintain the authenticity, relevance and importance of the information contained in them. Despite many ways to model events that may lead to a violation of the transportation process safety, the article discusses one of them: the creation of the tree structure of probable events that have a sequential connection with each other. The author believes that the application of this method in practice will allow to establish typical trends in the occurrence of such cases. It will have a positive impact on the quality of predictive analytics in the company and will improve the existing risk-based approach. In addition, a rethought way to determine and visually present the level of train delays risk caused by failures in signaling and interlocking division is proposed. The central place here is occupied by heat maps of risk and its components.

Visibility in the Italian Art World: How Far Along We Are

Even though the concept of “nationality” when we talk about an internationally acclaimed artists is more and more blurred and has little to do with the importance of the artworks, it is evident how small the Italian art presence in great international art manifestations is, sometimes almost negligible. It is then necessary to understand the causes of this situation, to draw a map of the current state of the contemporary Italian scene and state some resolutions from it. The first 2022 Report “How (well) known is Italian art abroad?” by BBS-Lombard Benefit Corporation, together with Arte Generali has investigated how much Italian contemporary artists are (in)visible in the international art market, laying the groundwork for a much more in-depth research of the art system, with the ultimate purpose of giving new solutions to overcome this situation, wandering whether Italian art has a business model with (still) some gray areas - unlike almost all other product sectors – and how building an economy to bridge the gap, having a market strategy and being attractive in the international positioning can be a strategic move. The study was conducted carrying out both qualitative and quantitative analysis throughout more than 20 interviews to Italian curators internationally known that have helped us understand causes, strengths and weaknesses of the Italian art system and a big data collection about the presence of Italian artist in international museums, art manifestations, biennials, such as Biennale di Venezia and documenta, international art galleries and auctions. The global media coverage of Italian artists was analyzed with the help of Artiker and through the artificial intelligence of Wondeur AI, partner of Arte Generali, it was possible to analyze the relationship between art development and cities, considering the city’s overall success rate (percentages of artists showing growth in the city) and the city’s risk propensity (ability to attract artists). The instrument of the Italian Council was also studied, the main MIC project (Italian Ministry for Culture) in support of Italian art and the initiatives of Italian cultural institutes abroad. From the overall analysis some relevant names have emerged: Francesco Vezzoli, Monica Bonvicini, Enrico David, Paola Pivi, Tatiana Trouvè, Roberto Cuoghi, Rosa Barba, Massimiliano Cattelan (above all) and a few others, all united by a time of study or work abroad which allowed them to create valuable relationships with curators, galleries and museums abroad.

Application of artificial neural network for peak load forecasting in 150 kV Semarang power system

Accurate load forecasting is essential for reliable and efficient operation of power systems. Traditional forecasting methods often struggle with capturing complex nonlinear patterns in load data. Artificial neural networks (ANNs) have emerged as a promising alternative due to their ability to learn complex relationships from historical data (Syed et al. in IEEEA 9:54992–55008, 2021. https://doi.org/10.1109/ACCESS.2021.3071654). This study investigates the potential of ANNs for short-term peak load forecasting in a 150 kV power system in Semarang, Indonesia. The study examines the impact of different input variables, including historical peak load, minimum load, population, and energy production, on forecasting accuracy. Several ANN architectures are trained and evaluated using mean absolute percentage error (MAPE) and mean squared error (MSE) metrics as reported by Demuth and De Jesús (neural network design). The results indicate that ANNs can achieve high accuracy in predicting peak load, with MAPE values below 10%. The study also demonstrates the importance of carefully selecting input variables and training parameters for optimal model performance. The findings highlight the potential of ANNs for improving load forecasting accuracy in power systems, contributing to enhanced grid reliability and operational efficiency. The findings of this study contribute to a deeper understanding of the application of ANNs in power system load forecasting. They demonstrate the potential of ANNs to achieve high accuracy and provide valuable insights into the factors influencing model performance. The findings are relevant for power system operators, researchers, and policymakers working to improve grid reliability and efficiency as reported by Prabha Kundur and Malik (Power System Stability and Control, McGraw-Hill Education, New York, 2022. https://www.accessengineeringlibrary.com/content/book/9781260473544).

Sports Results Prediction Model Using Machine Learning

Within the past few decades, more and more people use Machine learning technology to predict sports performance. This paper aims to research the possibility of sports results prediction with good precision. Machine learning methods are often used to create model that will produce a representative output of the probable match result. This can assist coaches and managers in evaluating player performance, identifying skills, predicting injuries, and evaluating game strategy. Thanks to the good accuracy of the forecast, one of the constantly evolving areas is sports betting. Finally, considering the current research review, a framework is proposed to develop a system based on artificial intelligence to predict the outcome of a football match. This research provides information on how system learning algorithms have proven to be useful and how they can produce success. This research also reviews the research of data mining systems to predict athletic performance and evaluate the strengths and weaknesses of each system.

Enhancing Cooperative Lane-Changing for Intelligent Connected Vehicles: A DDPG-Based Approach

Current research on lane-changing for intelligent connected vehicles (ICVs) primarily focuses on the control of a single ICV. Even studies on cooperative lane-changing among multiple ICVs often use fixed control algorithms, which are difficult to adapt to constantly changing traffic conditions. This paper proposes a method based on the deep deterministic policy gradient (DDPG) model. The DDPG algorithm has strong generalization capabilities, enabling it to handle complex traffic scenarios. Combined with the design of innovative reward functions, it successfully achieves the control of cooperative lane-changing for multiple ICVs. The novelty of this model is mainly reflected in the implementation of multiple ICVs control and cooperative strategies through the design of a speed reward function. Specifically, when two or more ICVs come within a 15-m range, a collaborative strategy is implemented. In this strategy, the original speeds of the vehicles involved are replaced with their average speed. This average speed is then used to compute the velocity reward. Moreover, an additional headway reward is introduced when two vehicles are on the same lane with a distance of less than 10[Formula: see text]m between them, further optimizing lane-changing decisions and enhancing driving efficiency. The innovative approach described in this paper has been validated through simulations in the highway-env simulation environment. The model’s effectiveness was tested under various conditions, including different road densities and numbers of ICVs. Results indicate that with an increasing number of episodes, the model consistently enhances both the fleet’s and each individual vehicle’s average speed and steps. It also ensures that the headway is maintained within a desired range of 10–20[Formula: see text]m. This paper showcases the potential applications of a new DDPG-based multi-networked vehicular cooperative lane-changing decision model across different scenarios. These findings provide important reference values for future research and development in intelligent transportation systems (ITS), particularly in optimizing vehicle cooperative behaviors and enhancing traffic efficiency. This paper emphasizes the model’s potential to significantly impact the management and operation of future vehicular networks, paving the way for more sophisticated and efficient transportation systems.

Bridging borders: Current trends and future directions in comparative health systems research.

Bridging borders: Current trends and future directions in comparative health systems research.

Research on the “1+1+1” Physical Intelligence Curriculum System under the Background of Healthy Children Action Plan

“14th Five-Year Plan” period is the first five years to the second century goal, but also the key period for the development of children’s health cause. “Healthy China 2030” plan outline “clearly put forward the implementation of healthy children plan [1]. Although the national level attaches great importance to children’s health, there are still many factors that affect children’s healthy growth in daily life, such as various diseases in children’s physical health, developmental delays in various physiological functions, insufficient exercise ability, obesity, etc., as well as mental health problems such as stuttering, night terrors, fear, anxiety, rage, lying, autism and hyperactivity. To solve these problems, not only the attention and deployment of the country and the government, but also the understanding and cooperation at the social level and the family level, in order to better promote the healthy growth of children under the joint efforts of everyone. The “1+1+1” “Body intelligence” curriculum system research includes a healthy child’s physical development system, a healthy child’s psychological (mental) development system, and a healthy child’s ability development system. Aiming at the problems of physical and mental development and ability development in the healthy growth of children, service programs are scientifically and rationally formulated, and implemented from the social level and family level to effectively improve children’s health level [2].

Mechanism design of EVs fast charging rights for enhanced vehicle-to-grid regulation

With the advancement of policy initiatives and technological developments, vehicle-to-grid (V2G) interactions have emerged as a critical focus within power system research. Despite numerous current studies on V2G, the exploration of incentive mechanisms to encourage user participation in these systems remains comparatively underdeveloped. This study proposes an innovative demand response (DR) incentive mechanism for electric vehicles (EVs) grounded in the regulation of fast charging powers via strategically assigning and allocating virtual points defined as fast charging right (FCR). The primary objective of this mechanism is to meet the power regulation requirements of DR while simultaneously balancing the grid’s response costs and accommodating the charging demands of EV users. Through simulation and comparative analysis with the existing pilot DR incentive mechanisms, the results demonstrate that the proposed mechanism outperforms the current model in three critical aspects: power regulation efficacy, cost-effectiveness, and user experience. In addition, this paper investigates the performance of the incentive mechanisms in both online and offline scheduling environments. The findings reveal that the FCR mechanism maintains robust power regulation performance even under online scheduling conditions.

Comprehensive groundwater potential mapping in Nagaland, India using integrated geo-spatial techniques

Groundwater is essential for a sustainable future, yet identifying potential groundwater zones remains challenging in regions with varied hydro-geological conditions. Nagaland is a state in north-eastern India characterized by diverse terrain, complex geological formations, and seasonal variations in rainfall. The objective of the present study is to evaluate groundwater potential zones in Nagaland using an integrated Geographic Information System (GIS) and Analytical Hierarchy Process (AHP) methodology, resulting in the first comprehensive map addressing the region's diverse hydro-geological conditions. Several attributes like geomorphology, geology, slope, soil, rainfall, drainage density, and Land use/Land cover (LU/LC) were generated from available data resources like Landsat 8 and Sentinel-2 satellite imageries, the Geological Survey of India (GSI), Environmental Systems Research Institute (ESRI), the Food and Agriculture Organization (FAO), and the Climatic Research Unit (CRU). These datasets are extracted using the Arc-GIS (Arc-GIS Desktop 10.8.1), with weights assigned according to Saaty's scale, and subsequently normalized through the AHP. The findings reveal that 14% (2240km2) of the state has very good groundwater potential, 42% (6720 km2) is classified as good, 9% (1440 km2) as moderate, 27% (4320 km2) as poor, and 8% (1280 km2) as very poor. These insights are significant for regions like Mokokchung, Longleng, and Wokha, which require urgent intervention. Conversely, the study identifies Dimapur and Chumoukedima as areas with significant groundwater potential, capable of meeting rising water demands. The validation of these results using well discharge data from 12 monitoring wells supports these findings.

Design and Research of Intelligent Valve Control

The design and research of intelligent valve control system aims to improve the efficiency and accuracy of fluid control in industrial process automation. By integrating advanced sensing technology, communication protocols and intelligent algorithms, the precise control of valve position, pressure and flow rate is achieved. STM32 controller and a variety of sensors are used in the system to realize real-time monitoring and feedback control of valve status. The control strategy based on PID algorithm enables the valve to maintain stable operating performance under complex working conditions. In addition, the safety and reliability of the system are also considered, and the robustness of the system is improved by redundant design and fault detection mechanism. The experimental results show that the designed intelligent valve control system has significant advantages in improving response speed, control precision and energy efficiency, and has a wide range of industrial application prospects.

Research and Application of the Synergistic Support System of “LDAGF” in an Extremely Soft and Fragile Fully Mechanized Caving Face Roadway

Research and Application of the Synergistic Support System of “LDAGF” in an Extremely Soft and Fragile Fully Mechanized Caving Face Roadway

Deep neural network based distribution system state estimation using hyperparameter optimization

In the past decade, distribution system state estimation has become a crucial topic in power system research due to the increasing importance of distribution networks amidst the decline of centralized energy production. This paper addresses a gap in the literature regarding the application of modern hyperparameter optimization techniques in low-voltage distribution system state estimation using deep neural networks. In particular, it demonstrates the use of the Tree-structured Parzen Estimator algorithm, which is a Bayesian hyperparameter optimization method, for distribution system state estimation on real Hungarian low-voltage networks. The study uses data from four real-life low-voltage supply areas in Hungary, which were modeled to address the challenges in obtaining network information. Compared to traditional methods like the weighted least squares method, the Tree-structured Parzen Estimator algorithm significantly improves the accuracy of the voltage amplitude and angle estimations, reducing the relative error by 14–73%. Additionally, it is shown that TPE outperforms simpler methods like Random Search in hyperparameter optimization. The results also reveal connections between the distribution system size and optimal hyperparameters, such as batch size, learning rate, and hidden layer configuration. The proposed non-iterative algorithm, combined with the parallel computation capabilities of deep neural networks utilizing GPU, resulted in four orders of magnitude improvement in runtime. These advancements make the proposed approach a valuable tool for renewable energy integration planning and real-time monitoring, highlighting its potential for practical applications in the power industry.

The forgotten prisoners: diet and behaviour research in carceral systems

The forgotten prisoners: diet and behaviour research in carceral systems

The process of transition to a value-based distribution model in the Turkish land readjustment system

The Turkish Land Readjustment (LR) System is a area-based system. The system is based on the principle of equal land contribution in return for the increase in value that will occur with the LR implementation. However, the applied area-based method is criticized because it does not ensure equality, does not include the construction of technical infrastructure and social facilities, is not participatory, and does not bring the increase in value to the public. For this reason, a study has been initiated by the Ministry of Environment, Urbanization and Climate Change to diversify plan implementation tools. The article presents the results of the study regarding the value-based LR model. According to the findings of the research, as an alternative to the current area-based LR System in Turkey, the application of the value-based method will add a new dimension to the Turkish LR System. The fact that the country has a developed cadastral system ensures that area-based LR is carried out healthfully. However, the inadequacy of the valuation infrastructure makes the transition to value-based LR difficult. In Turkey, suddenly switching to value-based practices without a well-established valuation infrastructure may completely stop the existing parcel production capacity. For this reason, alternative models should be gradually incorporated into the system by eliminating the shortcomings of the current method. In the article, short, medium and long-term suggestions are presented for the management of the transition process, in line with the findings obtained from the research in the Turkish LR System.

Use of electronic health records to characterize patients with uncontrolled hypertension in two large health system networks

BackgroundImproving hypertension control is a public health priority. However, consistent identification of uncontrolled hypertension using computable definitions in electronic health records (EHR) across health systems remains uncertain.MethodsIn this retrospective cohort study, we applied two computable definitions to the EHR data to identify patients with controlled and uncontrolled hypertension and to evaluate differences in characteristics, treatment, and clinical outcomes between these patient populations. We included adult patients (≥ 18 years) with hypertension (based on either ICD-10 codes of hypertension or two elevated blood pressure [BP] measurements) receiving ambulatory care within Yale-New Haven Health System (YNHHS; a large US health system) and OneFlorida Clinical Research Consortium (OneFlorida; a Clinical Research Network comprised of 16 health systems) between October 2015 and December 2018. We identified patients with controlled and uncontrolled hypertension based on either a single BP measurement from a randomly selected visit or all BP measurements recorded between hypertension identification and the randomly selected visit).ResultsOverall, 253,207 and 182,827 adults at YNHHS and OneFlorida were identified as having hypertension. Of these patients, 83.1% at YNHHS and 76.8% at OneFlorida were identified using ICD-10-CM codes, whereas 16.9% and 23.2%, respectively, were identified using elevated BP measurements (≥ 140/90 mmHg). A total of 24.1% of patients at YNHHS and 21.6% at OneFlorida had both diagnosis code for hypertension and elevated blood pressure measurements. Uncontrolled hypertension was observed among 32.5% and 43.7% of patients at YNHHS and OneFlorida, respectively. Uncontrolled hypertension was disproportionately higher among Black patients when compared with White patients (38.9% versus 31.5% in YNHHS; p < 0.001; 49.7% versus 41.2% in OneFlorida; p < 0.001). Medication prescription for hypertension management was more common in patients with uncontrolled hypertension when compared with those with controlled hypertension (overall treatment rate: 39.3% versus 37.3% in YNHHS; p = 0.04; 42.2% versus 34.8% in OneFlorida; p < 0.001). Patients with controlled and uncontrolled hypertension had similar incidence rates of deaths, CVD events, and healthcare visits at 3, 6, 12, and 24 months. The two computable definitions generated consistent results.ConclusionsWhile the current EHR systems are not fully optimized for disease surveillance and stratification, our findings illustrate the potential of leveraging EHR data to conduct digital population surveillance in the realm of hypertension management.

Getting away “Scott” (but not Susan) free: The effects of safety‐specific abusive supervision and supervisor gender on follower attributions and safety outcomes

SummaryWhile most research emphasizes the harmful effects of abusive supervision, we argue that certain contextual factors—specifically hazardous work environments and supervisor gender—may lead abusive supervision to be perceived as driven by performance promotion intentions as opposed to injury initiation intentions. We introduce the concept of Safety‐Specific Abusive Supervision (SSAS), which we define as the extent to which a supervisor's active response to safety incidents is perceived by employees as abusive. Drawing from event system theory and research on attributions of abusive supervision, we theorize that when supervisors engage in SSAS, employees are more likely to attribute their behavior to performance promotion rather than injury initiation, perceiving the supervisors' actions as a means to keep them safe rather than to cause harm. We predict that performance promotion attributions mediate the relationship between SSAS and safety performance outcomes, namely safety voice and perceived supervisor safety commitment. However, consistent with role congruity theory, we hypothesize that this relationship is only true for male supervisors and not for female supervisors. Across three studies (two experimental studies and one field study), we largely find support for our hypotheses.