Modern Systems Research Articles

Формирование творческого опыта хореографа в системе профессионального образования

The modern system of training specialists in the field of choreography prepares a creative person who constantly strives for professional self-improvement and strives to teach and train others (children and adults). Since choreographic art is the art of beauty and health of the human body. Therefore, many teachers around the world strive to study, analyze and develop their own innovative teaching methods. Since they have a positive effect on (1) the manifestation of creative abilities in future specialists; (2) development of various forms of thinking; (3) expand worldviews in general, and (4) help to see the interconnectedness of dance art with biology and other sciences. It is precisely these qualities that future teachers and choreography teachers need and must be developed in the learning process. Therefore, in many educational institutions, the development of art education is noted through updating its content, associated with rethinking the social purpose of the profession of a choreography teacher, since it is he who is able to creatively introduce socially significant innovations and flexibly respond to the cultural demands of the time. The necessity is substantiated not only for the teacher to develop the subject and practical skills of a dancer, but also for the synthesis of knowledge, abilities and skills that allow the effective implementation of professional training of future choreographers with relevant competencies. The article emphasizes that the formation of artistic and creative experience is associated (1) with the need to get acquainted with works of art through perception and performing interpretation; (2) with the motivation to acquire knowledge in the field of expressive means as the basis for creating choreographic images; (3) with the development of the ability to perceive artistic images in the field of choreographic art for in-depth mastery of the art of reproducing choreographic works. Taking into account the listed features requires the effective use of creative tasks in the educational process through means of improving and optimizing educational activities. It has been proven that professional training in modern choreography requires the teacher’s ability to integrate his knowledge when teaching specific disciplines in order to appropriately update the educational process.

A multifunctional inverter power quality coordinated optimization strategy based on comprehensive evaluation

The large-scale integration of renewable energy sources and power electronic devices has led to increasingly dispersed and networked characteristics of power quality disturbances in distribution systems. Traditional control devices, limited by their fixed-point control, no longer meet the development needs of modern distribution systems. Considering the distribution and structural characteristics of the current new-type sources and loads, a multifunctional inverter power quality coordinated control strategy based on comprehensive evaluation is proposed. This strategy aims to achieve power quality coordinated control by utilizing optimal compensation capacity while the grid-connected inverter provides active power output. Firstly, the traditional ipiq power quality detection method is analyzed and improved to enable detection of harmonics, reactive power, and three-phase imbalance currents in single-phase systems or three-phase four-wire systems. Secondly, in the power quality assessment, a balanced algorithm is employed to obtain the comprehensive evaluation index of power quality, thereby improving the deficiencies of single-weight evaluation. Next, to ensure the grid-connected inverter achieves optimal power quality coordinated control with minimal compensation capacity, an optimization compensation function is established between compensation capacity and comprehensive power quality index. This function is optimized using non-dominated sorting genetic algorithm to enhance equipment utilization and system economy. Finally, the feasibility and effectiveness of the proposed method are validated through simulations, demonstrating the achievement of optimal capacity configuration for power quality coordinated control while ensuring active power output by the grid-connected inverter.

A novel deep learning approach for estimating and classifying short-term voltage stability events in modern power systems with composite load and distributed energy resources

A novel deep learning approach for estimating and classifying short-term voltage stability events in modern power systems with composite load and distributed energy resources

Improved Particle Filter Algorithm for Multi-Target Detection and Tracking

In modern radar detection systems, the particle filter technique has become one of the core algorithms for real-time target detection and tracking due to its good nonlinear and non-Gaussian system state estimation capability. However, when dealing with complex dynamic scenes, the traditional particle filter algorithm exposes obvious deficiencies. The main expression is that the sample degradation is serious, which leads to a decrease in estimation accuracy. In multi-target states, the algorithm is difficult to effectively distinguish and stably track each target, which increases the difficulty of state estimation. These problems limit the application potential of particle filter technology in multi-target complex environments, and there is an urgent need to develop a more advanced algorithmic framework to enhance its robustness and accuracy in complex scenes. Therefore, this paper proposes an improved particle filter algorithm for multi-target detection and tracking. Firstly, the particles are divided into tracking particles and searching particles. The tracking particles are used to maintain and update the trajectory information of the target, and the searching particles are used to identify and screen out multiple potential targets in the environment, to sufficiently improve the diversity of the particles. Secondly, the density-based spatial clustering of applications with noise is integrated into the resampling phase to improve the efficiency and accuracy of particle replication, so that the algorithm can effectively track multiple targets. Experimental result shows that the proposed algorithm can effectively improve the detection probability, and it has a lower root mean square error (RMSE) and a stronger adaptability to multi-target situation.

Ruthlessly Subtract to Build Oncology Programs and Add Value.

Building modern healthcare programs and systems caring for populations requires expert skills in strategy, finance, people operations, workflow, evaluation, and more. Build often connotes adding services and people, but it al.

Relative stability evaluation of HVDC-connected wind farms

The small-signal stability of high voltage direct current (HVDC)-connected wind farms (WFs) is a challenging issue in modern power systems. The relative stability, i.e., the stability margin, of such a typical multiple-input multiple-output (MIMO) system is quite difficult to be quantified. This paper evaluates the relative stability of HVDC-connected WFs using a new stability index based on the ν-gap metric. We first develop a MIMO model represented by a transfer function matrix of the HVDC-connected WFs. Then, a new stability index, i.e., the robust stability margin, based on the ν-gap metric is proposed to quantify the relative stability of such a MIMO system. Finally, we propose a method to compute the stable region of control parameters based on the corresponding stability criterion of ν-gap metric. Case studies are given to demonstrate the effectiveness of the proposed method.

Measuring system resilience through a comparison of information- and flow-based network analyses

Quantifying the properties of complex, self-organizing systems is increasingly important for understanding the development and state of modern systems. Case studies have recommended sustainability frameworks predominately in literature, but little emphasis has been placed on methodological evaluation. Data availability is often an obstacle that constrains conventional flow-based network analysis, but a novel information-based technique (QtAC) developed by zu Castell and Schrenk overcomes these constraints by modelling interactions between agents as information transfers. This study compares the QtAC method to conventional flow analysis by applying both to the same 90-year dataset containing socio-economic data from the island of Samothraki, Greece. Resilience indicators, based on Ulanowicz’s ascendency analysis, are derived on both the information- and flow-based networks. We observe that the resulting dynamics of the information-based networks align closer with complex system dynamics as theorized by the adaptive cycle model. Additionally, we discuss how QtAC offers different interpretations of network indicators when compared to usual interpretations of flow analysis. Ultimately, QtAC is shown to provide an alternative for complex systems analysis if the data situation does not allow for conventional flow-analysis. Furthermore, we show that the combination of both approaches can yield valuable new insights.

ÜNSİYYӘTİN KOMMUNİKATİV MODELİ

The Communıcatıve Model of Communıcatıon Summary The article is about the communicative model of communication. The article presents communication as a complex process. A more complete and clear presentation of the communication model has recently been of interest not only to linguists, but also to other specialists involved in speech activity. Psychologists and physiologists try to unravel the psychological and physiological basis of communication with this model. The article compares the communication model with modern communication systems. It is noted that the units of the phonological level of the language are usually reflected in sounds. For this, in the process of historical development, the means of communication were formed in man, and man succeeded in pronouncing different sounds. Key words: communication, model, speech, phonological, brain, voice

An assessment of the value of deep neural networks in genetic risk prediction for surgically relevant outcomes.

Postoperative complications affect up to 15% of surgical patients constituting a major part of the overall disease burden in a modern healthcare system. While several surgical risk calculators have been developed, none have so far been shown to decrease the associated mortality and morbidity. Combining deep neural networks and genomics with the already established clinical predictors may hold promise for improvement. The UK Biobank was utilized to build linear and deep learning models for the prediction of surgery relevant outcomes. An initial GWAS for the relevant outcomes was initially conducted to select the Single Nucleotide Polymorphisms for inclusion in the models. Model performance was assessed with Receiver Operator Characteristics of the Area Under the Curve and optimum precision and recall. Feature importance was assessed with SHapley Additive exPlanations. Models were generated for atrial fibrillation, venous thromboembolism and pneumonia as genetics only, clinical features only and a combined model. For venous thromboembolism, the ROC-AUCs were 60.1% [59.6%-60.4%], 63.4% [63.2%-63.4%] and 66.6% [66.2%-66.9%] for the linear models and 51.5% [49.4%-53.4%], 63.2% [61.2%-65.0%] and 62.6% [60.7%-64.5%] for the deep learning SNP, clinical and combined models, respectively. For atrial fibrillation, the ROC-AUCs were 60.3% [60.0%-60.4%], 78.7% [78.7%-78.7%] and 80.0% [79.9%-80.0%] for the linear models and 59.4% [58.2%-60.9%], 78.8% [77.8%-79.8%] and 79.8% [78.8%-80.9%] for the deep learning SNP, clinical and combined models, respectively. For pneumonia, the ROC-AUCs were 50.1% [49.6%-50.6%], 69.2% [69.1%-69.2%] and 68.4% [68.0%-68.5%] for the linear models and 51.0% [49.7%-52.4%], 69.7% [.5%-70.8%] and 69.7% [68.6%-70.8%] for the deep learning SNP, clinical and combined models, respectively. In this report we presented linear and deep learning predictive models for surgery relevant outcomes. Overall, predictability was similar between linear and deep learning models and inclusion of genetics seemed to improve accuracy.

Enhancing power system stability with adaptive under frequency load shedding using synchrophasor measurements and empirical mode decomposition

This paper introduces an adaptive under frequency load shedding (AUFLS) scheme based on synchrophasor measurements and Empirical Mode Decomposition (EMD) to enhance power system stability during significant disturbances. The traditional UFLS methods, which rely on fixed frequency thresholds, often lead to unnecessary disconnections and a lack of flexibility. The proposed AUFLS scheme dynamically adapts to the magnitude of disturbances, frequency response, and voltage stability index, resulting in a more efficient and responsive system. Various UFLS techniques are reviewed, highlighting the advantages of adaptive strategies. The novelty of this research lies in the innovative application of the EMD algorithm within the AUFLS framework. This method identifies the center of inertia (CoI) of the rate of change of frequency (RoCoF) from frequency signals and calculates the total active power imbalance due to disturbances. Furthermore, EMD is applied to the voltage angle at each bus to identify and form coherent bus groups. These coefficients are subsequently employed to allocate the required active power shedding within each group to maintain system stability. Voltage stability indices are calculated for each group, and load shedding is distributed among the buses based on normalized voltage stability indices, thereby enhancing the precision of load shedding decisions and the overall stability of the power system. Results demonstrate that the proposed scheme provides satisfactory frequency response while requiring less load shedding compared to traditional methods, making it effective for modern power systems with high penetration of renewable energy sources (RES). The performance of the proposed scheme is assessed using the IEEE 39-bus test system, demonstrating its effectiveness in improving system resilience to frequency disturbances. The research concludes that the AUFLS scheme offers a promising approach for enhancing the resilience of power systems to frequency disturbances.

Conceptual design and optimization of integrating renewable energy sources with hydrogen energy storage capabilities

Systems are inherently tied to uncertainty, which necessitates the development of designs and schedules that accommodate this unpredictability. Particularly in modern power systems, the variable efficiency of numerous factors, including pricing, underscores the need for uncertainty modeling. In the meantime, the incorporation of EVs (electric vehicles) into the electrical grid is advancing, with these vehicles being recognized for their role in reducing dependency on fossil energies and enhancing the resilience, stability and efficiency of the grid. However, the rapid growth of the EV sector necessitates thoughtful strategies from decision-makers to manage its expansion effectively, particularly considering the environmental implications associated with the life cycle of EVs, including their charging and discharging processes. This research introduces the conceptual design and optimization approach utilizing a hybrid bat algorithm and differential evolution algorithm (BA-DEA) to improve the efficiency and resilience of EV-smart parking lots under the unpredictable conditions caused by grid pricing fluctuations within the DRP (demand response program). The technique effectively modulates daily costs by adjusting loads among peak and off-peak periods. Key features of the proposed approach include a non-dominated sorting model, innovative variable identification, a memory-based selection process, and the application of fuzzy theory to identify optimal Pareto outcomes. This methodology is not only swift in converging to a solution but also demonstrates a high likelihood of reaching the global optimum. Modeling considerations for smart hydrogen storage systems (SHSSs) incorporate significant constraints, notably those associated with electrolyzers, fuel cells, and storage capacities. The algorithm's effectiveness is validated in scenarios involving parking facilities and multiple uncertain resources, demonstrating a robust reduction in specific cost indicators and adjustments in cost dynamics when DRP is considered. Specifically, the algorithm achieves a substantial 42% decrease in cost variability when DRP is excluded, and a 46.9% reduction in cost variability when DRP is included, despite a minor 5% increase in average cost. These outcomes underscore the proposed system's capability to improve resilience and thermal performance under fluctuating conditions, making it a promising solution for future smart energy systems. Further, the results exhibited the complex interplay between cost optimization and operational adjustments in response to demand-side management.

Load frequency control in power systems with high renewable energy penetration: A strategy employing P[formula omitted](1+PDF) controller, hybrid energy storage, and IPFC-FACTS

The high penetration of Renewable Energy Sources (RESs) in the modern power system poses a challenge to power system stability. This stability is affected by the stochastic, fluctuating output of RESs, which is influenced by weather conditions, and a lack of inertia resulting from reduced rotating mass. To address this issue, a new controller, referred to as Proportional-Fractional Integrator Plus Proportional-Derivative with Filter, PIλ(1+PDF), is designed for Load Frequency Control (LFC) with the support of a Hybrid Energy Storage System (HESS) for power systems with high-RES penetration. The HESS comprises a Superconducting Magnetic Energy Storage System (SMES) and a Vanadium Redox Flow Battery (VRFB) coupled with an Interline Power Flow Controller Flexible AC Transmission Systems (IPFC-FACTs) controller. The HESS, working in conjunction with the proposed LFC, injects virtual inertia and maintains power flow to expedite the frequency stability process. These systems are also integrated with Alternating Current (AC) and High Voltage Direct Current (HVDC) transmission lines to collectively enhance both the system's stability and the capacity of its transmission lines. To optimize the PIλ(1+PDF) controller parameters, Zebra Optimization Algorithm (ZOA) is employed utilizing an Integral Time Absolute Error (ITAE) objective function. The proposed controller is tested on a four-area power system integrated with a wind turbine, photovoltaic (PV) panels, a biodiesel generator, and a hydrogen aqua electrolyzer fuel cell, representing a high penetration of RESs in modern power systems. The results are compared with those obtained using Proportional-Integral-Derivative (PID) and Fractional Order Proportional Integral Derivative (FOPID) controllers. Sensitivity analysis and robustness tests are also performed to verify the stability of the power network by changing system parameters and under randomly chosen loading conditions. The proposed PIλ(1+PDF) controller tuned with ZOA outperforms PID and FOPID controllers by minimizing settling time for frequency changes by 62 %, eliminating overshoot, and reducing undershoots for frequency and tie-line power changes by 73 % and 55 %, respectively. Simulation results demonstrate that the proposed controller outperforms PID and FOPID controllers by effectively damping frequency and tie-line deviations, resulting in reduced frequency overshoots, undershoots, and shorter settling times.

Intelligent optimal sitting and sizing of distributed resources in presence of renewable energy and fuel cells based on reliability and emission factors

As the integration of renewable energy sources—such as wind, solar, and fuel cells—into power systems increases, challenges related to system reliability and cost efficiency become paramount. This paper examines the reliability of a standard power network composed of diverse energy sources including wind turbines, solar panels, fuel cells, batteries, and diesel generators. This paper proposes a novel hybrid optimization approach utilizing a Grey Wolf Optimizer combined with another advanced algorithm to minimize the total cost of energy production, inclusive of emission penalties for CO2, NO2, and SO2. The proposed approach is tested on IEEE 57-bus and IEEE 118-bus systems, demonstrating significant improvements over existing algorithms. Quantitative results show that our method reduces the total cost by up to 18 % and enhances system reliability by reducing voltage deviations in various scenarios by 12 %. The paper also provides insights into the optimal sizing and placement of wind turbines, photovoltaics, diesel generators, and fuel cells under different emission constraints (15000, 25000, and 45000 kg), further emphasizing the flexibility and effectiveness of our method in addressing environmental and economic challenges in modern power systems.

Editorial: Advanced operation and control of distributed and grid-scale energy storage in modern low-voltage power systems

Editorial: Advanced operation and control of distributed and grid-scale energy storage in modern low-voltage power systems

Editorial: Advanced operation and control of distributed and grid-scale energy storage in modern low-voltage power systems

Editorial: Advanced operation and control of distributed and grid-scale energy storage in modern low-voltage power systems

Особенности объемно-планировочных решений стационарных зданий цирков

Objective: this work is devoted to the study of the features in space-planning solutions for stationary circus buildings and to determine the development of circus architecture. In recent years, with the appearance of new technologies, the requirements for space-planning and technical solutions of circus buildings have undergone significant changes. It is very important to preserve the traditions of circus art and, at the same time, for expansion of the circus performances are necessary modifications and transformations of the buildings. Methods: have been made a comprehensive analysis of existing circus buildings in Europe, Asia and Russia. The research is based on the analysis of professional literature and scientific articles. Results: has been revealed patterns and main features in construction of multifunctional entertainment buildings with elements of circus and various show performances (on ice, on water, in the air). Technical innovations applied in modern representations, such as removable playpens, the use of virtual reality and modern technical balancing systems, dictate new approaches to the organization of space. In Europe, animal performances are a thing of the past. In Russia and Asia, animals remain an essential part of the circus show, meanwhile control over their living conditions is becoming more strict. Based on the study of the already built circus facilities were determined the prospects for the construction of new and reconstruction of existing buildings. Practical significance: the results allow us to identify the main trends in the space-planning solutions of stationary circus buildings. This research will be useful for architects, designers and other specialists who are engaged in the design and reconstruction of circuses. The results of this research can be used to create modern and functional circus buildings that will meet the expectations of the audience and provide comfortable conditions for the artists.

A Comprehensive Review of Alarm Processing in Power Systems: Addressing Overreliance on Fault Analysis and Projecting Future Directions

This paper reviews alarm processing methods in electrical power systems, focusing on evolving strategies beyond traditional fault analysis to accommodate modern grid complexities. Historically, alarm processing has predominantly aimed at fault analysis, increasingly merging with technological advances in communication and computing. However, it still needs to fully meet the challenges posed by the dynamic characteristics of modern power systems. This review points out certain inadequacies in current practices, notably their limited adaptation to new grid conditions. The authors propose a novel generation of alarm processing methodologies designed for future grids, emphasizing managing rare events and enhancing operator decision-making through advanced anomaly detection and explainable artificial intelligence. This synthesis presents a prospective direction for future research and applications in alarm processing, advocating for methodologies better suited to supporting system operators amidst technological advancements.

ZTCloudGuard: Zero Trust Context-Aware Access Management Framework to Avoid Medical Errors in the Era of Generative AI and Cloud-Based Health Information Ecosystems

Managing access between large numbers of distributed medical devices has become a crucial aspect of modern healthcare systems, enabling the establishment of smart hospitals and telehealth infrastructure. However, as telehealth technology continues to evolve and Internet of Things (IoT) devices become more widely used, they are also increasingly exposed to various types of vulnerabilities and medical errors. In healthcare information systems, about 90% of vulnerabilities emerge from medical error and human error. As a result, there is a need for additional research and development of security tools to prevent such attacks. This article proposes a zero-trust-based context-aware framework for managing access to the main components of the cloud ecosystem, including users, devices, and output data. The main goal and benefit of the proposed framework is to build a scoring system to prevent or alleviate medical errors while using distributed medical devices in cloud-based healthcare information systems. The framework has two main scoring criteria to maintain the chain of trust. First, it proposes a critical trust score based on cloud-native microservices for authentication, encryption, logging, and authorizations. Second, a bond trust scoring system is created to assess the real-time semantic and syntactic analysis of attributes stored in a healthcare information system. The analysis is based on a pre-trained machine learning model that generates the semantic and syntactic scores. The framework also takes into account regulatory compliance and user consent in the creation of the scoring system. The advantage of this method is that it applies to any language and adapts to all attributes, as it relies on a language model, not just a set of predefined and limited attributes. The results show a high F1 score of 93.5%, which proves that it is valid for detecting medical errors.

Largely enhanced high‐temperature energy storage performance of P(VDF‐HFP) dielectric films via calcium niobate nanosheets

AbstractThe capacitive energy‐storage capacity of most emerging devices rapidly diminishes with increasing temperature, making high‐temperature dielectrics particularly desirable in modern electronic systems. In this work, calcium niobate (Ca2Nb3O10, CNO) nanosheets have been added into poly(vinylidene fluoride‐hexafluoropropylene) (P(VDF‐HFP), PVHP), forming PVHP/CNO nanocomposites with exceptional temperature stability and ultrahigh energy storage density. Especially, at 80°C and 450 MV/m, the PVHP/0.3 wt%CNO nanocomposite shows an excellent Wrec of 10.81 J/cm3 which is higher than previous PVDF‐based composite films at high temperatures. Because of the high dielectric permittivity of CNO nanosheets and the way that the parallel organization of the nanosheets blocks the course of electrical trees, nanocomposites exhibit greater dielectric constants and breakdown field strengths simultaneously. These findings, will be helpful in the development of flexible, high‐energy‐density capacitors that have stable performance at high temperatures.Highlights A record‐high high‐temperature Wrec is obtained. Excellent temperature stability from 25 to 80°C is achieved. The incorporation of Ca2Nb3O10 nanosheets significantly enhances Eb.

A hybrid quantum–classical algorithm for mixed-integer optimization in power systems

Mixed Integer Linear Programming (MILP) can be considered the backbone of the modern power system optimization process, with a large application spectrum, from Unit Commitment and Optimal Transmission Switching to verifying Neural Networks for power system applications. The main issue of these formulations is the computational complexity of the solution algorithms, as they are considered NP-Hard problems. Quantum computing has been tested as a potential solution towards reducing the computational burden imposed by these problems, providing promising results, motivating the can be used to speedup the solution of MILPs. In this work, we present a general framework for solving power system optimization problems with a Quantum Computer (QC), which leverages mathematical tools and QCs’ sampling ability to provide accelerated solutions. Our guiding applications are the optimal transmission switching and the verification of neural networks trained to solve a DC Optimal Power Flow. Specifically, using an accelerated version of Benders Decomposition , we split a given MILP into an Integer Master Problem and a linear Subproblem and solve it through a hybrid “quantum–classical” approach, getting the best of both worlds. We provide 2 use cases, and benchmark the developed framework against other classical and hybrid methodologies, to demonstrate the opportunities and challenges of hybrid quantum–classical algorithms for power system mixed integer optimization problems.