Power Support Research Articles

Distributed consensus-based voltage and frequency control for isolated microgrids with fault-induced delayed voltage recovery mitigation

This paper presents a distributed consensus-based voltage and frequency control (VFC) strategy for isolated microgrids with distributed energy resources (DERs) and induction motor loads. The proposed controller coordinates the DERs to regulate microgrid frequency and voltage while mitigating fault-induced delayed voltage recovery (FIDVR), a phenomenon where system voltage remains depressed for several seconds after fault clearance due to induction motor stalling. The VFC loop adjusts DER voltage setpoints based on frequency deviation and voltage level to regulate voltage and mitigate FIDVR events, while the active power control loop maintains frequency stability by coordinating active power sharing among DERs and compensating for the constant power load behavior of stalled induction motors. A proximity-based reactive power support prioritization and a distributed voltage estimator enhance the controller’s response to FIDVR events. Coordination between the VFC and active power control loops is achieved through adaptive gain adjustment and a voltage recovery coordination term. Simulation results demonstrate the effectiveness of the proposed controller in maintaining microgrid stability, ensuring fast voltage recovery, and providing robust performance under various operating conditions, including communication delays and different fault durations.

Analysis of the Influence of Deng Xiaoping’s Reform and Opening up Thought on China’s Modernization Process

Since Deng Xiaoping’s reform and opening up thought was proposed, it has had a profound impact on China’s modernization process. This paper first outlines the core content and characteristics of Deng Xiaoping’s reform and opening up thought, including focusing on economic construction and adhering to the four basic principles, and analyzes the practical application of this thought in various fields such as China’s economy, social politics, and cultural concepts. Subsequently, the paper systematically reviews the evolution of China’s modernization process before and after the reform and opening up, especially the significant achievements made by China in economic development, social and political progress, and cultural concept transformation after the reform and opening up. Specifically, Deng Xiaoping’s reform and opening up thought promoted the transformation of China’s economic system from a planned economy to a market economy, stimulated market vitality and creativity, and promoted the sustained and rapid development of Chinese economy. At the same time, this thought has also promoted social and political progress, strengthened the construction of democracy and the rule of law, and guaranteed the democratic rights and freedoms of the people. In terms of cultural concepts, Deng Xiaoping’s reform and opening up thought has led the transformation and innovation of cultural concepts, promoted the openness and progress of people’s ideological concepts, and provided spiritual power and intellectual support for modernization construction. In summary, Deng Xiaoping’s reform and opening up thought has had a profound impact on China’s modernization process, and its combination of theory and practice has provided valuable experience and inspiration for China’s modernization construction.

Indocyanine green dyed gauze-guided minimum invasive surgery for anatomical landmarks and preventing gauze remnants: a pilot study.

We aimed to develop a novel fluorescent surgical gauze dyed with indocyanine green (ICG) to guide surgeons to the target anatomical destination during surgery for real-time navigation and to prevent gauze remnants after surgery. Surgical gauze was dyed with an aqueous solution of ICG (5.0 × 10- 5 mol L- 1 for Steraze, 1.5 × 10- 4 mol L- 1 for BK-Opeze) at 132°C (inside pressure: 2.82atm, 286kPa) for 15min using an autoclave, followed by washing with distilled water, drying at room temperature, and sterilizing at 132°C for 8min before surgery. Fluorescence (FL) intensity was examined preclinically in the resected specimens using the SPY PHI (Stryker) system. Fourteen patients who underwent laparoscopic- and robotic-assisted gastroenterological surgery at Showa University Hospital were included. Fluorescent emission of ICG-dyed gauze was clearly observed through resected specimens with a thickness of approximately 10mm or more. In a clinical trial, the ICG-dyed gauze was detected earlier with near-infrared (near-IR) FL imaging than under white light during seven cases of laparoscopic and robotic surgery, which could become a precise marker for surgeons to locate the dissection site despite overlaying tissues and nearby disturbances. Additionally, no seepage of ICG from the gauze was observed in all surgical fields. We successfully developed ICG-dyed gauze exhibiting bright near-IR FL which can guide surgeons to the target anatomical destination and prevent gauze remnants during surgery. This invention would be a powerful support for real-time navigation surgery.

Mechatronics Application and Performance Analysis in Intelligent Transportation Systems

This paper presents a comprehensive analysis of the integration of mechatronics in intelligent transportation systems (ITS), elucidating its functional capabilities and limitations. As urbanization accelerates and traffic demand rises, the ITS has emerged as vital solutions for addressing traffic congestion and safety issues. In this regard, mechatronics technology plays a key role by enhancing the intelligence of traffic management, thereby improving the overall efficiency and safety of transportation networks. Thus, the paper provides a detailed analysis of the mechatronics application in intelligent traffic signal control, road condition monitoring and management, and vehicle positioning and navigation. In the domain of intelligent traffic signal control, mechatronics technology facilitates adaptive signal adjustments through real-time data collection and analysis, effectively alleviating traffic congestion and enhancing road capacity. In the field of road condition monitoring and management, this technology combines advanced sensors and monitoring equipment to realize accurate identification and dynamic monitoring of vehicles, providing powerful support for traffic management and law enforcement. In addition, the use of mechatronics technology in critical areas such as vehicle positioning and navigation markedly enhances road utilization, effectively mitigates traffic congestion, as well as supports sustainable urban development initiatives. Meanwhile, the performance of mechatronics in ITS, including real-time, intelligence, safety and reliability, is analyzed, highlighting its remarkable effect in enhancing the overall performance of ITS.

Enhancement of first carbon hit rate in converter steelmaking through integrated learning‐based data cleansing

AbstractFirst carbon hit rate (FCHR) is an essential indicator of steel converter smelting, reflecting the proportion of steel tapping completed without additional oxygen blowing. However, significant data loss has occurred due to equipment ageing and worker operations, resulting in difficulties in analysing the FCHR. This paper uses mechanism analysis and feature screening to determine the model input, predicts and fills in abnormal data through ensemble learning, and then optimises it through data transformation. Finally, the Stacking model predicts the FCHR, with a training accuracy of up to 94.5% and a test set accuracy of 90.5%. In addition, the authors also conducted a predictive study on oxygen consumption, and the hit rate performed well under different error thresholds, with a maximum of 97.9%. These results provide powerful decision support for steel production and effectively overcome the challenges of data missingness.

Modulation strategy of a DAB-type isolated DC-AC converter with reactive power support capabilities

Modulation strategy of a DAB-type isolated DC-AC converter with reactive power support capabilities

Mitral Regurgitation Evaluation in Modern Echocardiography: Bridging Standard Techniques and Advanced Tools for Enhanced Assessment.

Mitral regurgitation (MR) is one of the most common valvular heart diseases worldwide. Echocardiography remains the first line and most effective imaging modality for the diagnosis of mitral valve (MV) pathology and quantitative assessment of MR. The advent of three-dimensional echocardiography has significantly enhanced the evaluation of MV anatomy and function. Furthermore, recent advancements in cardiovascular imaging software have emerged as step-forward tools, providing a powerful support for acquisition, analysis, and interpretation of cardiac ultrasound images in the context of MR. This review aims to provide an overview of the contemporary workflow for echocardiographic assessment of MR, encompassing standard echocardiographic techniques and the integration of semiautomated and automated ultrasound solutions. These novel approaches include advancements in segmentation, phenotyping, morphological quantification, functional grading, and chamber quantification.

Defense Stability and Ecosystem Sustainability in the Strait of Malacca: Maritime Security Challenges from an International Relations Perspective

This study examines the challenges of defense stability and ecosystem sustainability in the Strait of Malacca, one of the most strategic shipping lanes in the world. The strait plays a crucial role in global trade and geopolitics; however, the high volume of maritime traffic has led to issues such as maritime security threats, including piracy, smuggling, and geopolitical tensions, as well as environmental threats like marine pollution and ecosystem degradation. Cooperation between coastal countries such as Indonesia, Malaysia, and Singapore through the Cooperative Mechanism has successfully reduced security threats and environmental impacts, though significant challenges remain. The research employs a qualitative approach through literature reviews and document analysis. It focuses on the interaction between defense stability and ecosystem sustainability from an international relations perspective, including the role of security actors and environmental diplomacy. The findings reveal that defense stability in the Strait of Malacca is closely linked to regional cooperation and international power support. The use of advanced technologies such as satellite monitoring has enhanced maritime security, yet stronger environmental protection policies are still needed. In conclusion, the stability of the Strait of Malacca requires effective mitigation through cross-border cooperation, international policies, and enhanced monitoring technologies to safeguard this vital route for the global economy and the environment.

Research on Value Guidance and Practical Pathways for Constructing the Professional Culture of Teachers

As knowledge disseminators and moral guides, the shaping of the teacher's professional culture not only concerns the growth and development of teachers themselves, but also directly affects the formation of students' values and the future outlook of society. Value guidance, as the core of the teacher's professional culture construction, aims to guide teachers to form a positive and dedicated professional spirit through the integration of a series of concepts and practices. This study aims to deeply explore the issue of value guidance in the construction of teacher professional culture, providing powerful theoretical support and practical guidance for building a more positive and meaningful teacher professional culture.

Structural Design and Analysis of Knee Joint Rehabilitation Exoskeleton Robot

This paper provides the design and optimization of a knee rehabilitation exoskeleton robot, focusing on mechanical structure, material selection, and motion mechanics analysis. First, it starts by identifying design requirements based on knee joint biomechanics. The design uses a tandem elastic actuator as the main power source and includes a crank guide rod mechanism to adjust the lengths of the thigh and calf segments. This helps reduce the peak force demand during actuation and improves the comfort of human-computer interaction and the degree of lightweight of the device. The study also analyzed the kinematic parameters of the knee joint to ensure that the exoskeleton can effectively support the needs of patients during rehabilitation training and limit the knee range of motion to a safe range through the mechanics. Furthermore, the paper compares the properties of three materials, titanium alloy, carbon fiber, and aluminum alloy, and discusses how to balance factors such as strength, weight, durability, and cost in exoskeleton design. The final design of the exoskeleton robot is not only suitable for rehabilitation training but also able to provide continuous power support for patients with permanent disabilities and effectively improve their quality of life.

A decision support system for comments-adjusted ranking of hotels

Increasing online reviews provide a powerful support tool to obtain a relevant hotel selection for tourists. Some reviews seem to have similar contents but their latent meanings are discrepant, due to user preferences. It has a negative effect on the recommendation result without a well processing. To solve this issue, this study develops a cloud-based ranking method to provide hotel recommendation lists for tourists. The method uses the distribution of historical user ratings to adjust original user ratings for minimizing the understanding bias among reviews, and utilizes sentiment analysis and cloud models to quantify text comments. After these, both of them are merged into a holistic score to generate a hotel recommendation list. Our method is validated through a case study of 87,735 users and 835,462 reviews from TripAdvisor.com. Further, our research findings suggest that potential hotel stayers should realize to lower their expectations of a good stay from online reviews, and trust more online reviews of couple and friend travelers.

Multi-Objective Coordinated Optimization Method of Active and Reactive Power Considering Power Characteristics of Renewable Energy Converters

In new power systems with a high proportion of renewable energy, optimization criteria based solely on economic efficiency or system stability may lead to a reduction in the static stability domain of the system or lead to long-term deviations from economic operation, thus reducing the overall applicability of such methods. This paper proposes a multi-objective active–reactive power coordinated optimization model that considers both economic efficiency and static stability indicators. The goal of the model is to minimize operating costs while optimizing static stability margins. It combines the reactive power support capabilities of converters and other reactive power compensation equipment to ensure safe and economical dispatch of the system. The proposed method is verified through a case study, which shows that this method can make full use of the potential reactive power regulation capability of the converter. At the same time, the economics and stability of the system are significantly improved by using this method. The overall improvement is about is 12.3%.

The Influence of Stability in New Power Systems with the Addition of Phase Modulation Functions in Thermal Power Units

The addition of phase modulation function technology to thermal power units is one of the most effective measures to solve dynamic reactive power shortages in the construction process of new power systems. In this paper, the influence of the phase modulation function transformation of thermal power units on the stability of a new power system is studied. Firstly, the new power system stability index is deeply analyzed, and an evaluation system for power system transient stability is constructed from five key dimensions: transient voltage, static voltage, power angle stability, power flow characteristics, and grid support. Secondly, a fuzzy comprehensive evaluation method considering the subjective and objective comprehensive weights is proposed, and the influence of the phase modulation transformation of the thermal power unit on the stability of the receiving-end power grid is quantitatively analyzed. Finally, a CEPRI36 node example model was built based on the PSASP v.7.91.04.9258 (China Electric Power Research Institute, Beijing, China) platform to verify the accuracy and effectiveness of the proposed method. The results show that the proposed method can quantitatively analyze the impact of adding a phase modulation function to thermal power units on the stability of the power system. At the point of renewable energy connection, the static voltage stability index improved by 42.9%, the transient power angle stability index improved by 32.1%, the multi-feed effective short-circuit ratio index improved by 33.9%, and the comprehensive evaluation score improved by 14.7%. These results further indicate that adding a phase modulation function to thermal power units can provide a large amount of dynamic reactive power support and improve the voltage stability and operational flexibility of the system.

Research on Unmanned Aerial Vehicle Emergency Support System and Optimization Method Based on Gaussian Global Seagull Algorithm

In emergency rescue scenarios, drones can be equipped with different payloads as needed to aid in tasks such as disaster reconnaissance, situational awareness, communication support, and material assistance. However, rescue missions typically face challenges such as limited reconnaissance boundaries, heterogeneous communication networks, complex data fusion, high task latency, and limited equipment endurance. To address these issues, an unmanned emergency support system tailored for emergency rescue scenarios is designed. This system leverages 5G edge computing technology to provide high-speed and flexible network access along with elastic computing power support, reducing the complexity of data fusion across heterogeneous networks. It supports the control and data transmission of drones through the separation of the control plane and the data plane. Furthermore, by applying the Tammer decomposition method to break down the system optimization problem, the Global Learning Seagull Algorithm for Gaussian Mapping (GLSOAG) is proposed to jointly optimize the system’s energy consumption and latency. Through simulation experiments, the GLSOAG demonstrates significant advantages over the Seagull Optimization Algorithm (SOA), Particle Swarm Optimization (PSO), and Beetle Antennae Search Algorithm (BAS) in terms of convergence speed, optimization accuracy, and stability. The system optimization approach effectively reduces the system’s energy consumption and latency costs. Overall, our work alleviates the pain points faced in rescue scenarios to some extent.

Analysis Of Machine Learning Algorithms For Image Classification

This research aims to use a subset of the CIFAR100 dataset to develop and present an empirical examination of the performance of powerful Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) for object detection. Image datasets, CIFAR10, CIFAR 100, and MINIST image datasets are all common benchmark datasets for testing performance. This research examines the well-known dataset CIFAR 100. Both classifiers' important attributes are combined in the constructed models. The suggested model's algorithm is trained and tested using images from the CIFAR-100 dataset. The CIFAR 100 is made up of 100 different classes, each with 600 photos. CNN and SVM's receptive fields aid in automatically extracting the most identifiable features from these images. The experimental results confirm the effectiveness of the implementation by reaching a maximum recognition accuracy of 0.4919 percent. Results were obtained as maximum accuracy of 0.4817% For the CNN on Coarse Super Label and maximum accuracy of 0.3696% For the CNN on Fine Super Label, while SVM on Coarse Super Label resulted in an accuracy of 0.0795% and SVM on Fine super Label resulted in 0.0285%. According to the results, CNN was able to exceed the benchmark of 39.43% and 24.49%, but SVM was unable to do so.

Control strategy of the novel stator free speed regulating wind turbine generation system.

Building a high-proportion renewable energy power system is a key measure to address the challenges of the energy revolution and climate change. However, current high-proportion renewable energy systems face issues of frequency instability and voltage fluctuations. To address these challenges, this paper proposes a novel topology for a stator free speed regulating wind turbine generation system. The stator free speed regulating machine connects a gearbox and a synchronous generator, forming a flexible drive chain that increases system inertia and enhances frequency stability in high-proportion renewable energy power systems. The synchronous generator at the end of the drive chain can be directly connected to the grid without the converter, thanks to the speed regulation provided by the stator free speed regulating machine, thus avoiding harmonic pollution caused by power electronic devices in traditional wind turbines, enhancing the reactive power support capability, and improving voltage stability in high-proportion renewable energy systems. Given that the proposed stator free speed regulating machine consists solely of a rotating inner and outer rotor without a stator, traditional motor control strategies are not applicable. Therefore, this paper focuses on developing control strategies for the stator free speed regulating machine, employing a dual closed-loop PI control strategy with an outer loop for speed and an inner loop for current, based on flux orientation of the outer rotor. Simulation experiments will validate the feasibility of the proposed stator free speed regulating wind turbine generation system topology and the effectiveness of the control strategy.

Utilizing Soft Open Points for Effective Voltage Management in Multi-Microgrid Distribution Systems

To enhance stability and reliability, multi-microgrid systems have been developed as replacements for conventional distribution networks. Traditionally, switches have been used to interconnect these microgrids, but this approach often results in uncoordinated power sharing, leading to economic inefficiencies and technical challenges such as voltage fluctuations, delay in response, etc. This research, in turn, introduces a novel multi-microgrid system that utilizes advanced electronic devices known as soft open points (SOPs) to enable effective voltage management and controllable power sharing between microgrids while also providing reactive power support. To account for uncertainties in the system, the two-point estimate method (2PEM) is applied. Simulation results on an IEEE 33-bus network with high renewable energy penetration reveal that the proposed SOP-based system significantly outperforms the traditional switch-based method, with a minimum voltage level of 0.98 p.u., compared to 0.93 p.u. in the conventional approach. These findings demonstrate the advantages of using SOPs for voltage management in forming multi-microgrid systems.

Targeting extracellular matrix stiffness for cancer therapy.

The physical characteristics of the tumor microenvironment (TME) include solid stress, interstitial fluid pressure, tissue stiffness and microarchitecture. Among them, abnormal changes in tissue stiffness hinder drug delivery, inhibit infiltration of immune killer cells to the tumor site, and contribute to tumor resistance to immunotherapy. Therefore, targeting tissue stiffness to increase the infiltration of drugs and immune cells can offer a powerful support and opportunities to improve the immunotherapy efficacy in solid tumors. In this review, we discuss the mechanical properties of tumors, the impact of a stiff TME on tumor cells and immune cells, and the strategies to modulate tumor mechanics.

Synthetic Data Generation with Modified Artificial Bee Colony Optimization Algorithm and Statistical Modeling

Machine learning is a powerful decision support system used in analyzing and evaluating real-life data. This system aims to create new solutions and improve performance. Therefore, it is related to the field of data science. There are data on the basis of this relationship The effectiveness of drawing meaningful insights from data depends on the quality of the model's training. To improve this performance, the variety of combinations among the data and the total number of data in the dataset should be increased. But in this topic, insufficient data access, legal regulations, ethical rules, confidentiality procedures, privacy, data sharing restrictions and cost parameters are obstacles. Synthetic data generation is a basic step in the field of data science in order to solve all these problems, improve functionality and provide powerful machine-learning inferences. Therefore, a new synthetic data generation approach consisting of 3 basic stages is proposed in this study. In the first stage, synthetic data production similar to the distribution of the original data was carried out with the modified ABC (Artificial Bee Colony) optimization algorithm. In the second stage, the category information of the independent variables was determined by the statistical evaluation analyzed with regression methods among the artificial data produced. In the third stage, the efficiency and applicability of the artificial data produced were evaluated with supervised machine learning classifiers. As a result of the evaluation, it has been proven that the proposed synthetic data generation approach improves the performance of machine learning classifiers in proportion to the increasing number of data. The decision tree algorithm that showed maximum performance produced success rates of 100%, 92.5%, 100%, 85%, and 66% on 5 separate enriched datasets, respectively.

Understanding and addressing gender-based violence in Eswatini

The high prevalence of gender-based violence (GBV) in the kingdom of Eswatini is quiet saddening. Women and girl children become victims of GBV to the extent of losing their lives. Therefore, this article sought to find the causes of GBV, collaborative majors and guidelines to help reduce GBV. The paper employed a qualitative research design underpinned by a transformative paradigm as a framework for promoting preventive action toward social justice for marginalized populations. Participatory Action Learning Action Research (PALAR) was employed. A purposeful sample of traditional leaders, religious leaders, law enforcers, NGOs and public were selected. Data was obtained through individual interviews, focus group interviews and analyzed using the thematic analysis approach. Findings obtained revealed various causes of GBV: unequal power relations, socio-economic factors, weak legal and support systems. Study recommends that government in collaboration with stakeholders must establish educational programs and guidelines to fight GBV, improve psycho-social support, strengthen recreational systems, implement programs that focus on poverty alleviation and economic empowerment, decolonization of patriarchal cultural practices and develop awareness programs about issues faced by GBV survivors. Sexual offences domestic violence act (SODV) must be enforced by the law.