Technology Research Articles

A Comprehensive Review on Imperative Role of Ionic Liquids in Pharmaceutical Sciences.

Ionic liquids (ILs) are poorly-coordinated ionic salts that can exist as a liquid at room temperatures (or <100 °C). ILs are also referred to as "designer solvents" because so many of them have been created to solve particular synthetic issues. ILs are regarded as "green solvents" because they have several distinctive qualities, including better ionic conduction, recyclability, improved solvation ability, low volatility, and thermal stability. These have been at the forefront of the most innovative fields of science and technology during the past few years. ILs may be employed in new drug formulation development and drug design in the field of pharmacy for various functions such as improvement of solubility, targeted drug delivery, stabilizer, permeability enhancer, or improvement of bioavailability in the development of pharmaceutical or vaccine dosage formulations. Ionic liquids have become a key component in various areas such as synthetic and catalytic chemistry, extraction, analytics, biotechnology, etc., due to their superior abilities along with highly modifiable potential. This study concentrates on the usage of ILs in various pharmaceutical applications enlisting their numerous purposes from the delivery of drugs to pharmaceutical synthesis. To better comprehend cuttingedge technologies in IL-based drug delivery systems, highly focused mechanistic studies regarding the synthesis/preparation of ILs and their biocompatibility along with the ecotoxicological and biological effects need to be studied. The use of IL techniques can address key issues regarding pharmaceutical preparations such as lower solubility and bioavailability which plays a key role in the lack of effectiveness of significant commercially available drugs.

Reviewing the Impact of Engineering Measures on Soil Erosion in Jammu and Kashmir Anantnag Watershed

Planners now find that stopping soil loss in India's watersheds must be their top priority. Currently, a wide range of mathematical models are used worldwide to predict soil erosion. The Revised Universal Soil Loss Equation (RUSLE), which is frequently used to predict average soil loss, is one such model. Recently, there has been an increased emphasis on applying USLE/RUSLE in conjunction with Geographic Information System (GIS) technology, enabling grid-based analysis for predicting soil erosion and aiding management methods. This study assesses the efficacy of flood control and erosion management programs launched in 1914. Four different maps representing the possible erosion threats were created for four different time periods, and a comparison study was carried out to assess the changes that occurred. The implementation of various strategies such as terracing, afforestation, and rehabilitation in the watershed led to a noteworthy prediction of decreased soil loss in the watershed. In the temperate region of Jammu and Kashmir and sparsely vegetated areas, the rate of anticipated soil loss decreased from 632 to 378 metric tons per hectare per year between 1990 and 2020, indicating the efficacy of soil conservation measures in mitigating potential soil loss.

Optimizing Wing-In-Ground Effect UAVS for Enhanced Search and Rescue Operations: A Comprehensive Review

Abstract: Wing-In-Ground Effect (WIG) Unmanned Aerial Vehicles (UAVs) represent a unique convergence of aerodynamic principles and unmanned systems technology, offering promising solutions for maritime operations. This paper explores the historical development, theoretical foundations, and potential applications of WIG UAVs. The phenomenon of ground effect, which enhances lift and reduces drag when flying close to water surfaces, has been observed since the early 20th century. Soviet engineer Rostislav Alexeyev's pioneering work on the Lun-class ekranoplan in the 1960s demonstrated the viability of WIG technology for military logistics. Advancements in materials science, propulsion systems, and computer-aided design have further refined WIG vehicle performance. The integration of autonomous capabilities has expanded the operational scope of WIG UAVs, enabling missions in challenging maritime environments. The aerodynamic investigation of WIG vehicles reveals the complex interplay between the vehicle's wings and the water surface, leading to enhanced lift-to-drag ratios and improved efficiency. Computational Fluid Dynamics (CFD) simulations and wind tunnel experiments have provided valuable insights into the optimization of WIG UAV designs. The potential applications of WIG UAVs span maritime surveillance, coastal patrolling, search and rescue operations, and cargo transportation. Their ability to collaborate with other unmanned systems, such as aerial drones and surface vessels, enhances overall mission effectiveness. As the demand for sophisticated maritime capabilities grows, the development of WIG UAVs presents a promising frontier for innovation, offering unique solutions to address evolving challenges in maritime security and operational effectiveness

Smart noise pollution monitoring system

Increasing pollution poses serious health risks, necessitating urgent control measures for a healthier future for all. Among various pollution types, sound pollution significantly impacts quality of life and well-being, especially in densely populated urban areas. This proposal describes the creation and deployment of an Internet of Things (IoT)-based noise pollution monitoring system intended to track sound levels in certain regions in real time. The core of this system comprises advanced sensors strategically placed to measure sound intensity accurately. These sensors can detect even minor variations in noise levels, providing precise and reliable data. After being gathered, the data is sent to a microcontroller, which effectively processes it. This microcontroller ensures the data is accurately captured and sent over the internet to an online server. Once the data reaches the online server, it becomes accessible for analysis and monitoring by authorities. The system's real-time data capability is crucial for timely intervention. Authorities can continuously monitor noise levels and identify trends or spikes in sound pollution, allowing them to address issues promptly. This feature is particularly beneficial for sensitive areas such as schools, hospitals, and residential zones, areas with heavy traffic, library, Where excessive noise can have severe health effects, including stress, sleep disturbances, and hearing loss. The system includes an alert mechanism that notifies relevant authorities when noise levels exceed predefined thresholds. These alerts enable quick responses to mitigate noise pollution. For instance, in a school, reducing noise levels can enhance the learning atmosphere and improve students' concentration. In hospitals, maintaining a quiet environment is crucial for patient recovery. The integration of IoT technology in this noise pollution monitoring system not only enhances efficiency but also provides a scalable solution adaptable to various urban and rural settings. This scalability ensures the system can be expanded to cover larger areas or multiple locations as needed.

Machine learning techniques for enhancing security in financial technology systems

The financial technology (fintech) industry has transformed the way financial services are delivered, offering enhanced convenience, accessibility, and efficiency. However, this rapid digitization has also increased the sector’s exposure to a wide array of security risks, including cyberattacks, fraudulent activities, data breaches, and insider threats. As traditional security measures struggle to keep pace with the growing sophistication of these threats, machine learning (ML) techniques have emerged as promising tools for reinforcing security in fintech systems. Machine learning models can analyze vast amounts of data, detect anomalous behavior, predict potential risks, and respond to security threats in real-time. This paper explores the potential of machine learning to enhance security in financial technology by addressing key challenges such as anomaly detection, fraud detection, intrusion detection systems (IDS), and risk management. We provide an overview of common machine learning algorithms—such as decision trees, neural networks, support vector machines (SVM), and clustering methods—that are applied to these security tasks. Additionally, we discuss the evaluation metrics used to measure the accuracy, precision, recall, and overall effectiveness of these models. Through real-world case studies, we highlight successful implementations of machine learning in fintech security, offering insights into best practices and lessons learned. Despite the many benefits, there are also significant challenges and limitations associated with the adoption of machine learning in fintech, including concerns over data privacy, the accuracy and reliability of models, and the computational resources required for large-scale deployment. Looking ahead, this paper identifies emerging trends and potential innovations that could further enhance security in fintech, from advancements in deep learning to the integration of artificial intelligence with blockchain technology. Finally, we propose areas for future research to address unresolved issues and support the continued development of machine learning-driven security solutions in the fintech industry.

The Application of Near Field Communication (NFC) in Public Transportation: Current Practices, Challenges, and Future Prospects

In the field of public transportation, the introduction of NFC technology can realize contactless payment and automatic ticket checking functions, significantly improving system efficiency and passenger experience. The article first reviews the development history of the public transportation system and the basic principles of NFC technology, and then analyzes the technical architecture and working principle of the NFC payment system. Through the cases of public transportation systems in many cities around the world, the practical application and effect of NFC technology are demonstrated. This article also verifies the transaction speed, stability and user satisfaction of NFC technology in the public transportation system through a series of experimental evaluations. The results show that NFC technology has excellent performance in terms of processing speed, system stability and user satisfaction, especially during peak hours, its advantages are more obvious. At the same time, the research also pointed out the limitations of current NFC technology applications and the challenges faced in practical applications. In the future, NFC technology will become more complete and is expected to provide passengers with a more personalized and intelligent service experience.

Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks

This paper presents a novel approach to predicting shot accuracy in badminton by analyzing Quiet Eye (QE) metrics such as QE duration, fixation points, and gaze dynamics. We develop a neural network model that combines visual data from eye-tracking devices with biomechanical data such as body posture and shuttlecock trajectory. Our model is designed to predict shot accuracy, providing insights into the role of QE in performance. The study involved 30 badminton players of varying skill levels from the Chinese Swimming Club in Singapore. Using a combination of eye-tracking technology and motion capture systems, we collected data on QE metrics and biomechanical factors during a series of badminton shots for a total of 750. Key results include: (1) The neural network model achieved 85% accuracy in predicting shot outcomes, demonstrating the potential of integrating QE metrics with biomechanical data. (2) QE duration and onset were identified as the most significant predictors of shot accuracy, followed by racket speed and wrist angle at impact. (3) Elite players exhibited significantly longer QE durations (M = 289.5 ms) compared to intermediate (M = 213.7 ms) and novice players (M = 168.3 ms). (4) A strong positive correlation (r = 0.72) was found between QE duration and shot accuracy across all skill levels. These findings have important implications for badminton training and performance evaluation. The study suggests that QE-based training programs could significantly enhance players’ shot accuracy. Furthermore, the predictive model developed in this study offers a framework for real-time performance analysis and personalized training regimens in badminton. By bridging cognitive neuroscience and sports performance through advanced data analytics, this research paves the way for more sophisticated, individualized training approaches in badminton and potentially other fast-paced sports. Future research directions include exploring the temporal dynamics of QE during matches and developing real-time feedback systems based on QE metrics.

Learners’ attitudes towards blended learning at Nguyen Tat Thanh University

In many educational settings accross the world, including Vietnam, blended learning has grown quickly due to evidence of its benefits over traditional classroom or online instruction alone. The study examined the attitudes of students at Nguyen Tat Thanh University toward the existing English blended learning course with the overall goal of enhancing the quality of language teaching and learning. A total of 367 students non-majoring in English language participated in the survey conducted at Nguyen Tat Thanh University in Ho Chi Minh City, Vietnam. The 44-item survey was used to collect the data, and SPSS version 20.0 was used for statistical analysis. The results demonstrated that most students had very positive sentiments towards four factors: the course's overall quality, the quality of the instructors, the course material, and the supportive services. However, a few students expressed dissatisfaction with the technology system, as they frequently encounter technical issues when engaging in online learning outside of the classroom.

Modeling and Performance Analysis of Variable Cycle Engine with Ceramic Matrix Composite Turbine Blades

To meet the requirements of future aircraft for power systems, the turbine inlet temperatures of aero engines are gradually increasing. Ceramic matrix composite (CMC), with its higher thermal limit, has become the preferred material for the turbine blades of variable cycle engines (VCEs). However, the impact of CMC turbine blades on the performance of a VCE is still unknown. In this research project, the comprehensive cooling-efficiency characteristics of CMC are determined through a fluid–solid coupling calculation; a cooling calculation model for turbine blades is established, and cooling airflow solution and control technology (CSCT) for an air system is developed. Additionally, a VCE simulation model is established to analyze the influence of CMC turbine blades on the cooling airflow of the air system and the overall performance of the engine. The results show that, for the design condition, the CMC turbine blade can reduce the cooling airflow of the air system by approximately 10%, and the net thrust is increased by 6.07–7.98%. For the off-design conditions, with the CSCT, the specific fuel consumption can be reduced by 3.06–5.73% while ensuring that the engine net thrust remains unchanged. A comprehensive analysis of the performance for both the design point and off-design points indicates that the use of CMC for high-pressure turbine (HPT) guide vanes and rotor blades yields significant performance benefits, while the performance improvement from the use of CMC for low-pressure turbine (LPT) rotor blades is minimal.

Complementarities in R&amp;D and Innovation Decisions: An Enquiry on an Extensive Sample of Latin American Companies

This study uses an extensive sample of company data referring to seven Latin American countries to analyze the potential influence of simultaneity and heterogeneity in determining decisions to commit to R&amp;D and innovation outputs. First, the analysis focuses on the decisions about spending on in-house and external research. Second, the outcomes of three different innovations: process, product, and organizational. Choices were considered simultaneously by employing a multivariate probit estimation model. In so doing, the enquiry considered the possible systematic interdependencies among the decisions. The results indicate that the two R&amp;D decisions are interdependent. However, a different picture emerged in relation to innovation outputs, in which the correlations coefficients were small with unexpected signs. This may indicate a structural weakness of the technological production system in Latin America, wherein the synergies, complementarities, and complex relationships usually involved in the process of innovation are not fully developed.

The physician-AI relationship: partnering for precision medicine via clinical decision support

Abstract Background Clinical decision support systems (CDSSs) can enhance physician decision-making, but successful adoption depends on positive user attitudes. Understanding Israeli physicians’ attitudes toward CDSSs is crucial for overcoming implementation barriers and refining training. Methods Semi-structured interviews explored knowledge, experiences, perceived benefits/drawbacks, and potential barriers/facilitators to CDSS use among 25 Israeli physicians across specialties, roles, settings, and experience levels selected via stratified sampling. Individual 45-minute video interviews were conducted in January-July 2023. Results Familiarity and CDSS experience varied substantially across medical specialties. While risk calculators, information systems, and expert systems disseminating contemporary research insights are widespread, AI-based system adoption is relatively low outside radiology. Physicians expressed positive attitudes, recognizing CDSSs’ potential to enhance clinical work, reduce errors, and alleviate burdens. However, concerns included AI systems’ impact on workload, patient relationships, skill erosion, and overreliance. Key implementation barriers involved technological infrastructure, organizational processes, physician perceptions, and system features. Facilitating factors included tailored design, focused training, continuous accuracy/reliability validation, and clear autonomy/liability guidelines. Conclusions Addressing key concerns around transparency, trustworthiness and responsible use is crucial for AI-CDSS to fulfill its promise of enhancing quality and outcomes. Insights from Israeli clinicians provide guidance on priorities like transparency of data and algorithms as well as ensuring AI behaves ethically. Collaboration among technology designers, healthcare professionals and stakeholders can help develop systems that physicians trust to improve care while safeguarding patients. Ongoing evaluation is also needed to optimize AI-CDSS over time. Key messages • Understanding physician attitudes is key to overcoming barriers to clinical decision support system adoption and optimizing training protocols. • Addressing transparency, trustworthiness, and ethical considerations around AI clinical decision support systems is crucial for enhancing quality care.

A Strategy for Predicting the Performance of Supervised and Unsupervised Tabular Data Classifiers

AbstractMachine Learning algorithms that perform classification are increasingly been adopted in Information and Communication Technology (ICT) systems and infrastructures due to their capability to profile their expected behavior and detect anomalies due to ongoing errors or intrusions. Deploying a classifier for a given system requires conducting comparison and sensitivity analyses that are time-consuming, require domain expertise, and may even not achieve satisfactory classification performance, resulting in a waste of money and time for practitioners and stakeholders. This paper predicts the expected performance of classifiers without needing to select, craft, exercise, or compare them, requiring minimal expertise and machinery. Should classification performance be predicted worse than expectations, the users could focus on improving data quality and monitoring systems instead of wasting time in exercising classifiers, saving key time and money. The prediction strategy uses scores of feature rankers, which are processed by regressors to predict metrics such as Matthews Correlation Coefficient (MCC) and Area Under ROC-Curve (AUC) for quantifying classification performance. We validate our prediction strategy through a massive experimental analysis using up to 12 feature rankers that process features from 23 public datasets, creating additional variants in the process and exercising supervised and unsupervised classifiers. Our findings show that it is possible to predict the value of performance metrics for supervised or unsupervised classifiers with a mean average error (MAE) of residuals lower than 0.1 for many classification tasks. The predictors are publicly available in a Python library whose usage is straightforward and does not require domain-specific skill or expertise.

The Level of Accessibility of e-Feedback System Technology among Citizens of Temeke Municipal Council in Dar es Salaam, Tanzania

The study sought to investigate effectiveness of e-feedback system among citizens of Temeke Municipal Council in Dar es Salaam, Tanzania. The specific objectives of the study were to evaluate level of accessibility of e-feedback system technology among citizens of Temeke Municipal Council, to assess the level of digital skills and its influence on e-feedback system usage among citizens of Temeke Municipal and to assess the level of motivation and its influence on the usage of e-feedback system among citizens of Temeke Municipal. The study was guided by decomposed theory of planned behaviour while conceptual framework was developed to indicate the relationship between independent variables and dependent variable. The study used explanatory research design while simple random sampling technique was used to select members of sample size who were 133 respondents from the target population of 199 people. Data were collected through questionnaire while data were analyzed using descriptive and multiple regression analysis with the aid of SPSS. The study found that there is low level accessibility of e-feedback system technology among citizens of Temeke Municipal Council since most of the citizens are not aware of the existence of the system while level of digital skills on e-feedback system among citizens of Temeke Municipality is low. The study concluded that to a large extent there is low usage of e-feedback system due to the low-level awareness of the existence of the system. It is recommended that the management of the organisation should conduct widespread public awareness campaigns to inform citizens about the existence and benefits of the e-feedback system at large.

An overview of machine learning applications in elastic power systems

Abstract. With the development of economy and the access of renewable energy, the power system is facing new challenges. Machine learning provides a new way for the intelligent management of power system through big data analysis, pattern recognition and predictive modeling. This paper reviews the application of machine learning technology in elastic power system, including load forecasting, transient stability assessment, intelligent scheduling and fault diagnosis. Machine learning will better improve the stability, reliability and economic benefit of the system. Future research will focus on algorithm optimization, model adaptability improvement and integration with traditional power system knowledge to promote the development of power system to a higher level of intelligence and automation.

Epidemiological analysis of 2106 geriatric trauma patients in a level I trauma center in Lanzhou City, Gansu Province, China.

As the global population ages, geriatric trauma gains attention. Limited studies on geriatric trauma in developing countries necessitate comprehensive investigation. This study aims to delineate the epidemiological characteristics of geriatric trauma, providing insights for prevention and enhanced elderly trauma care. Clinical data of 2106 geriatric trauma patients (age ≥ 60) treated from November 1, 2020, to October 31, 2023, at the trauma center of the First Hospital of Lanzhou University were retrospectively analyzed. Descriptive statistics covered temporal and monthly distribution, trauma sites, causes, and length of stay. Inclusive were 2106 geriatric patients (941 males, 1165 females; male-to-female ratio 1:1.24). Trauma incidence was higher in summer and autumn (May-October) than winter (November-December). Peak incidents occurred between 14:00-16:00, with concentrations at 12:00-16:00 and 20:00-22:00. Falls (59.35%), road traffic accidents (16.62%), other traumas (14.10%), cutting/stabbing (4.56%), violence (3.42%), mechanical injuries (1.61%), winter sports injuries (0.14%), burns (0.14%), and animal bites (0.06%) were major causes. Extremity and skin injuries prevailed, with significant site distribution differences (P < .001). Average length of stay at the trauma center was 78.35 ± 48.32 minutes. The gender distribution, timing, causes, and sites of trauma in the elderly differ significantly from other age groups. Unlike the higher occurrence of trauma in young men compared to women, among the elderly, it is more prevalent in women than men, mainly attributed to falls. It is crucial to implement specific measures to prevent trauma in the elderly, and the advancement of information technology in the trauma care system could additionally improve the quality of care.

A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision

Abnormal crowd behavior recognition is one of the research hotspots in computer vision. Its goal is to use computer vision technology and abnormal behavior detection models to accurately perceive, predict, and intervene in potential abnormal behaviors of the crowd and monitor the status of the crowd system in public places in real time, to effectively prevent and deal with public security risks and ensure public life safety and social order. To this end, focusing on the abnormal crowd behavior recognition technology in the computer vision system, a systematic review study of its theory and cutting-edge technology is conducted. First, the crowd level and abnormal behaviors in public places are defined, and the challenges faced by abnormal crowd behavior recognition are expounded. Then, from the dimensions based on traditional methods and based on deep learning, the mainstream technologies of abnormal behavior recognition are discussed, and the design ideas, advantages, and limitations of various methods are analyzed. Next, the mainstream software tools are introduced to provide a comprehensive reference for the technical framework. Secondly, typical abnormal behavior datasets at home and abroad are sorted out, and the characteristics of these datasets are compared in detail from multiple perspectives such as scale, characteristics, and uses, and the performance indicators of different algorithms on the datasets are compared and analyzed. Finally, the full text is summarized and the future development direction of abnormal crowd behavior recognition technology is prospected.

Research on Artificial Self-Recovery Technology in Engineering Practice

With the development of mechanical systems in the industrial era, there is an increasing emphasis on the safety and reliability of mechanical equipment. The theory of artificial self-recovery technology has emerged, aiming to enable mechanical equipment to autonomously prevent and repair faults by simulating biological self-recovery mechanisms, thereby enhancing the safety and reliability of industrial production, reducing manual intervention, and promoting the intelligent development of manufacturing. The article mainly explores the application of artificial self-recovery theory in mechanical equipment, elaborating on the achievements in self-recovery technologies such as automatic balancing technology, compensation and self-protection technology for large systems, equipment health assistive technology, and active control technology for sealing devices and hydrostatic bearings, as well as self-repair and self-cleaning technologies. Finally, the paper looks forward to the future development of artificial self-recovery technology, believing that with technological advancements it will play an increasingly important role in the industrial field and promote the development of manufacturing towards self-recovery.

3.8 µm pulse burst self-optical parametric oscillator utilizing a programmable step-active Q-switch with Nd:MgO:PPLN crystal

This paper reports a 3.8 µm pulse burst self-optical parametric oscillator (SOPO) employing the Nd:MgO:PPLN crystal, achieving programmable mid-infrared pulse burst output based on step-active Q-switching technology. Building on the intracavity optical parametric oscillator (IOPO) theory, a theoretical model for the step-active Q-switched self-optical parametric oscillator is developed by introducing idler photon and step loss terms. The simulation results elucidate the evolution of population inversion and photon numbers and determine step-active Q-switching loss values for different sub-pulse numbers. Additionally, a 3.8 µm pulse burst laser output with a repetition rate of 10 kHz is experimentally achieved using the step-active Q-switching signal designed from the theoretical simulation. The effective programming of the step-active Q-switching signal achieves control over 2-4 sub-pulses, 260-1000 ns intervals, and any amplitude ratios. The experimental and simulation results demonstrate consistency, offering valuable insights for optimizing the Q-switching technology in other SOPO systems.

A Comprehensive Geophysical Exploration of Sedimentary Exhalative Deposits: An Example from the Huaniushan Lead–Zinc–Silver Polymetallic Deposit in Gansu, China

The Huaniushan lead–zinc–silver deposit is a hydrothermal sedimentary exhalative deposit (SEDEX), and the mining area has complex geological conditions, with the main tectonic structure being the Huaheitan–Shuangfengshan Fault (F3), which controls the distribution of strata and magmatic rocks. Since the discovery of the Huaniushan lead–zinc–silver deposit, diverse interpretations of its genesis and metallogeny have been proposed, making it challenging to establish a definitive geological explanation. Moreover, using a single geophysical exploration method relies on limited rock physical parameters, making it difficult to effectively characterize underground structures. The combined use of multiple geophysical methods can effectively integrate the geophysical characteristics of different rock physical parameters, reducing the multiplicity and uncertainty of the inverse interpretation of geophysical data. The comprehensive interpretation of three-dimensional inversion based on various geophysical data, the construction of geological–geophysical models on geological grounds, the establishment of hidden ore exploration and positioning, and the rapid evaluation of geophysical technological systems are the current research trends in mineral exploration. In light of this, in this study, we conducted research on the three-dimensional inversion interpretation of gravity and magnetoelectric exploration data of the Huaniushan sedimentary exhalative lead–zinc–silver polymetallic deposit and constructed a three-dimensional geological–geophysical model of the study area based on the obtained three-dimensional physical structure of the underground density, magnetization intensity, resistivity, and polarizability of the study area, in combination with related geological and drilling hole data. Finally, we comprehensively interpreted the favorable mineralization sites in the study area.

The design of a model predictive control strategy and performance analysis for pumped storage units and super capacitors in power systems

IntroductionSuper capacitors were regarded as one of the most effective frequency regulation technologies in power systems. The major issue of applying super capacitors for frequency regulation is the limited energy capacity and the high construction costs. The pumped storage units have been developed and applied in power systems for decades, whereas it was rarely operated to regulate the system frequency due to the operation limitations. The pumped storage units could be operated as a hydro generator under the generation mode and provide effective frequency regulation service with the conventional automatic generation control (AGC) system while its output power was constant under the pumping mode.MethodIn this paper, the pumped storage unit was controlled to participate in frequency regulation under the generation mode with super capacitors. Considering the AGC system could not consider the state of charging (SOC) of super capacitors for frequency regulation, an optimization model was proposed to determine the operation points for super capacitors and pumped storage units under a model predictive control (MPC) strategy to regulate the system frequency for power systems.Results and DiscussionBased on the various operation scenarios, the effectiveness and costs of pumped storage units and super capacitors to provide frequency regulation services were discussed. It was indicated that pumped storage units and super capacitors were effective technologies for power systems.