Analysis Of System Research Articles

Developing an Understanding of Artistic Imagery through the Analysis of Character Systems in Literary Works

This article explores the importance of a systematic analysis of characters in literary works for a deeper understanding of the content and the author’s intent. Various approaches to classifying characters (main, secondary, episodic) and their roles in creating the philosophical, social, and psychological dimensions of the text are examined. Using examples from Russian classical literature, the study analyzes how the character system helps to reveal central themes and conflicts, highlighting the layers and depth of the work. Recommendations are provided for employing character system analysis in educational settings, promoting critical thinking and analytical skills among students.

Advancing hyperspectral imaging techniques for root systems: a new pipeline for macro- and microscale image acquisition and classification

Abstract Background Understanding the environmental impacts on root growth and root health is essential for effective agricultural and environmental management. Hyperspectral imaging (HSI) technology provides a non-destructive method for detailed analysis and monitoring of plant tissues and organ development, but unfortunately examples for its application to root systems and the root-soil interface are very scarce. There is also a notable lack of standardized guidelines for image acquisition and data analysis pipelines. Methods This study investigated HSI techniques for analyzing rhizobox-grown root systems across various imaging configurations, from the macro- to micro-scale, using the imec VNIR SNAPSCAN camera. Focusing on three graminoid species with different root architectures allowed us to evaluate the influence of key image acquisition parameters and data processing techniques on the differentiation of root, soil, and root-soil interface/rhizosheath spectral signatures. We compared two image classification methods, Spectral Angle Mapper (SAM) and K-Means clustering, and two machine learning approaches, Random Forest (RF) and Support Vector Machine (SVM), to assess their efficiency in automating root system image classification. Results Our study demonstrated that training a RF model using SAM classifications, coupled with wavelength reduction using the second derivative spectra with Savitzky-Golay (SG) smoothing, provided reliable classification between root, soil, and the root-soil interface, achieving 88–91% accuracy across all configurations and scales. Although the root-soil interface was not clearly resolved, it helped to improve the distinction between root and soil classes. This approach effectively highlighted spectral differences resulting from the different configurations, image acquisition settings, and among the three species. Utilizing this classification method can facilitate the monitoring of root biomass and future work investigating root adaptations to harsh environmental conditions. Conclusions Our study addressed the key challenges in HSI acquisition and data processing for root system analysis and lays the groundwork for further exploration of VNIR HSI application across various scales of root system studies. This work provides a full data analysis pipeline that can be utilized as an online Python-based tool for the semi-automated analysis of root-soil HSI data.

Sensitivity Analysis of a Nuclear Hybrid Energy System With Thermal Energy Storage in Deregulated Electricity Markets Considering Time Series Uncertainty in Electricity Price

ABSTRACTAdding thermal energy storage to nuclear power plants has been proposed as a way to allow nuclear plants to operate more flexibly and potentially be more competitive in deregulated electricity markets. The economics of these systems in deregulated markets are subject to uncertainties in capital costs, operating costs, and revenue. This study quantifies the uncertainty in the net present value of a nuclear power plant with integrated thermal energy storage in three U.S. deregulated electricity markets considering these sources of uncertainty and quantifies, for the first time, the relative contributions each source makes to the overall uncertainty. To accomplish this, a computationally efficient block bootstrap method is introduced to quantify uncertainty contributions from the stochastic time series of electricity prices, achieving a two order of magnitude decrease computational time compared to the model‐based methods used in previous works while also relaxing several strict assumptions made by the model‐based approach. Up to 18.5% of the overall variance in net present value is attributable to variance in the electricity price stochastic process, with this sensitivity varying significantly across markets.

Triangular neutrosophic set and its application to reliability analysis of gas turbine system

Triangular neutrosophic set and its application to reliability analysis of gas turbine system

Gigaton gear – policy insights for scaling up the global deployment of direct air carbon capture and sequestration technology (DACCS)

ABSTRACT Direct Air Carbon Capture and Sequestration (DACCS) is increasingly recognized as a pivotal technology for mitigating climate change by removing carbon dioxide (CO2) from the atmosphere and storing it securely. Despite significant technical potential, the current global capacity for DACCS is minimal, emphasizing the urgent need for accelerated deployment to meet climate targets. While there are technical intricacies, an accelerated deployment is substantially dependent on setting the right policy frameworks. This paper presents a comprehensive analysis of the global DACCS innovation system using the Technological Innovation Systems (TIS) framework, focusing on identifying systemic policy problems and suggesting policy leverage points. Through 34 semi-structured interviews with 37 key actors across Europe and North America, this study captures the current state and challenges within the DACCS innovation system. It offers insights on the status quo concerning entrepreneurial activities, knowledge creation and diffusion, supportive policy, market formation, resource mobilization, as well as public and local legitimacy issues. To advance the development of the DACCS innovation system towards achieving gigaton-scale carbon removal, the paper provides targeted insights for policymakers, such as creating clearer policy frameworks and incentives, securing demand, and investing more into research, as well as knowledge creation and knowledge diffusion. By pointing out these systemic issues and creating policy insights, the paper contributes valuable insights to scale DACCS technologies globally.

Characteristic analysis method for integrated multi-parameter hydro-viscous speed control system

Hydro-viscous clutch has become an inevitable choice for special vehicle transmissions. As a nonlinear dynamic system with large lagging link, its timing performance is affected by input rotational speed, lubricating oil temperature and pressure and other factors. However, from the control perspective, the speed regulation law, formation mechanism control characteristics, global model of hydro-viscous speed control system (HSCS) are unclear. To solve these problems, this paper presents a comprehensive analysis of the Hydro-viscous Speed Control System (HSCS), focusing on its steady-state and dynamic speed control characteristics. A data-driven model is established to describe the relationship between input rotational speed, output speed, control oil pressure, and lubricating oil temperature. The findings provide a foundation for optimizing HSCS structure and parameters, enhancing the performance and reliability of such systems in special vehicle transmissions and establishing a temperature-speed control system for special vehicle transmissions.

Construction and performance analysis of DC servo system based on operational amplifier

Abstract. The motivation for researching modelling the severe motor system is its frequent use in applications that demand accurate control of position, speed, or force. Examples include robots and CNC machines, extensively employed and advanced in modern manufacturing processes. The application field of this technology is expanding significantly due to the increasing demand for automation in modern times. In order to achieve seamless integration of the system, using Op-Amp as the central element for signal processing and control is crucial. This research aims to determine the methodology for utilising an operational amplifier (Op-Amp) to construct a model for a direct current (DC) servo motor. Additionally, the research aims to develop a control system and do circuit board simulations.The outcome of this research is to implement theoretical principles into a design model, identify the most appropriate system for this model and develop the corresponding circuit board. This study aims to incorporate a control system to regulate server motors, effectively enhancing the automation process.

Operation Data Analysis and Performance Optimization of the Air-Cooled System in a Coal-Fired Power Plant Based on Machine Learning Algorithms

Operation Data Analysis and Performance Optimization of the Air-Cooled System in a Coal-Fired Power Plant Based on Machine Learning Algorithms

Dynamic Characteristics, Analysis, and Measurement of a Large Optical Mirror Processing System

Dynamic Characteristics, Analysis, and Measurement of a Large Optical Mirror Processing System

Thermodynamic and Economic Analysis of a High-efficiency System Integrating ORC Power Generation, CO2 Capture, and CO2 Storage Driven by Complementary LNG Cold Energy and Flue Gas Waste Heat.

Thermodynamic and Economic Analysis of a High-efficiency System Integrating ORC Power Generation, CO2 Capture, and CO2 Storage Driven by Complementary LNG Cold Energy and Flue Gas Waste Heat.

Reliability and sensitivity analysis of a maintainable energy system under priority repair

Reliability and sensitivity analysis of a maintainable energy system under priority repair

Modeling EV dynamic wireless charging loads and constructing risk constrained operating strategy for associated distribution systems

Dynamic wireless charging (DWC) is an emerging technology that enables the charging of electric vehicles (EVs) while they are in motion. However, previous load modeling methods have not thoroughly explored the detailed analysis of DWC load characteristics. Existing research only considers the single-node supply mode for dynamic wireless charging roads (DWCRs), and the assessment of operational risks arising from the uncertain DWC loads has not been addressed. This paper begins by conducting an equivalent circuit analysis of a typical EV DWC system with multiple segmented coils. We present a more accurate trapezoidal power model for a single EV. Subsequently, we model the aggregated EV DWC load, accounting for traffic flow and headway using Poisson and negative exponential distribution functions, respectively. In the operation process, we consider a multi-node supply mode for DWCRs. To address the inaccuracy of long-term predictions, we propose a rolling optimization model to coordinate DWC and renewables with heterogeneous uncertainties by introducing a risk metric to manage potential uncertain risks. The proposed optimization model is transformed into a mixed-integer second-order cone programming (MISOCP) problem after convex relaxation. Finally, we conduct case studies to validate the proposed methods.

Set pair analysis and system dynamics coupling approach for structure simulation and variation trend evaluation of water resources spatial equilibrium system

AbstractThe implement of water resources spatial equilibrium (WRSE) schemes is fundamental task of integrated water resources management in China, in which, the evaluation and simulation analysis of WRSE system is of great concern for understanding the overall variation and feedback characteristics of WRSE system. Therefore, we utilized the ordered degree, entropy and connection number coupling model to evaluate the variation of WRSE system, and also employed system dynamics (SD) and scenario simulation integrated method to reveal the feedback characteristics between different equilibrium variables and subsystems, and thus the set pair analysis‐SD based approach for structure simulation and variation trend evaluation of WRSE system was constructed. The application results in Anhui province, China demonstrated that, the overall variation of provincial spatial equilibrium situation of WRSE system presented obvious improving trend, 2009–2019, the index of ordered degree and connection number entropy in Hefei, increased from the minimum of 0.6434 (Grade 3, unequilibrium) to maximum of 0.9985 (Grade 1, equilibrium). Moreover, the future variation of WRSE system will display stable improving trend during 2020 to 2029, in which, annual water resources availability and water resources utilization efficiency will have significant influence on WRSE system. And the research findings can be favorable to formulate water resources development and utilization strategies.

Raman Quantitative Measurement on the Cl− Molarity of H2O-NaCl-CO2 System: Application to Fluid Inclusions

In this study, Raman spectroscopy is applied to determine the salinity of fluid inclusions in the H2O-NaCl-CO2 system. In the work, various systems are prepared, such as H2O-NaCl, H2O-CO2, and H2O-NaCl-CO2. For the H2O-NaCl system, the addition of NaCl salts decreases the intensity of the sub-band below 3330 cm−1 but increases the intensity of the sub-band above 3330 cm−1. According to the structural analysis of the H2O-NaCl system, the spectral changes are mainly related to the interactions between Cl− and water. After the Raman OH stretching bands are fitted into two sub-bands, the intensity ratio between them is used to calculate the Cl− concentrations (molarity scale) of NaCl solutions. Additionally, based on the measured Raman spectra, the effects of CO2 on water structure may be weak. It is reasonable to ignore the impact of dissolved CO2 on Raman OH stretching bands. The procedure above can be extended to quantitatively determine the Cl− molarity of the H2O-NaCl-CO2 system. To demonstrate its reliability, this method is applied to determine the salinity of synthetic and natural fluid inclusions containing CO2.

Teaching agile: A case study approach with an optional game

Learning Agile philosophy is essential for system analysis and design or software development courses. Organizations operate in highly dynamic environments, constantly needing to adapt, change, or create innovative Information Systems. Agile provides guiding values and principles that enable systems analysts to support organizations in responding to change and developing, modifying, or updating information systems. By learning Scrum, an Agile-inspired framework, students can assume the role of systems analyst, organizing activities and working in teams iteratively to solve complex problems using Agile principles. This hands-on approach helps students understand the importance of stakeholder involvement, constant scrutiny, and adjustment to achieve frequent system updates. Hence, this case study immerses students in a scenario where they apply the Scrum framework, with an optional puzzle game to introduce students to agile philosophy through its manifesto. Learning by doing has proven effective for complex or dry topics, with students responding positively to this method. Interview data collected at the end of the activity showed high student satisfaction and a strong sense of knowledge acquisition while enjoying the class. Additionally, integrating a game to internalize agile concepts with a teaching case to apply concepts presents a rich educational approach that can be adapted to other Information Systems topics.

Uncovering migration systems through spatio-temporal tensor co-clustering

A central problem in the study of human mobility is that of migration systems. Typically, migration systems are defined as a set of relatively stable movements of people between two or more locations over time. While these emergent systems are expected to vary over time, they ideally contain a stable underlying structure that could be discovered empirically. There have been some notable attempts to formally or informally define migration systems. However, they have been limited by being hard to operationalize and defining migration systems in ways that ignore origin/destination aspects and fail to account for migration dynamics over time. In this work, we propose to employ spatio-temporal tensor co-clustering—that stems from signal processing and machine learning theory—as a novel migration system analysis tool. Tensor co-clustering is designed to cluster entities exhibiting similar patterns across multiple modalities and thus suits our purpose of analyzing spatial migration activities across time. To demonstrate its effectiveness in describing stable migration systems, we first focus on domestic migration between counties in the US from 1990 to 2018. We conduct three case studies on domestic migration, namely, (i) US Metropolitan Areas, (ii) the state of California, and (iii) Louisiana, in which the last focuses on detecting exogenous events such as Hurricane Katrina in 2005. In addition, we also examine a case study at a larger scale, using worldwide international migration data from 200 countries between 1990 and 2015. Finally, we conclude with a discussion of this approach and its limitations.

Enhancing User Experience in Private Bank Mobile Banking: Insights from Management Information System Analysis

This research explores the impact of Management Information Systems (MIS) on user experience in private bank mobile banking, focusing specifically on the effectiveness of m-BCA. Utilizing a descriptive quantitative approach, the study aims to understand how mobile banking features, including accessibility, user interface, security, and functionality, influence user satisfaction. Data were collected through direct experimentation with the m-BCA application, complemented by analysis from secondary data sources and a survey of 30 users. Results indicate that 93.3% of users find m-BCA effective for routine banking needs, attributing high ratings to its convenience and functionality. However, while 80% of respondents appreciate the current security measures, 20% cite the absence of advanced security features such as fingerprint and facial recognition as a drawback. The study also reveals mixed feedback on the user interface; while the straightforward design aids usability, 10% of users express a desire for a more visually appealing interface. Despite these areas for improvement, m-BCA’s comprehensive set of features is generally seen as effective, supporting users in performing essential transactions with ease and reliability. The findings suggest that enhancing interface aesthetics and integrating advanced security options could further boost user engagement and trust in mobile banking applications, underscoring MIS’s role in meeting evolving customer expectations in digital banking.

Hazard, risk, reliability assessment and improvement of Darlington Tritium Removal Facility (DTRF) cryogenic refrigeration system

Tritium is a radioactive isotope of hydrogen, produced in heavy water, during CANDU reactor operations by neutron absorption. The tritium removal facility at Darlington (DTRF) is used to extract tritium from the heavy water on a continuous basis and concentrates it in preparation for immobilization and storage. There are several internal and external hazards associated with the operation of DTRF, such as fires, onsite transportation accidents, exposure to radioactive materials, explosion due to Hydrogen (H2) and the release of radioactive substances from the Tritium Immobilization System. This paper conducts a risk analysis for the DTRF Cryogenic Refrigeration System, associated with both Low and High Tritium Distillation columns. This study includes hazard identification, consequence analysis and risk estimation. Fault Tree and Event Tree Analysis conducted, system reliability, availability and human reliability was considered. Consequently, methods for risk reduction and integration with improvement to CRS design is discussed.

Enhancing the performance of solar-powered EV charging stations using the TOSSI-based CTF technique

In this paper, the design and analysis of a novel solar-powered EV-charging system employing a third-order sinusoidal signal integrator (TOSSI) based-CTF (character of triangular function) is proposed. The TOSSI-based CTF is used to extract fundamental active components by eliminating harmonic distortions from the load currents. This control structure has the unique capability of active current separation, employing simple mathematical operations. Moreover, the response is further refined with the help of optimized gain parameters. The designed system is capable of handling various power quality issues, including harmonic mitigation, current balancing, and power factor improvement. The EV battery is primarily charged by solar-PV power employing a bidirectional DC-DC converter. Alternatively, the EV battery may be charged by the grid supply during the unavailability of sunlight by taking the power quality issues into due consideration. The suggested control topology is used to enhance the dynamic operation of solar-powered EV charging stations experiencing solar power intermittency and variation of load. Using MATLAB, the efficacy of the proposed control topology is tested for different operating scenarios. The suggested control topology is also verified and validated using prototype hardware developed in the laboratory, where the suggested controller has proven its utility over and above existing state-of-the-art controllers in the domain.

An Approach to Multivariable Regression Analysis of a Rooftop PV System in Households’ Sector in Tirana. A Novel Approach using Energy Modeling Tool

Solar power prediction plays an essential role in functioning, mapping, and obtaining energy and climate goals in 2030 and beyond and contributing to real-time balancing of the power system. On the other side, electricity consumption is influenced by Heating Degree Days (HDD), Cooling Degree Days (CDD), average monthly temperature, energy management, human behaviour, architecture, orientation, and many other factors. The simulations are performed based on three consecutive energy consumption data for a typical dwelling in Tirana city. Once the base case scenario is designed, the model is validated based on the monthly electricity bills. In the base case scenario are included some energy efficiency measures (EEM) afterward the optimization of the supply side using PV and Solar Water Heating is part of our simulation, too. The focus of our work is to design a correlation between the relationship between consumption and generation of electricity given as a dependent variable (Y), which is regressed with weather parameters given as independent variables (Xi) under Durbin Watson statistics. The output of the study can help designers to compile a reliable power system, better utilization of energy resources, and forecasting accuracy analysis from both sides of the energy system (demand and supply side). The tested household and EEM applied in the proposed scenario may lead to an electricity reduction level of 8655 kWh per year and 76.6 % of solar fraction is used to meet the hot water demand.