Analysis Of Systems Research Articles

Performance and economic analysis of various geothermal power plant systems: Case study for the Chingshui geothermal field in Taiwan

Geothermal power plants are a source of sustainable energy. However, their performance is influenced by various factors, including production temperature and well locations, and few studies have investigated the relationships between these factors. To address this research gap, the present study developed integrated models of a geothermal reservoir and a variety of power plant systems. By using COMSOL and MATLAB, we conducted simulations for the Chingshui geothermal field in Taiwan to investigate the performance and economic feasibility of five common types of geothermal power plant systems over their operational lifetime under different well configurations. The key findings of this investigation are as follows. First, the locations of production wells are a primary factor influencing production temperature. Constructing wells in low-permeability zones results in considerable increases in the pressure differential in the injection pump; thus, wells should not be constructed in such zones. Second, as system flow rate and well depth decrease, production temperature remains relatively stable, net power output decreases, and system efficiency increases. This efficiency increase is caused by decreases in pumping power requirements at lower flow rates and shallower well depths. Finally, among the investigated systems, the organic Rankine cycle system had the lowest electricity production cost and shortest payback period within the enthalpy range of 500–1000 kJ/kg. Double-flash (DF) and DF–binary systems are also viable options when the mass flow rate is 10–20 kg/s and the enthalpy is 700–800 kJ/kg. Overall, the results of this study offer valuable insights for the design of geothermal power plants, thereby contributing to the optimization of renewable energy systems.

Mathematical Modeling and Indirect Carbon Emission Reduction Analysis of Urban Wastewater Treatment Systems Under Different Temperature Conditions

In the context of achieving the two-carbon target, this study utilized a wastewater treatment plant in Shenyang City as a case study to accurately calculate indirect emissions related to energy and chemical consumption within the energy-intensive wastewater treatment industry. Sumo software was employed for precise mathematical modeling. Considering the operational characteristics of wastewater treatment plants in cold regions, this study innovatively divided the annual operation cycle into two periods, namely normal temperature and low temperature, and determined the optimal operational parameters under a low-carbon mode. The results indicate that precise regulation of dissolved oxygen concentration to 0.5–1.5 mg/L (normal temperature period) and 1–2 mg/L (low temperature period) can significantly reduce carbon emissions related to electricity consumption by 13,781.9 t CO2-eq. From the perspective of chemical consumption, adjusting the dosage of polyaluminum chloride (PAC) to 75% and sodium acetate to 70% during the normal temperature period can lead to a reduction in indirect carbon emissions of 1614.4 t CO2-eq compared to the same period last year. During the low-temperature period, by reducing the dosage of polyaluminum chloride to 80% and sodium acetate to 75%, the indirect carbon emissions can be reduced by 1557.3 t CO2-eq compared to the corresponding period last year. After optimization, USD 1.49 million can be saved. This study simulated the operation conditions of cold-region urban wastewater treatment plants at different times to effectively control carbon emissions resulting from energy and chemical consumption in wastewater treatment. This result can provide innovative ideas for energy saving and carbon reduction in cold-region wastewater treatment plants.

An efficient surrogate model for system reliability with multiple failure modes

AbstractThe failure modes of complex systems are often highly dependent. Aiming at the reliability analysis of engineering systems with multiple failure modes, an efficient surrogate model for system reliability with multiple failure modes is proposed, namely, the Copula Adaptive Kriging surrogate model (CAKSM). First, the initial Kriging model is established based on the initial samples. In order to select more effective added samples to update the Kriging model, a new learning function, namely, Copula Unites with Expected Improvement Function (CUEIF), is established. The function can make a reasonable sample selection by considering the failure correlation. Then, based on the optimal Copula function, the reliability data of each failure mode are coupled and analyzed. In order to adapt to various computing scales, regularization is introduced to improve the prediction speed of the surrogate model, and the probability density function of each performance function is obtained by introducing nonparametric kernel density estimation. Finally, the effectiveness of the CAKSM method and the learning function CUEIF is verified through four examples.

Tackling the multitude of uncertainties in energy systems analysis by model coupling and high-performance computing

This paper identifies and addresses three key challenges in energy systems analysis—varying assumptions, computational limitations, and coverage of a few indicators only. First, results depend strongly on assumptions, i.e., varying input data. Hence, comparisons and robust results are hard to achieve. To address this, we use a broad range of possible inputs through an extensive literature review by scenario experts. Second, we overcome computational limitations using high-performance computing (HPC) and an automated workflow. Third, by coupling models and developing 13 indicators to evaluate the overall quality of energy systems in Germany for 2030, we include many aspects of security of supply, market impact, life cycle analysis and cost optimization. A cluster analysis of scenarios by indicators reveals three recognizable clusters, separating systems with a high share of renewables clearly from more conventional sets. Additionally, scenarios can be identified which perform very positive for many of the 13 indicators. We conclude that an automated, coupled workflow on supercomputers based on a broad parameter space is able to produce robust results for many important aspects of future energy systems. Since all models and software components are released as open-source, all components of a multi-perspective model-chain are now available to the energy system modeling community.

Comparative institutional analysis of the banking systems of Russia and Belarus: a transactional approach

This study presents the results of a comparative institutional analysis of the banking systems of Russia and Belarus based on a transactional approach. These countries were chosen as the object of research for reasons of common factors of historical development, the degree of integration interaction between countries, as well as the availability of common approaches to financial regulation. It is shown that despite the similarity of the evolutionary development of the banking systems of Russia and Belarus, the modern credit markets of these countries differ in such parameters as the powers of the central bank, the degree of competition between banking and non-banking financial markets, the functional purpose and institutional structure of the second level of the national banking system.

Comparative analysis of single plane and biplane angiography systems for mechanical thrombectomy for acute ischemic stroke: a systematic review and meta-analysis

BackgroundThe choice of angiography system could influence the outcomes of mechanical thrombectomy (MT) in the treatment of acute ischemic stroke (AIS), but its impact is not yet well understood. This...

Mission Reliability Modeling and Analysis Methods for Reconfigurable Ship Electronic Information Systems

With the increasing intensity of ship missions, the mission reliability requirements of various ship systems are getting higher. Especially the new reconfigurable ship electronic information systems, which have the characteristics of resource sharing and reuse, function reconfiguration on demand, software and hardware loose coupling, and business integration, etc., is difficult to be taken into account by traditional reliability design and analysis methods. Therefore, it is imperative to design a reliability modeling and analysis method with careful consideration and strong applicability. Taking the new reconfigurable ship electronic information system as the research subject, this paper firstly adopts the architecture analysis and design language (AADL) to establish a reliability model from three perspectives, namely, system structure, system behavior, and failure impact; then, a model of mission reliability allocation under multiple constraints is developed and solved using the hybrid algorithm of Slime Mode Optimization Differential Evolution (SMA-DE) based on the optimal allocation model of reliability; next, a method for analyzing the mission reliability of ship electronic information systems based on the Goal Oriented (GO) method is proposed, and based on the state probability algorithm therein, a correction algorithm oriented to the co-causal failure modes is derived to realize the quantitative calculation of the mission reliability of ship electronic information systems; finally, an application validation was performed in conjunction with a typical ship electronic information system task. The results show that the method proposed in this study can successfully achieve the mission reliability allocation as well as quantitative analysis of reconfigurable ship electronic information systems under multi-mission requirements.

Resilience optimization analysis of smart mining cluster cyber-physical systems based on the NK model

This paper explores the challenges and opportunities presented by the digitization and intelligent clustering in the mining industry, focusing on the security and inter-system influences within Cyber-Physical Systems (CPS) of smart mining clusters. Utilizing the NK model and complex adaptive systems theory, we construct a dual-layered network and cascade failure model, alongside a dynamic repair strategy that adapts to environmental changes. By analyzing the critical parameters of node number (N) and relationship density (K), we assess the resilience of smart mining CPS under cascade failures with dynamic repairs. Our findings reveal that dynamic repair strategies significantly enhance network resilience against both random and deliberate attacks, with N and K acting as crucial regulators of repair effectiveness. This suggests that adapting N and K can improve repair outcomes and system resilience, despite increasing complexity. The paper discusses the practical implications of these strategies through simulation and real-world scenarios, highlighting the need for adaptive repair strategies in enhancing smart mining CPS resilience and process safety. Introducing the NK model to CPS resilience research offers new insights into the resilience of smart mining cyber-physical systems and provides a comprehensive risk analysis method for these systems in complex scenarios.

The Potential of Wood Construction Waste Circularity

Wood construction waste circularity presents enormous potential to significantly de-crease total greenhouse gas (GHG) emissions in the European Union (EU). Latvia could become a frontrunner due to its historic relationship with forestry, wood construction practises and unused potential of the innovative application of wood. This research examines what the potential of “circular wood” in Latvia is, how ready the Latvian wood house construction sector is to engage in a circular economy and wood waste circularity and whether the legal framework is ready to support wood waste management in the country. This study presents a combined approach for systematic wood construction product circularity assessment that includes a review of existing EU and Latvian frameworks for construction and demolition waste (CDW) management and wood construction, a general analysis of wood waste recycling systems and technologies, a quantitative data analysis of construction waste management in Latvia and qualitative data analysis of the Latvian wood house construction sector, and interviews with a focus group of Latvian wood industry representatives. The Latvian scope has allowed us to clarify the pattern methodology and impact points to be replicated, tested and measured further on a broader scale, in other countries, or throughout the whole EU. The main findings reveal a potential life cycle assessment (LCA) verifying the circularity of wood and limitations of wood construction waste circularity in Latvia in terms of wood house construction industry readiness and a legal framework as well as overall social prejudices for circular construction. Findings indicate an overall awareness and level of willingness to participate and engage in the circular construction models among Latvians; however, proactiveness and support (legal and financial) is expected from the government and municipalities. The recommendations point towards improvements in wood waste data management, the wood construction sector and the overall impact on sustainable development goals.

Short-Wave Infrared Upconverting Nanoparticles.

Optical technologies enable real-time, noninvasive analysis of complex systems but are limited to discrete regions of the optical spectrum. While wavelengths in the short-wave infrared (SWIR) window (typically, 1700-3000 nm) should enable deep subsurface penetration and reduced photodamage, there are few luminescent probes that can be excited in this region. Here, we report the discovery of lanthanide-based upconverting nanoparticles (UCNPs) that efficiently convert 1740 or 1950 nm excitation to wavelengths compatible with conventional silicon detectors. Screening of Ln3+ ion combinations by differential rate equation modeling identifies Ho3+/Tm3+ or Tm3+ dopants with strong visible or NIR-I emission following SWIR excitation. Experimental upconverted photoluminescence excitation (U-PLE) spectra find that 10% Tm3+-doped NaYF4 core/shell UCNPs have the strongest 800 nm emission from SWIR wavelengths, while UCNPs with an added 2% or 10% Ho3+ show the strongest red emission when excited at 1740 or 1950 nm. Mechanistic modeling shows that addition of a low percentage of Ho3+ to Tm3+-doped UCNPs shifts their emission from 800 to 652 nm by acting as a hub of efficient SWIR energy acceptance and redistribution up to visible emission manifolds. Parallel experimental and computational analysis shows rate equation models are able to predict compositions for specific wavelengths of both excitation and emission. These SWIR-responsive probes open a new IR bioimaging window, and are responsive at wavelengths important for vision technologies.

On formal modeling, analysis and optimization of reconfigurable manufacturing systems

ABSTRACT This paper deals with the formal specification and multi-objective optimization of reconfigurable manufacturing systems (RMSs). RMSs are characterized by their dynamic structure, crucial for responding to functional variations, adjusting production capacity and overcoming operational dysfuntions promptly and cos-effectively. The effective utilization of this dynamic capability depends on thorough study and optimization of RMS configurations. To tackle this challenge, a novel formalism called genetic reconfigurable object nets (Gen-RONs) is introduced. Gen-RONs formalism integrates the modeling, verification and reconfiguration capabilities of reconfigurable object nets with advanced multi-objective techniques from genetic algorithms. Within Gen-RONs, Petri nets serve to: (i) encode RMS configurations, (ii) manage RMS scheduling and (iii) employ graph transformation theory rules as genetic algorithm operators for real-time system reconfiguration. Thus, Gen-RONs formalism provides a robust framework for optimizing RMSs using genetic algorithms. To ensure deadlock-free RMS configurations, Gen-RONs incorporate a binary tree-based technique. Furthermore, Taguchi’s method is employed to fine-tune genetic algorithm parameters. The contribution of this paper is demonstrated through a case study involving an RMS and the widely used genetic algorithm NSGA-II. Significant gains in terms of the number of solutions (i.e. configurations) and the criteria values are achieved.

On the Strategies and Efficiency of Care and Support Systems for Elderly Across Cantons in Switzerland

The Swiss Federal Council has formulated a policy requiring each canton to establish housing conducive to independent living during old age. Maintaining the independence of older adults at home rather than transitioning to more expensive institutional care has gained traction. This study investigates the heterogeneity of strategies arising from the autonomy granted to each canton. Using mandatory statistics from the Swiss Federal Statistical Office for 2020 on 1550 institutions for the elderly and 2546 at-home care providers, we rank the 26 cantons by permanent and temporary care and support, distinguishing between at-home and institutional care. We use univariate regressions to explain differences and perform efficiency analyses of the respective systems. We find that the French- and Italian-speaking cantons prioritise at-home care, while the German-speaking regions rely more on institutional care. The analyses reveal an inefficient stationary approach in German-speaking cantons, which often places elderly individuals with minimal care needs in institutions. Conversely, with an ambulatory approach favouring independence, cantons like Vaud and Valais prioritize counselling and at-home services, deferring entry into institutions and allowing for short convalescence stays. Our findings are relevant for governmental infrastructure planning and the property owners, investors, and property managers involved in senior housing decisions.

Comparison of Urban Environment Factors for Solar-Powered Vehicles

Abstract. Urban areas face significant environmental challenges, including high CO2 emissions largely attributed to the transportation sector. Introducing solar-powered vehicles (SPVs) presents a promising solution to reduce urban carbon footprints. However, in dense urban settings, the effectiveness of SPVs is often compromised by shading from buildings, which significantly diminishes solar power generation. This study explores the impact of shading on urban roads, aiming to identify roads with substantial shadow variations that affect solar energy harvesting. Using Geographic Information Systems (GIS) and hillshade analysis, the study examines how nearby buildings and road orientation influence shadow variability. The findings reveal considerable variability in shadow throughout the year, with significant fluctuations during transitional months like February, August, October, and November. These results highlight the importance of considering shadow patterns in urban planning and vehicle routing to optimise the use of solar energy in urban settings.

Event‐Triggered Generalized Extended State Observer‐Based Control for Nonlinear Networked Systems Under Gain Variation and Multi‐Channel Attacks

ABSTRACTThis paper investigates the event‐triggered generalized extended state observer‐based estimation issue for a class of nonlinear networked control systems with unmodeled dynamics, external disturbances and multi‐channel attacks. In order to resist different forms of attack threats on multiple communication channels from sensors to the observer, Markov chain is introduced to describe the stochastic switching or jumping behavior between different attack modes. Considering the limited network resources, the measured outputs are transmitted to the observer only when the triggering conditions are met. Moreover, the parameters modeled by the Bernoulli process are adopted to help analyze potential random gain variations of the generalized extended state observer. By employing Lyapunov stability theory and stochastic systems analysis, sufficient conditions are derived to ensure that the augmented system is exponentially bounded in mean square, and the expected observer gains are further determined through linear matrix inequalities. Finally, a numerical example and a simulation related to the RLC series circuit system are conducted, illustrating the proposed method's effectiveness.

Coverage Probability Analysis of a Double IRS Assisted SISO Communication System

Intelligent reflecting surface (IRS) improves wireless communication efficiency and coverage probability. Existing studies focus on IRS deployment but overlook optimal fading channels. This research aims to identify and analyze the most effective channel fading estimation for the double IRS system model. The coverage probability using two-IRS is derived under different fading channels like Rayleigh and Rician to find the optimal solution in a Single-Input-Single-Output (SISO) system. Additionally, a closed-form expression for the coverage probability using statistical channel state information is derived while assuming no direct path between the transmitter and receiver. The analytical results are validated through simulations. It offers valuable insights into the modeling and analysis of double IRS-assisted wireless communication systems.

Convective heat transfer analysis of circular tube-annular tube systems with nanofluids: a comparative study

This study investigates the convective heat transfer performance of various fluid combinations within a circular tube-annular tube system, with a particular emphasis on the influence of nanofluids. The analysis evaluates four distinct fluid combinations: base fluid-base fluid (BW), base fluid-nanofluid (HN), nanofluid-base fluid (CN), and nanofluid-nanofluid (BN). Convective heat transfer coefficients were systematically measured across a range of mass flow rates, and the effects of varying nanoparticle concentrations (ϕ) on heat transfer efficiency were meticulously analyzed. The results indicate a general trend of increasing convective heat transfer coefficients with rising mass flow rates for most fluid combinations, particularly for BW, CN, and BN. Notably, however, a significant decrease in heat transfer efficiency is observed at higher mass flow rates in the HN combination, suggesting a potential saturation effect that may limit the effectiveness of this fluid pairing. This comparative analysis not only highlights the intricate interactions between different fluid types but also provides valuable insights into the potential of nanofluids to enhance thermal efficiency in heat exchanger applications, paving the way for future research and practical implementations in thermal management systems.

Innovations for Holistic and Sustainable Transitions

Energy system planning has evolved from a narrow focus on engineering and supply works towards addressing more complex, multifactorial challenges. Increasingly challenged by climate change, extreme events, economic shocks, and altered supply demand patterns, the analysis of energy systems requires holistic approaches based on data-driven models, taking into account key socio-economic factors. We draw insights from reviewing the literature, indicating the need to cover the following major gaps: the shift to transdisciplinary approaches, incorporating environmental system analysis; resilient and sustainable energy designs based on flexible portfolios of renewable mixes; the integration of socio-economic aspects, economic analyses and behavioural models to ensure energy systems are not only technically sound but socially acceptable and viable; the need for stakeholder engagement considering the human angle in energy security and behavioural shifts. Responding to these pressing challenges and emerging needs, the Global Climate Hub (GCH) initiative, operating under the UN Sustainable Development Solutions Network, offers a conceptual framework, leveraging transdisciplinary approaches. In this Concept Paper, we present for the first time the idea of the GCH as a framework that we believe has the potential to address the modern holistic needs for energy system analysis and policymaking. By setting the conceptual/theoretical ground of our suggested approach, we aim to provide guidance for innovative combinations of cutting-edge models, socio-economic narratives, and inclusive interaction with relevant stakeholders for the development and the long-term implementation of sustainable pathways.

Integrating mathematical modeling into synthetic biology education: a comprehensive approach through webinars and syllabus development

Mathematical modeling is a fundamental aspect of synthetic biology, enabling precise design and analysis of biological systems. To enhance students' understanding of this critical topic, we developed a series of webinars aimed at teaching mathematical modeling to iGEM teams. These webinars were initially created to maintain student engagement during a period of restricted lab access but quickly demonstrated their value as an effective educational tool. The success of these webinars highlighted the suitability of mathematical modeling as a topic well-suited to both onsite and online learning environments. Recognizing this, we expanded the content into a comprehensive syllabus for undergraduate courses in synthetic biology at the Universitat Politècnica de Valencia in Spain and Universidad de las Fuerzas Armadas—ESPE in Ecuador. The course now serves as a core component of synthetic biology education, offering students a robust framework for understanding and applying mathematical models. It includes a series of lectures, practical exercises, and case studies, all designed to deepen students' knowledge and skills in this essential area. To support educators and students, we have also developed a deck of slides and example scripts that provide practical examples and reinforce the concepts taught in the course. This manuscript presents the development, implementation, and impact of these educational initiatives, demonstrating how mathematical modeling can be effectively integrated into synthetic biology curricula to prepare students for real-world challenges in the field.

Effective modeling of open quantum systems by low-rank discretization of structured environments.

The accurate description of the interaction of a quantum system with its environment is a challenging problem ubiquitous across all areas of physics and lies at the foundation of quantum mechanics theory. Here, we pioneer a new strategy to create discrete low-rank models of the system-environment interaction, by exploiting the frequency and time domain information encoded in the fluctuation-dissipation relation connecting the system-bath correlation function and the spectral density. We demonstrate the effectiveness of our methodology by combining it with tensor-network methodologies and simulating the quantum dynamics of complex excitonic systems in a highly structured bosonic environment. The new modeling framework sets the basis for a leap in the analysis of open quantum systems, providing controlled accuracy at significantly reduced computational costs, with benefits in all connected research areas.

Integrating Global Norms for Gender Equity in Kazakhstan: Aligning Workplace and Social Security Legislation with Sustainable Development Goals

Objective: This study investigates the impact of international standards on shaping national approaches to gender equality, focusing on Kazakhstan’s legal and social frameworks. Theoretical Framework: This research is based on theoretical perspectives regarding implementing international law, offering a foundation for understanding how global norms influence national policies. Method: A multidisciplinary methodology is applied, including comparative legal analysis, normative-legal methods, systems analysis, and legal modeling. Forecasting techniques are also used to anticipate future trends in the alignment of national policies with international gender equality standards. Results and Discussion: This study identifies several critical challenges that Kazakhstan faces in reforming its social and labor policies to align with international standards. Significant institutional and legal barriers hinder progress in promoting gender equality and eliminating discrimination. The discussion focuses on these obstacles and examines their implications for policy development and implementation. Research Implications: The findings of this study contribute to academic research and policy development by enriching the scientific discourse on gender equality and offering recommendations for integrating international standards into national legislation. This study not only provides a theoretical basis but also offers practical ideas for improving policies and legal frameworks. Originality/Value: This study sheds new light on the relationship between international legal standards and national legislation. It offers practical recommendations for advancing gender equality and supports the achievement of Sustainable Development Goal 5 (SDG 5) by promoting the empowerment of women.