Comprehensive Analysis System Research Articles

Toward the Development of a Comprehensive Nuclear System Analysis Code Based on Two-Fluid Model: Starting with an Isentropic Approach

Toward the Development of a Comprehensive Nuclear System Analysis Code Based on Two-Fluid Model: Starting with an Isentropic Approach

Reconfigurable Acceleration of Neural Networks: A Comprehensive Study of FPGA-based Systems

This paper explores the potential of Field-Programmable Gate Arrays (FPGAs) for accelerating both neural network inference and training. We present a comprehensive analysis of FPGA-based systems, encompassing architecture design, hardware implementation strategies, and performance evaluation. Our study highlights the advantages of FPGAs over traditional CPUs and GPUs for neural network workloads, including their inherent parallelism, reconfigurability, and ability to tailor hardware to specific network needs. We delve into various hardware implementation strategies, from direct mapping to dataflow architectures and specialized hardware blocks, examining their impact on performance. Furthermore, we benchmark FPGA-based systems against traditional platforms, evaluating inference speed, energy efficiency, and memory bandwidth. Finally, we explore emerging trends in FPGA-based neural network acceleration, such as specialized architectures, efficient memory management techniques, and hybrid CPU-FPGA systems. Our analysis underscores the significant potential of FPGAs for accelerating deep learning applications, particularly those requiring high performance, low latency, and energy efficiency.

GutMetaNet: an integrated database for exploring horizontal gene transfer and functional redundancy in the human gut microbiome.

Metagenomic studies have revealed the critical roles of complex microbial interactions, including horizontal gene transfer (HGT) and functional redundancy (FR), in shaping the gut microbiome's functional capacity and resilience. However, the lack of comprehensive data integration and systematic analysis approaches has limited the in-depth exploration of HGT and FR dynamics across large-scale gut microbiome datasets. To address this gap, we present GutMetaNet (https://gutmetanet.deepomics.org/), a first-of-its-kind database integrating extensive human gut microbiome data with comprehensive HGT and FR analyses. GutMetaNet contains 21567 human gut metagenome samples with whole-genome shotgun sequencing data related to various health conditions. Through systematic analysis, we have characterized the taxonomic profiles andFR profiles, and identified 14636 HGT events using a shared reference genome database across the collected samples. These HGT events have been curated into 8049 clusters, which are annotated with categorized mobile genetic elements, including transposons, prophages, integrative mobilizable elements, genomic islands, integrative conjugative elements and group II introns. Additionally, GutMetaNet incorporates automated analyses and visualizations for the HGT events and FR, serving as an efficient platform for in-depth exploration of the interactions among gut microbiome taxa and their implications for human health.

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.

Research on Driver Emotion Monitoring and Regulation System in Intelligent Assisted Driving

Abstract. With the rapid development of smart connected vehicles, the influence, recognition and regulation of driver's emotions are crucial for intelligent cockpits with "emotional interaction". This review aims to provide a comprehensive analysis of driver emotion monitoring systems in intelligent assisted driving. By summarizing and analyzing related literature, the structure and application of emotion monitoring systems are introduced, their principles and evaluation criteria are discussed in depth, and the advantages and limitations of the three sub-systems, namely, detection, decision-making and regulation, are analyzed in detail. The results show that although emotion monitoring and modulation technologies perform well in enhancing driving safety and comfort, their practical applications still face challenges in terms of technology maturity, cost control, and privacy protection. In addition, the reliability and accuracy of current emotion monitoring systems in complex driving environments have yet to be improved. Future research should focus on interdisciplinary cooperation to integrate the latest achievements in psychology, computer science and engineering. At the same time, it is necessary to develop unified technical standards and evaluation guidelines to promote compatibility and interoperability between different systems and enhance research on privacy protection measures to ensure the security and confidentiality of user data. Through these efforts, the field of intelligent driving is expected to achieve comprehensive development and wide application, further enhancing driver safety and user experience.

Ethical Implications of AI-Driven AAC Systems: Ensuring Inclusivity and Equity in Assistive Technologies

This study investigates the ethical implications of Artificial Intelligence (AI) integration into Augmentative and Alternative Communication (AAC) systems, focusing on inclusivity, accessibility, and data privacy in assistive technologies. AI-driven AAC systems enhance communication for individuals with impairments by offering features like predictive text, speech recognition, and symbol-to-text translation. However, ethical challenges arise as these systems increasingly rely on sensitive user data, which could jeopardize privacy without strict safeguards. Furthermore, limitations in linguistic diversity within AI training data compromise inclusivity, potentially excluding users from varied cultural and linguistic backgrounds. Accessibility also remains an issue, as AI-driven AAC systems are often financially inaccessible for marginalized communities, exacerbating social inequities. Through a comprehensive analysis of current systems, including Proloquo2Go, Tobii Dynavox and Google’s Project Euphonia, this paper evaluates both the advancements and ethical shortcomings in AI-driven AAC technologies. The findings advocate for privacy-preserving AI practices, culturally diverse training datasets, and affordable AAC solutions to foster a more inclusive, accessible, and equitable digital communication environment.

Integrating Diverse Data Streams for Enhanced Emotional Intelligence in Mental Health Care

This research presents a comprehensive system for real-time emotion recognition and analysis using multimodal data, including image, video, audio, and text. The system employs deep learning models to extract features and classify emotions from each modality. By integrating these predictions, we aim to provide a holistic understanding of a user's emotional state and assess potential risks. The system further analyzes the collected emotion data to identify trends, patterns, and indicators of emotional well-being and suicide risk. Visualizations such as time-series plots, distribution charts, and stacked area charts are employed to represent the emotional dynamics over time.Based on the analysis; the system generates personalized recommendations and alerts for users exhibiting signs of distress or emotional instability, including potential suicide risk factors. The goal is to promote proactive well-being management and early intervention. It is crucial to emphasize that this system is a tool for early detection and should be used in conjunction with professional mental health care.

Modeling of hybrid renewable resources and comprehensive feasibility analysis of grid interactive energy system for commercial building: a case study

ABSTRACT The growing energy demand in commercial buildings and businesses poses significant environmental challenges due to the dependence on fossil fuel-based energy. To address this issue, increasing the use of renewable energy sources and decreasing reliance on fossil fuels is crucial. Commercial buildings, in particular, have many opportunities to harness energy from renewable resources, such as solar, wind, and biomass. This study analyzed historical energy consumption data from a commercial building using MATLAB to forecast future energy demands. A range of machine learning algorithms were employed to accurately predict energy consumption, including Ensemble Boosting, Ensemble Bagging, Decision Trees, Support Vector Machines (SVM), and Neural Networks (NN). A Hybrid Renewable Energy (HRE) system was designed to meet the building’s energy needs, integrating renewable sources with grid-interactive inverters. Performance metrics for the forecasting models were calculated and documented, followed by a detailed techno-economic and environmental feasibility analysis of the HRE system. Different levels of renewable energy penetration were explored to optimize HRE designs using load-following dispatch algorithms tailored to the commercial building’s specific needs. The study revealed that achieving a renewable energy proportion of 12.6% resulted in the lowest cost of electricity (COE) at $0.135 and a Net Present Cost (NPC) of $8.84 million. Additionally, a reliability study using load flow techniques confirmed the stability of the optimized Hybrid Renewable Energy system. The energy costs for varying levels of renewable penetration, ranging from 10% to 40% is also explored in this research. In summary, transitioning to renewable energy sources in commercial buildings can reduce environmental impact while ensuring a sustainable energy supply.

Enhancing Snooker Player Development in China: A Comprehensive Analysis of Training System and Competition Setup

This paper explores the development of snooker in China, focusing on the proliferation and challenges faced by snooker in China. It examines the inequalities in snooker popularity and specialization in China. By comparing youth development mechanisms in other sports, this paper argues that snooker needs better screening and development mechanisms to improve the quality and quantity of players. Also, Chinese snooker needs more tournaments held by local clubs to screen players for the higher levels of the game.

Analysis of the degradation in transmission accuracy of RV reducers considering the wear clearance between cycloidal wheels and pinwheels

To accurately determine the transmission accuracy degradation pattern of Rotate Vector (RV) reducers due to wear, a predictive method is proposed that simultaneously considers the initial clearance and wear clearance between the cycloid wheel and the pinwheel. The initial clearance is derived from a comprehensive gear system analysis, considering assembly errors, manufacturing errors and modifications. Building on this, the wear depth of the cycloid wheel tooth profile is numerically simulated using Archard's wear theory, revealing the wear distribution pattern under various operating conditions. A Gaussian regression model is used to predict the maximum wear amount for different operational durations. Based on the predicted wear amount, a multi-body dynamic simulation model incorporating wear clearance was established to analyze the transmission error under varying clearance conditions. Results indicate that the wear depth along the tooth profile initially increases and then decreases. The depth and region of wear along the tooth profile gradually increase with load and output speed, with the impact of load on the wear region being greater than that of output speed. Under the initial clearance condition, the maximum transmission error is 22.91″. As the wear clearance increases, transmission accuracy declines sharply. When the cumulative wear clearance reaches 0.2 mm, the transmission error exceeds the allowable backlash limit. This study provides a theoretical reference for predicting accuracy degradation and guides the manufacturing design of RV reducers.

Thermodynamic and economic analysis of a novel solid oxide fuel cell, two level thermoelectric generator and double effect parallel absorption chiller integrated system

This study presents a comprehensive thermodynamic and economic analysis of a novel hybrid energy system integrating a Solid Oxide Fuel Cell (SOFC) with a Two-Level Thermoelectric Generator (TTEG) and a Double-Effect Parallel Absorption Chiller (DEPAC) for enhanced waste heat recovery. The primary objective is to maximize energy utilization and overall efficiency by leveraging the synergetic effects of these components. Unlike previous studies, this research uniquely evaluates the effectiveness of the integrated system and examines the impact of different Absorption Chiller configurations and the number of TTEG elements, filling a critical gap in the literature. The proposed system achieves a net electrical efficiency of 42.88 %, an energy efficiency of 44.61 %, and an exergy efficiency of 57.16 % under optimal conditions. This marks a significant improvement compared to standalone SOFC systems, with the TTEG effectively harnessing waste heat and contributing an additional 0.680 kW/m2 to the overall power output. The integration of the DEPAC system further enhances performance by providing cooling with a Coefficient of Performance (COP) of 1.351. The economic analysis reveals a positive Net Present Value (NPV) of $1,993,136, a Dynamic Payback Period (DPP) of 6.7 years, and a Levelized Cost of Electricity (LCOE) of $59/MWh, underscoring the system's financial viability. These findings demonstrate that the hybrid system offers a sustainable and efficient solution for power generation and waste heat recovery, aligning with the broader goals of advancing renewable energy technologies and setting a new benchmark in hybrid energy system design and optimization.

Exergy based yearly performance analysis of a poly-generation system for a small-town application

The current study conducts a comprehensive year-round exergy analysis of an integrated system centered around a reversible solid oxide cell (RSOC), aimed at fulfilling the power and fresh water requirements of a small town, equivalent to 2.7 MW and 2000 m³/day, respectively. The integrated system encompasses four subsystems: the solar field, RSOC, organic Rankine cycle coupled with ejector cooling (ORC), and zero liquid discharge multi-effect desalination (MED-ZLD). The proposed system is analyzed using MATLAB. The annual exergy efficiency for each subsystem is examined, yielding average values of 11.9 %, 48.11 %, and 56.22 % for the solar field, ORC cycle, and MED-ZLD, respectively. Notably, the exergy efficiency of the Rankine cycle is higher in colder months compared to warmer ones. This phenomenon is attributed to the cycle's greater heat generation relative to the cooling it produces during the cold season. The RSOC exhibits noteworthy average exergy efficiencies of 93 % in electrolysis mode, 69.87 % in fuel cell mode, and an overall average exergy efficiency of 79.62 % throughout the year. The average exergy efficiency of the overall system is evaluated throughout the day, nighst, and the entire operational period. The results are values of 10.74 %, 34.31 %, and 23.34 %, respectively. This discrepancy is due to the primary energy supply source, which is solar energy during the day and input hydrogen during the night. The highest exergy efficiency is observed in December, while the lowest is in June. Furthermore, the study examines the distribution of exergy destruction and identifies cycles with significant impacts by analyzing the exergy destruction ratio across various months. The solar field is the primary contributor to daytime exergy destruction, peaking at 94.9 %. At night, the MED-SET cycle leads in exergy destruction, followed closely by the RSOC cycle operating as a fuel cell. The research also delves into exploring key contributors to exergy destruction in various cycles, shedding light on crucial insights into the intricate dynamics of the integrated system's performance.

A comprehensive propulsion system analysis: an integrated approach utilising engine simulation and free-running model experiments

Regulations concerning the environmental impact and energy efficiency of ships are becoming increasingly stringent. To meet these demanding criteria, new technologies are actively being developed to achieve emission reductions while maintaining efficient fuel energy conversion into propulsion power. However, the propulsion system behaviour in a dynamic environment of the actual sea is highly nonlinear and complex, and a profound understanding of the mutual interactions is still lacking. This paper proposes an integrated approach for comprehensive propulsion system analysis. The key feature of this technique is the utilisation of an intelligent propeller drive controlled by a marine diesel engine simulator (MDES) to drive the free-running model of a ship, providing real-like behaviour of the engine model in real-time in response to actual measured values. The core of the MDES consists of a continuous cycle-mean value dynamic engine model supplemented with a detailed combustion cycle evaluation. The developed fast calculation algorithm of crank-angle resolved thermodynamic equations of the gas state in the engine cylinder enables the complete engine model to be integrated into the real-time environment of a physical model ship. Thus, the propulsion system's performance can be analysed under conditions that closely mimic those of the actual sea environment.

Exploring the research trend and development of sports science technology in the last 4 decades: systematic review

This study systematically reviews the trends and developments in sports science technology over the past four decades. Utilizing a comprehensive systematic review analysis of 1,127 articles from the SCOPUS database, we aimed to identify key subject areas, contributing countries, and prevailing keyword patterns in the field. The analysis highlights a significant increase in publications and citations, indicating the growing importance and recognition of sports science technology. Japan leads in contributions with the highest number of publications and citations, followed by the United States and China. Medicine and engineering emerged as the most influential subject areas, underscoring the interdisciplinary nature of the research. Keyword analysis revealed a strong emphasis on human studies, sports, and the integration of science and technology, reflecting the field's evolving landscape. Findings show that technological advancements such as sensor technology, big data analytics, and virtual reality have revolutionized sports science by enhancing performance monitoring, injury prevention, and rehabilitation processes. The study identifies critical trends and provides insights into future research directions, advocating for continued interdisciplinary collaboration and global cooperation to further advance the field. These insights offer a comprehensive overview of the historical and current state of sports science technology, serving as a roadmap for future innovations and applications. By understanding the development and current trends in sports science technology, researchers and practitioners can better plan and implement effective strategies for improving athletic performance and health, ultimately contributing to the broader field of sports science and technology. Keywords: technological advancements, sensor technology, big data analytics, virtual reality in sports

Comprehensive performance analysis and optimization of an ORC-HDH system for power and freshwater production driven by marine exhaust gas

Freshwater serves as a vital energy source for ships, underscoring the paramount importance of research into desalination systems in facilitating diverse and reliable energy supply for maritime vessels. Aiming at numerous waste heat of marine engines and the shortage of freshwater resources on board, this paper designs an innovative waste heat recovery (WHR) system. It includes an organic Rankine cycle, a compression refrigeration cycle, and a humidification-dehumidification (HDH) desalination system to recover waste heat from exhaust gases in cascades, which can effectively produce power, cooling, and freshwater, respectively. A parametric study of the co-generation system in terms of the freshwater production, gained output ratio (GOR), cost per unit of freshwater production, net power output, and per unit of output power cost, are conducted to identify the key operating parameters. A set of operating parameters is determined for the HDH system with a temperature of the humidifier outlet of 358.15 K and a mass flow rate ratio (MR) of seawater to humid air of 4.5. The effect of the relative humidity and temperature of the humid air, as well as the salinity and temperature of the seawater, on the performance of the HDH subsystem are analyzed. And the optimal operating conditions of the co-generation system are obtained through multi-objective cuckoo search optimization. The results demonstrate that the co-generation system can produce an output-power of 1479.8 kW, 500 kW, and 0.49 kg/s for cooling and freshwater production by using the working-fluid R365mfc/Cyclopentane with a mixing ratio of 33:67 under optimal conditions. The GOR and cost per unit of HDH subsystem are 1.62 and 1.70 $/m3, with a capital payback period of 4.18 years. The equivalent output-power of the co-generation system is 9.5 % higher than that of an ORC system.

Comprehensive Risk Analysis of Emergency Medical Response Systems in Serbian Healthcare: Assessing Systemic Vulnerabilities in Disaster Preparedness and Response.

Emergency Medical Response Systems (EMRSs) play a vital role in delivering medical aid during natural and man-made disasters. This quantitative research delves into the analysis of risk and effectiveness within Serbia's Emergency Medical Services (EMS), with a special emphasis on how work organization, resource distribution, and preparedness for mass casualty events contribute to overall disaster preparedness. The study was conducted using a questionnaire consisting of 7 sections and a total of 88 variables, distributed to and collected from 172 healthcare institutions (Public Health Centers and Hospitals). Statistical methods, including Pearson's correlation, multivariate regression analysis, and chi-square tests, were rigorously applied to analyze and interpret the data. The results from the multivariate regression analysis revealed that the organization of working hours (β = 0.035) and shift work (β = 0.042) were significant predictors of EMS organization, explaining 1.9% of the variance (R2 = 0.019). Furthermore, shift work (β = -0.045) and working hours (β = -0.037) accounted for 2.0% of the variance in the number of EMS points performed (R2 = 0.020). Also, the availability of ambulance vehicles (β = 0.075) and financial resources (β = 0.033) explained 4.1% of the variance in mass casualty preparedness (R2 = 0.041). When it comes to service area coverage, the regression results suggest that none of the predictors were statistically significant. Based on Pearson's correlation results, there is a statistically significant correlation between the EMS organization and several key variables such as the number of EMS doctors (p = 0.000), emergency medicine specialists (p = 0.000), etc. Moreover, the Chi-square test results reveal statistically significant correlations between EMS organization and how EMS activities are conducted (p = 0.001), the number of activity locations (p = 0.005), and the structure of working hours (p = 0.001). Additionally, the results underscore the necessity for increased financial support, standardized protocols, and enhanced intersectoral collaboration to strengthen Serbia's EMRS and improve overall disaster response effectiveness. Based on these findings, a clear roadmap is provided for policymakers, healthcare administrators, and EMS personnel to prioritize strategic interventions and build a robust emergency medical response system.

Optimizing cascade refrigeration systems with low GWP refrigerants for Low-Temperature Applications: A thermodynamic analysis

This study offers a comprehensive thermodynamic analysis of a cascade refrigeration system conducted for low-temperature applications, focusing on the use of low-GWP and zero-ODP refrigerants to improve efficiency and sustainability. The target of this study is to explore the performance of the system using a combination of refrigerants: low-temperature cycle refrigerants such as R744, while high-temperature cycle refrigerants such as R717, R1234yf, R1234ze(E), R1336mzz(E), and R1336mzz(Z), respectively. The ECS model incorporating in REFPROP 10.0a tool was utilized to extract the data through coding for analysis. The assessment of this system involves the examination of various parameters such as its coefficient of performance, total compressor workload, total exergy, and exergy efficiency across various operational conditions. The results are compared with and without superheated and subcooled conditions to understand the impact of these parameters on the system performance. The system with R744 in the low-temperature cycle and R717, R1234yf, R1234ze(E), R1336mzz(E), and R1336mzz(Z) in the high-temperature cycle provides a higher COP and lower total compressor work compared to the conventional cascade refrigeration system. In optimal CRS, the R744-R717 pair attains the highest COP of 2.05 at the lowest possible compressor work from 9.53 to 4.87 kW than other refrigerants pair while evaporator temperature shifting from −55 °C to −20 °C. The exergy efficiency of R744/R717 is found at 26.47 % at an evaporator temperature of −55 °C, but it extends to a peak of 51.73 % at −20 °C. The study also shows that the superheating and sub-cooling of the refrigerants have a significant impact on the performance of the cascade refrigeration system. Superheating and sub-cooling at 10 °C, improves the COP of the system by reducing the compressor work and increasing the heat transfer efficiency. The research offers valuable insights into the system’s thermodynamic performance when employing low GWP refrigerants in low-temperature applications. These findings have the potential to guide the design and optimization of cascade refrigeration systems over the range of low-temperature applications.

Unravelling complexity toward circularity: a system-dynamics based approach

Abstract This paper employed the System Dynamics (SD) methodological tool to investigate the intricate dynamics of transitioning to the circular economy, as a climate change mitigation strategy, in the building sector while focusing on stakeholder dynamics throughout the building life cycle. This paper’s methodological approach used a two-stage approach, encompassing problem articulation and formulation of dynamic hypothesis. In the problem articulation stage, this paper identified the stakeholder dynamics in the building life cycle thanks to a comprehensive critical literature review. This paper then delved deeper into the stakeholder dynamics in the formulation of dynamic hypothesis stage. The results revealed that the stakeholders are interdependent while working together to address common concerns. Furthermore, the results emphasized the importance of stakeholder collaboration and communication in overcoming barriers to the circular economy transition in the building sector. Thus, this paper aims to contribute to the understanding of stakeholder dynamics while streamlining the decision-making process and suggesting potential solutions for the effective implementation of the circular economy in the prevailing linear state of the building sector to pave the way for a collaborative and communicative approach to climate change mitigation. While recognizing the limitations, this paper suggests future research for a more comprehensive analysis of the complex system’s dynamics.

Towards a comprehensive analysis of agricultural land systems in the EU and US: A critical view on publicly available datasets

Understanding agricultural land systems is paramount to preparing for future transitions under climate change. Systematic quantitative, multi-context analyses of agricultural land systems are a promising way of increasing our understanding but rely on the availability of large-scale and commensurable data. The available data, however, are outcomes of political priorities and processes and thus not neutral windows into agricultural realities. We map, describe, and compare publicly available and publicly curated data describing agricultural land systems—those devoted to the production of food, fuel, and fiber—in the United States (US) and European Union (EU) to identify underlying data collection norms and political priorities. We discuss how these priorities shape what we can(not) see in each region and articulate future data needs to support an in-depth understanding of agricultural land systems dynamics. This comprehensive understanding of how and why agricultural land systems change is imperative to promoting sustainable, resilient, and just agriculture futures.