Comprehensive Analysis System Research Articles

Improving Computer-Aided Thoracic Disease Diagnosis through Comparative Analysis Using Chest X-ray Images Taken at Different Times.

Medical professionals in thoracic medicine routinely analyze chest X-ray images, often comparing pairs of images taken at different times to detect lesions or anomalies in patients. This research aims to design a computer-aided diagnosis system that enhances the efficiency of thoracic physicians in comparing and diagnosing X-ray images, ultimately reducing misjudgments. The proposed system encompasses four key components: segmentation, alignment, comparison, and classification of lung X-ray images. Utilizing a public NIH Chest X-ray14 dataset and a local dataset gathered by the Chiayi Christian Hospital in Taiwan, the efficacy of both the traditional methods and deep-learning methods were compared. Experimental results indicate that, in both the segmentation and alignment stages, the deep-learning method outperforms the traditional method, achieving higher average IoU, detection rates, and significantly reduced processing time. In the comparison stage, we designed nonlinear transfer functions to highlight the differences between pre- and post-images through heat maps. In the classification stage, single-input and dual-input network architectures were proposed. The inclusion of difference information in single-input networks enhances AUC by approximately 1%, and dual-input networks achieve a 1.2-1.4% AUC increase, underscoring the importance of difference images in lung disease identification and classification based on chest X-ray images. While the proposed system is still in its early stages and far from clinical application, the results demonstrate potential steps forward in the development of a comprehensive computer-aided diagnostic system for comparative analysis of chest X-ray images.

Intelligent vehicle navigation systems and autonomous driving technology: A comprehensive analysis

This paper conducts a comprehensive study and analysis of intelligent vehicle navigation systems and autonomous driving technology. We review the historical development of autonomous driving technology, discuss key concepts such as perception, decision-making, and control, explore various types of autonomous vehicles, and examine various aspects of intelligent vehicle navigation systems. Additionally, we investigate safety, reliability, legal frameworks in the field of autonomous driving, as well as future trends and ethical considerations. Finally, we summarize the main findings of the research and provide recommendations for future studies.

The cybersecurity mesh: A comprehensive survey of involved artificial intelligence methods, cryptographic protocols and challenges for future research

In today’s world, it is vital to have strong cybersecurity measures in place. To combat the ever-evolving threats, adopting advanced models like cybersecurity mesh is necessary to enhance our protection. Cybersecurity mesh is an architecture scalable, flexible, composable, robust and resilient and allows the interoperability and coordination between intelligent systems to provide security services. Designing a cybersecurity mesh faces three major challenges: scalability, distributed or federated systems, and technology integration. For the design, it is necessary to apply security tools that support scalability because millions and millions of data are stored, processed, and analysed. Federated systems are needed to improve interoperability in a decentralized cybersecurity mesh. However, it can be tough to integrate different security tools and communication protocols. Cryptographic algorithms and AI models like federated learning, swarming intelligence and blockchain technologies are useful for security services. It is essential to study the integration of existing methods to determine the best technology for the job. We conduct a comprehensive analysis of intelligent systems, including federated learning, blockchain technology, and swarming intelligence, with a particular focus on how they have been and can be used to enhance cybersecurity. We examine the latest trends in these technologies, explore their connections, and weigh the pros and cons of each approach. To conduct this review, we utilized the Web of Science and Scopus databases and followed the PRISMA guidelines.

Comprehensive analysis of offshore wind-photovoltaic hybrid systems: unveiling state-of-the-art autonomous components for maritime applications

This paper conducts a comprehensive analysis of offshore wind-photovoltaic hybrid systems, presenting autonomous components tailored for maritime applications. Focusing on the critical role of these systems in supplying electricity to regions with limited infrastructure, particularly maritime zones, the study explores economic advantages over traditional power line installations, emphasizing remote areas. Key components, including battery storage, autonomy, battery selection (with a spotlight on lead-acid batteries), charge controllers, inverters, and the integration of Automatic Identification System (A.I.S.) for maritime safety, are discussed. The article concludes by underscoring the significance of power supply buoys for ship safety and efficient refueling near hybrid systems.

Thermodynamic Modeling and Performance Evaluation of Absorption Refrigeration System with Graphene Nanofluid

The study presents a comprehensive computational analysis of an absorption refrigeration system that employs graphene nanofluids as the secondary fluid. The system's tube-type evaporator, utilizing copper tubes, was optimized and analyzed under varying nanofluid concentrations through thermodynamic modeling. Key thermal and design parameters were thoroughly examined, including evaporation area, heat flux, conductive and convective coefficients, and overall heat transfer coefficient. The findings demonstrate notable enhancements in the analyzed parameters when employing nanofluids in the system, with improvements of 5.2% in the pumping power of the secondary system and 0.8% in the evaporation area. These outcomes highlight the potential benefits of incorporating graphene nanofluids in absorption refrigeration systems, paving the way for more efficient and sustainable cooling solutions.

A Comprehensive Topology Review and Comparative Analysis of Wind Energy Conversion Systems

A Comprehensive Topology Review and Comparative Analysis of Wind Energy Conversion Systems

Optimization of Topological Reconfiguration in Electric Power Systems Using Genetic Algorithm and Nonlinear Programming with Discontinuous Derivatives

This article addresses a comprehensive analysis of power electrical systems, employing a combined approach of genetic algorithms and mathematical optimization through nonlinear programming with discontinuous derivatives (DNLP) in GAMS. The primary objective is to minimize economic losses and associated costs faced by the network operator following disruptive events. The analysis is divided into two fundamental aspects. Firstly, it addresses the topological reconfiguration of the network, involving the addition of lines and distributed energy resources such as distributed generation. To determine the optimal topological reconfiguration, a genetic algorithm was developed and implemented. This approach aims to restore electrical service to the maximum load within the system. Secondly, an optimal energy dispatch was performed for each generator, considering the variation in load throughout the day. The system’s load curve is taken into account to determine the optimal energy distribution. Thus, the problem of economic losses is approached from two perspectives: the minimization of costs due to nonsupplied electrical energy and the determination of efficient energy dispatch for each generator after network reconfiguration. For the analysis and case studies, simulations were conducted on the IEEE 9- and 30-node test systems. The results demonstrate the effectiveness of the proposed solution, evaluated in terms of reduced load shedding and economic losses.

Machine Learning and Augmented Intelligence Enables Prognosis of Type 2 Diabetes Prior to Clinical Manifestation.

The global incidence of type 2 diabetes (T2D) persists at epidemic proportions. Early diagnosis and/or preventive efforts are critical to attenuate the multi-systemic clinical manifestation and consequent healthcare burden. Despite enormous strides in the understanding of pathophysiology and on-going therapeutic development, effectiveness and access are persistent limitations. Among the greatest challenges, the extensive research efforts have not promulgated reliable predictive biomarkers for early detection and risk assessment. The emerging fields of multi-omics combined with machine learning (ML) and augmented intelligence (AI) have profoundly impacted the capacity for predictive, preventive, and personalized medicine. This paper explores the current challenges associated with the identification of predictive biomarkers for T2D and discusses potential actionable solutions for biomarker identification and validation. The articles included were collected from PubMed queries. The selected topics of inquiry represented a wide range of themes in diabetes biomarker prediction and prognosis. The current criteria and cutoffs for T2D diagnosis are not optimal nor consider a myriad of contributing factors in terms of early detection. There is an opportunity to leverage AI and ML to significantly enhance the understanding of the underlying mechanisms of the disease and identify prognostic biomarkers. The innovative technologies being developed by GATC are expected to play a crucial role in this pursuit via algorithm training and validation, enabling comprehensive and in-depth analysis of complex biological systems. GATC is an emerging leader guiding the establishment of a systems approach towards research and predictive, personalized medicine. The integration of these technologies with clinical data can contribute to a more comprehensive understanding of T2D, paving the way for precision medicine approaches and improved patient outcomes.

A framework for efficient cross-chain token transfers in blockchain networks

The proliferation of blockchain technology has resulted in diverse token standards, posing challenges for compatibility, security, and performance in existing cross-chain bridges. This paper introduces a novel framework capable of concurrently facilitating fungible token exchange, as well as the processing of both individual and batch non-fungible tokens (NFTs). We deploy token bridges that meet different token standards to support cross-chain staking and unlocking of ERC20, ERC721, and ERC1155. To minimize both waiting times and handling fees, we relocate processes necessitating frequent transactions and verifications to the sidechain. Additionally, we adopt a batch-processing approach for tokens necessitating cross-chain transfers, leveraging payment channels to facilitate efficiency. The system’s reliability is upheld through the validator group. Validators acquire an initial reputation value by making deposits and enhance both their rewards and reputation by successfully completing NFT auction tasks on the sidechain. We use OpenZeppelin’s security library functions to standardize token operations, and carefully design the validator’s reward, punishment, and reputation mechanisms. Our comprehensive contract security audit and system analysis validate our solution’s effectiveness in mitigating common vulnerabilities and internal threats. Implementation and testing with Ethereum and its test network demonstrate substantial reductions in transmission time for key cross-chain token steps by nearly half. Moreover, our framework showcases efficiency and cost-effectiveness with an average gas cost of 693,379.

A paradigm shift in crisis management: The nexus of AGI‐driven intelligence fusion networks and blockchain trustworthiness

AbstractIn an era characterized by vast data streams and complex socioeconomic dynamics, the fusion and precise analysis of multi‐sourced intelligence has emerged as a pivotal challenge. To address this, the study constructs a sophisticated intelligence fusion network (IFN) architecture leveraging the potential of Artificial General Intelligence (AGI) and the security tenets of blockchain technology. Drawing from diverse fields including informatics, computer science, data analytics, and network security, the research adopts an integrative methodology comprising both a comprehensive literature review and systems analysis. Key findings highlight the prowess of AGI‐driven IFNs in enhancing governmental early warning systems for crisis management. These networks underscore a paradigm shift from reactive postevent measures to proactive pre‐event forecasting, thus bolstering the efficacy of governmental responses. Moreover, the decentralized nature of blockchain technology ensures data integrity, fostering trust in interdepartmental data sharing—an essential for efficient crisis management in hierarchical administrative structures. This study accentuates the need for redefining crisis management strategies, emphasizing data‐driven decision‐making and seamless intelligence sharing to ensure optimal outcomes.

Attracting Tourists to the Region: Analysis on Macau's Transportation System

This essay provides a comprehensive analysis of Macao's transportation system, focusing on aviation, railways, buses, and ferries, and offers suggestions for improvement. The aviation sector is examined in terms of airport location, capacity, current status, and future development plans. The railway system is evaluated for its convenience, environmental impact, accessibility, and passenger safety measures, while also highlighting areas for improvement such as consumer experience and design. The public transport network is assessed for its coverage, vehicle condition, service quality, and resilience to major environmental changes like epidemics and financial crises. The reliability, frequency, comfort, and economic contribution of ferry services are also scrutinized. The essay aims to propose measures to enhance Macao's transportation convenience, tourist experience, and sustainable tourism development.

Comprehensive analysis of renewable hybrid energy systems in highway tunnels

In order to explore the feasibility of a renewable hybrid energy system in highway tunnels, a scenario-coupled construction method for a highway tunnel renewable hybrid energy system is proposed. Based on this method, a tunnel on a highway in southern China serves as an example, and a renewable hybrid energy system for the highway tunnel is constructed. Using HOMER, the hybrid energy system is simulated, optimized, and analyzed, studying the characteristics of the operation of the highway tunnel hybrid renewable energy system in an off-grid mode. The optimal configuration for the hybrid energy system is proposed. The research results indicate the feasibility of constructing a highway tunnel renewable hybrid energy system by utilizing natural resources within the road area (solar energy, wind energy). The hybrid renewable energy system for this tunnel can provide 1112392 kWh of electrical energy annually, with the lowest energy cost being $0.17/kW. Furthermore, the hybrid energy system can reduce greenhouse gas emissions, contributing to the sustainable development of the environment.

Benchmarking the Functional, Technical, and Business Characteristics of Automated Passenger Counting Products

Urban transport planning and the integration of various mobility options have become increasingly complex, necessitating a thorough understanding of user mobility patterns and their diverse needs. This paper focuses on benchmarking different Automatic Passenger Counting (APC) technologies, which play a key role in Mobility as a Service (MaaS) systems. APC systems provide valuable data for analysing mobility patterns and informing decisions about resource allocation. Our study presents a comprehensive data collection and benchmark analysis of APC solutions. The literature review emphasises the significance of passenger counting for transport companies and discusses various existing APC technologies, such as pressure sensors, wireless sensors, optical infrared sensors (IR), and video image technology. Real-world applications of APC systems are examined, highlighting experimental results and their potential for improving accuracy. The methodology outlines the data collection process, which involved identifying APC companies, conducting interviews with companies and customers, and administering an ad hoc survey to gather specific information about APC systems. The collected data were used to establish criteria and key performance indicators (KPIs) for the benchmarking analysis. The benchmarking analysis compares APC devices and companies based on ten criteria: technology, accuracy, environment, coverage, interface, interference, robustness (for devices), price, pricing model, and system integration (for companies). KPIs were developed to measure performance and make comparison easier. The results of the benchmarking analysis offer insights into the costs and accuracy of different APC systems, enabling informed decision making regarding system selection and implementation. The findings fill a research gap and provide valuable information for transport companies and policy makers, and we offer a comprehensive analysis of APC systems, highlighting their strengths, weaknesses, and business strategies. The paper concludes by discussing limitations and suggesting future research directions for APC technologies.

Genome-Wide and Expression Pattern Analysis of the DVL Gene Family Reveals GhM_A05G1032 Is Involved in Fuzz Development in G. hirsutum.

DVL is one of the small polypeptides which plays an important role in regulating plant growth and development, tissue differentiation, and organ formation in the process of coping with stress conditions. So far, there has been no comprehensive analysis of the expression profile and function of the cotton DVL gene. According to previous studies, a candidate gene related to the development of fuzz was screened, belonging to the DVL family, and was related to the development of trichomes in Arabidopsis thaliana. However, the comprehensive identification and systematic analysis of DVL in cotton have not been conducted. In this study, we employed bioinformatics approaches to conduct a novel analysis of the structural characteristics, phylogenetic tree, gene structure, expression pattern, evolutionary relationship, and selective pressure of the DVL gene family members in four cotton species. A total of 117 DVL genes were identified, including 39 members in G. hirsutum. Based on the phylogenetic analysis, the DVL protein sequences were categorized into five distinct subfamilies. Additionally, we successfully mapped these genes onto chromosomes and visually represented their gene structure information. Furthermore, we predicted the presence of cis-acting elements in DVL genes in G. hirsutum and characterized the repeat types of DVL genes in the four cotton species. Moreover, we computed the Ka/Ks ratio of homologous genes across the four cotton species and elucidated the selective pressure acting on these homologous genes. In addition, we described the expression patterns of the DVL gene family using RNA-seq data, verified the correlation between GhMDVL3 and fuzz development through VIGS technology, and found that some DVL genes may be involved in resistance to biotic and abiotic stress conditions through qRT-PCR technology. Furthermore, a potential interaction network was constructed by WGCNA, and our findings demonstrated the potential of GhM_A05G1032 to interact with numerous genes, thereby playing a crucial role in regulating fuzz development. This research significantly contributed to the comprehension of DVL genes in upland cotton, thereby establishing a solid basis for future investigations into the functional aspects of DVL genes in cotton.

Comprehensive analysis and optimization of a low-carbon multi-generation system driven by municipal solid waste and solar thermal energy integrated with a microbial fuel cell

Abstract In this article, a novel multi-generation plant is addressed and assessed from the energy, exergy, exergoenvironmental and exergoeconomic points of view. The multi-generation plant is composed of two main units: one unit for energy production and another unit for carbon capture and methanol synthesis. Biomass fuel, solar energy and seawater are the main nutrients in the plant. Steam, Brayton, organic Rankine and Kalina cycles have been employed to generate electricity. A linear Fresnel collector-driven solar farm is considered as an auxiliary heat source. In addition, an integrated desalination unit based on a multi-effect desalination unit, a microbial fuel cell and a reverse osmosis unit has been installed in the multi-generation plant. The proposed structure for the offered multi-generation plant is designed under a new configuration and layout that had not been reported in the publications. From the outcomes, the multi-generation plant can produce 69.6 MW of net electricity, 0.53 kg/s of methanol, 0.81 kg/s of oxygen gas, 73.8 kg/s of fresh water and ~0.015 kg/s of hydrogen gas. Under such performance, the offered multi-generation plant can be 51.72 and 27.5% efficient from the points of view of energy and exergy, respectively. Further, the total cost rate and environmental impact of the plant are ~3378 US$/h and 294.1 mPts/s, respectively. A comparative analysis is developed to exhibit the superiority of the planned multi-generation plant. A five-objective optimization is also developed to achieve the optimum design data and outcomes of the plant.

Assessing the existing guidelines of environmental impact assessment and mitigation measures for future hydropower projects in Pakistan

Introduction: More than 16% of the total electricity used worldwide is met by hydropower, having local to regional environmental consequences. With positive indicators that energy is becoming more broadly available and sustainable, the world is moving closer to achieving Sustainable Development Goal 7 (SDG 7). Pakistan became the first nation to include the Sustainable Development Goals (SDGs) in its national development strategy.Methodology: The current study sought to investigate the structural limits of Environmental Impact Assessment (EIA) guidelines for hydropower development in Pakistan. The study included the document review of the EIA reports about hydropower projects in Pakistan, scientific questionnaires from decision-makers, and public consultation.Results and Discussion: The document evaluates that an adequate mechanism is available, and donors like the Asian Development Bank and World Bank observe the implementation process of EIA in Pakistan. However, a comprehensive analysis of the EIA system found several things that could be improved, not only in the institutional framework but also in actual implementation and practices. More than 20% of respondent decision-makers disagreed with the compliance of the current Institutional Framework with EIA guidelines, and 25% think that the existing guidelines followed in Pakistan are not aligned with international standards and practices for Hydropower in actual practice. EIA has a limited impact on decision-making due to insufficient technical and financial resources.Recommendations: There should be a think tank with experts who can meet the needs of present and future epochs. The public should be communicated with and educated about EIA. For better efficiency, the officers and decision-makers should be trained internationally, i.e., the Water and Power Development Authority (WAPDA).

A comprehensive analysis and multi-objective optimization of a sustainable production system based on renewable energies

The adoption and promotion of clean and sustainable energy sources bring about significant benefits to the environment, economy, and society, both in the present and for future generations. This study introduces a pioneering solar-wind biomass multi-generation system, aimed at facilitating the sustainable production of electricity, fresh water, hydrogen, and heating. This system provides a comprehensive evaluation of the influence of environmental conditions and design factors on system efficiency, taking into account the crucial aspects of energy, exergy, economy, and environment (4E). Additionally, a meticulous sensitivity analysis is conducted to identify and prioritize key decision variables that exert the most significant impact on the overall system. Ultimately, the multi-objective optimization is conducted by taking into account various priorities in four distinct scenarios. This approach enables the simultaneous or individual optimization of the system's useful objective functions in relation to the total cost rate. Subsequently, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is employed to determine the ideal point that best fulfills the optimization criteria. By employing TOPSIS, this study aims to provide a robust and systematic decision-making framework for scenario selection in various domains. By evaluating the optimal points across various scenarios, the first scenario yields the most favorable outcomes in terms of the system's useful outputs. These include an exergy efficiency of 19.63 %, hydrogen production of 11.07 kg/h, fresh water production of 34.16 kg/s, and a total cost rate amounting to 213.80 $/h. Additionally, a considerable decrease of 22.65 % in the total cost rate is noted at the optimal point.

Comprehensive analysis of the magnetic field closed-loop control system in SERF atomic magnetometer

Benefiting from the capability to measure and suppress magnetic field interferences in situ, the closed-loop magnetometer system has garnered significant attention. In this paper, the low-pass filtering link is analyzed theoretically and experimentally for the first time within the framework of the single-beam spin exchange relaxation free atomic magnetometer system (the object to be controlled). The analysis not only elucidates its impact on the closed-loop system but also establishes optimal operating parameters through a comprehensive consideration of factors such as steady-state error, bandwidth, and other metrics. A comparison and analysis of the direct output signal of the system with the controller output signal reveal the necessity of using the controller output as the closed-loop system’s output, supported by the amplitude and frequency response characteristics of the output signal. The paper introduces evaluation indicators essential for comparing the long-term measurement stability of open- and closed-loop systems, and it explores the correlation between the output signals of the open- and closed-loop systems and the cardio-magnetic input signals. Finally, the calibration methods for multi-channel closed-loop magnetometers are discussed.

The role of technology in entrepreneurship: a comprehensive systematic and bibliometric analysis

PurposeThrough bibliometric analysis, the study intends to reveal the evolution of the trends in the Scopus database, the scope of research and the connection between technology and entrepreneurship.Design/methodology/approachThis study uses a comprehensive science mapping approach, integrating network visualisation to map research groups, bibliometric analysis to measure publication trends and thematic analysis to identify overarching themes. This study uses a thorough technique to examine the complex interaction between technology and entrepreneurship from 2000 to 2023. The collection includes information from various sources, creating a corpus of 2,207 documents. These sources include 698 scholarly journals, books and other publications.FindingsAccording to the report, the interest in technology and entrepreneurship is expanding. The three nations conducting the most study on this subject is the USA, the UK and Italy. Some of the top writers in this area include James A. Cunningham, Alison N. Link and David B. Audretsch.Research limitations/implicationsThe study found possibilities and problems associated with the interaction between technology and entrepreneurship. Additionally, the study found several research holes in this area. The study also noted some research gaps in this field, including those related to the sustainability of society and the environment, the effects of entrepreneurship on inequality and the difficulties faced by entrepreneurs in underdeveloped nations.Practical implicationsThis study thoroughly overviews the business and technology sectors. It outlines some of the difficulties that must be overcome whilst identifying the main research trends in this field. Researchers, decision-makers and businesspeople interested in using technology for entrepreneurial endeavours can all benefit from the study’s findings.Social implicationsThis study’s dataset’s scope, which might not include all pertinent publications, is one of its limitations. Nevertheless, the results add to a thorough picture of the state of the profession and recent developments. This study’s insights are valuable for researchers, policymakers and entrepreneurs interested in leveraging technology for entrepreneurial pursuits.Originality/valueThe research points to a number of directions that need more inquiry, such as in-depth studies into the social and environmental implications of technology-driven entrepreneurship and methods to combat inequality.

Impact of Technology and Governance on Consumer Behaviour

This comprehensive systematic analysis examines the complicated interplay between technology, governance, and consumer behavior. Technological advancement and shifting governance systems affect consumer behavior; therefore, understanding how they do so is vital. This review meticulously analyzes a variety of literature to elucidate the complex relationships between technology, government, and consumer behavior in many fields. The study highlights how technological advances and governance systems affect customer preferences, attitudes, and decision-making. Governance systems can also regulate how technology affects consumer behavior, according to the paper. This systematic review consolidates current information and identifies promising research areas, offering future directions including technological and governance advances. This review aims to inform strategic decision-making, policy development, and scholarly inquiry in a networked and digitally mediated society.