Comprehensive Analysis System Research Articles

Ultra-wideband mm-wave remote antenna unit for radio-over-fiber distributed antenna systems in silicon photonics

This article presents what we believe to be a novel chip-scale 25-45-GHz re-configurable mm-wave remote antenna unit (RAU) for radio over fiber (RoF) distributed antenna systems. The proposed RAU architecture optimizes energy efficiency by operating directly at mm-wave frequencies, and spectral efficiency by selecting re-configurable RF photonic filters topology. Additionally, it achieves frequency agility by rejecting interferes and a small form factor by utilizing the SOI photonics process. Two photonic integrated circuits (PICs) that act as downlink and uplink units are presented while occupying a total area of 12.33 mm2. The downlink selects a single channel within the desired frequency range, while the uplink rejects up to four interferes. The building blocks of the proposed architecture are discussed and their design consideration and parameters are shown. Then, a comprehensive system analysis of the proposed RAU architecture including key performance indicators is presented. A scalable 5-channel system is demonstrated each with a 3-dB Bandwidth of 5-GHz. Moreover, this architecture can be continuously tuned and re-configured within a wide frequency range to cover all 5-channels. To the best of the authors’ knowledge, this is the first wideband modular and re-configurable mm-wave RAU that covers the entire mm-wave sub-45-GHz band implemented in silicon photonics.

Optimization of Energy Consumption in Formaldehyde Production Process Using Reboiled Absorption Process

Formaldehyde is a crucial chemical building block in various industries, and its production often involves energy-intensive processes. This study focuses on optimizing energy consumption in formaldehyde production, explicitly employing the reboiled absorption process with a production capacity of 27,000 tons per year. The objective of this article is to develop a more energy-efficient process of formaldehyde synthesis by addition or refrigerant cycle stream to preserve the energy, reducing energy consumption and improving the sustainability of the process. The reboiled absorption process involves the absorption of formaldehyde gas into a liquid absorbent, followed by reboiling to release the absorbed formaldehyde. A comprehensive analysis of the entire production system compares unmodified and modified process simulations, heat integration, and energy analysis. Beside the energy consumption of the process, the number of stages within the absorption process contributes to the product mass flow rate of the overall process by increasing the surface area which mass transfer can occur. However, adding too many stages to the process may negatively impact the energy efficiency of the process. Therefore, optimizing energy consumption and absorption processes in formaldehyde production is essential to improve the sustainability of the process and increase the overall profitability of the production process. The results show that the proposed method dramatically improves the sustainability of CH2O synthesis by reducing overall energy consumption and emissions by 93.978%, reducing energy consumption from 153,735,360.4 kJ/h to 9,256,646.618 kJ/h. Copyright © 2024 by Authors, Published by Universitas Diponegoro and BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

Comparing the Performance of Optical Communication Links using G.652 and G.655 Fiber in Python Packages

This study investigates and compares the performance of a 10 Gbps optical communication link utilizing two prevalent single-mode fibers: G.652 and G.655. The analysis employs both theoretical calculations and Python-based simulations to assess the effectiveness of each fiber type in this high-speed transmission scenario. With the ever-growing demand for bandwidth in communication networks, 10 Gbps transmission systems are becoming increasingly commonplace. Single-mode fibers like G.652 and G.655 play a vital role in these systems, offering low signal attenuation for long-distance data transmission. However, each fiber type exhibits distinct dispersion characteristics, which can impact signal integrity over extended distances. This investigation adopts a two method for performance evaluation. Firstly, link power budget calculations are performed to determine the optical signal power before and after propagating through a 50-kilometer fiber span. The received power serves as the foundation for subsequent Q-factor and Bit Error Rate (BER) analysis. These calculations establish the theoretical limitations of the system based on well-defined formulas. Secondly, Python-based simulations are conducted to corroborate the theoretical findings and provide a more comprehensive performance assessment. This approach leverages the capabilities of two prominent Python packages: Opticomlib and OpticommPy. Opticomlib excels at analyzing the behavior of individual optical pulses within the system, enabling an understanding of the signal propagation. On the other hand, OpticommPy specializes in parameter sweep analysis, allowing for the investigation of how critical parameters like received power influence the Q-factor. By combining these functionalities, the simulations provide a detailed picture of the system's performance under various conditions. The calculated BER and Q-factor values for both G.652 and G.655 fiber links surpass the industry-accepted performance standards. These results demonstrate the effectiveness of using Python-based tools for comprehensive performance analysis of optical communication systems. However, it's important to note that slight discrepancies exist between the calculated and simulated results.

Advanced AI Solutions for Securities Trading: Building Scalable and Optimized Systems for Global Financial Markets

Integrating Artificial Intelligence (AI) into securities trading software has revolutionized the financial markets by enhancing scalability, efficiency, and optimization. This paper explores the historical evolution and advancements in AI-driven trading systems, emphasizing their impact on global financial markets. The study investigates how machine learning, deep learning, and other AI technologies enable sophisticated trading strategies, improve market liquidity, and reduce transaction costs. It also addresses the challenges AI integration poses, including decision-making opacity, bias, increased market volatility, and data privacy concerns. The paper argues for the need for scalable architectures, optimization algorithms, and transparent governance to mitigate these issues. Additionally, human oversight remains crucial for evaluating AI outputs and maintaining accountability. This research aims to provide a comprehensive analysis of advanced AI systems in securities trading, highlighting their potential to enhance market efficiency and stability.

MUTUAL FUND PORTFOLIO ANALYSIS

Abstract — Mutual funds serve as popular investment vehicles due to their potential for diversification and professional management. However, choosing the right funds and optimizing one’s portfolio allocation can be complex. This project presents a comprehensive Mutual Fund Portfolio Analysis system designed to assist investors in making informed decisions. The system allows users to input their mutual fund holdings, tracking them over time to assess performance. It offers a range of analytical tools, including portfolio diversification analysis, risk assessment, and comparison against relevant benchmark indices. These features provide investors with a holistic view of their portfolio’s performance and risk profile. Moreover, the system employs advanced algorithms to generate personalized recommendations for optimizing the portfolio based on the user’s investment goals and risk tolerance. These recommendations aim to enhance returns while managing risk effectively. By utilizing this system, investors can gain deeper insights into their mutual fund investments, enabling them to make well-informed decisions. Ultimately, this project aims to empower investors to achieve their financial goals through strategic mutual fund portfolio management. Keywords – Mutual funds, Portfolio analysis, Investment decisions, Diversification, Risk assessment, Benchmark comparison, Portfolio optimization, Investment goals, Risk tolerance.

Enhancing Job Recommendation Systems through Machine Learning: A Comprehensive Analysis of Skill Sync Job Recommendation System

Enhancing Job Recommendation Systems through Machine Learning: A Comprehensive Analysis of Skill Sync Job Recommendation System

Development and comprehensive thermo-economic analysis of a novel compressed CO2 energy storage system integrated with high-temperature thermal energy storage

This study introduces an innovative compressed CO2 energy storage (CCES) system poised to significantly enhance the management of fluctuating renewable energy sources. Unlike traditional system layouts, a high-temperature thermal energy storage (HTES), an ejector cycle and a flash process are adopted in the system and innovatively integrated into its charging and discharging process. These above methods aim to reduce geographic limitations and improve round-trip efficiency. Thermodynamic and economic models are established to conduct a comprehensive evaluation, engaging in detailed parameter analysis followed by multi-objective optimization. The analysis indicates that increasing turbine inlet pressure and temperature significantly improves system performance. While increasing flash pressure and the temperature of low-pressure storage benefit round-trip efficiency (RTE) and levelized cost of electricity (LCOE), it negatively impacts the energy storage density (ESD). Additionally, setting the ejector back pressure at approximately 7.5 MPa optimizes performance. Multi-objective analysis indicates that the optimal low-pressure storage temperature is approximately 295.07 K, with the associated flash pressure and ejector back pressure set at 3.94 MPa and 7.48 MPa, respectively. Under these conditions, the proposed system achieves the optimal RTE, ESD and LCOE of 50.43 %, 16.60 kWh/m³, and 0.0992 $/kWh, respectively. Compared to other similar systems, the proposed system exhibits preferable balance performance.

Optimized Grid-Connected Hybrid Renewable Energy Power Generation: A Comprehensive Analysis of Photovoltaic, Wind, and Fuel Cell Systems

Optimized Grid-Connected Hybrid Renewable Energy Power Generation: A Comprehensive Analysis of Photovoltaic, Wind, and Fuel Cell Systems

A Comprehensive Analysis of various Covid-19 Detection and Pandemic Prevention Systems and Methods Using Data Science

Abstract: The paper proposes a technique for early Covid-19 test prediction using AI and data science. The study analyzes various data sources to understand the patterns and trends of the pandemic and the impact of various interventions on the spread of the virus. The proposed system integrates data from multiple sources to identify potential cases and can also track the spread of the virus in real-time. The system has high accuracy in predicting positive cases and has the potential to improve our ability to detect and predict Covid-19 cases, ultimately saving lives and reducing the economic impact of the pandemic. The proposed technique for early Covid-19 test prediction using AI and data science can be a game- changer in our fight against the pandemic. With the integration of multiple data sources, including individual health records, social media, and geographic data, the system can identify potential cases even before symptoms appear. This early detection can help individuals get timely medical attention and prevent the spread of the virus to others.

Optimization of a hybrid system (PV-fuel cell) of energy production for an isolated site

In recent years, there has been a significant shift in the design and implementation of autonomous micro-grid systems, moving away from the traditional reliance on fossil fuels. Instead, these systems are now integrating existing renewable energy sources available in specific geographic locations to promote sustainability and environmental responsibility in the energy sector. One particularly promising approach for energy storage and transmission is through the utilization of hydrogen and solar energy, especially in remote regions where hybrid techniques can effectively meet the required energy demands. Our research focuses on electrifying an agricultural site located in the SBAA region, specifically in the city of ADRAR, southwest Algeria. We propose the implementation of an independent small electric network that combines various renewable energy sources, including photovoltaic (PV) systems and fuel cells. To ensure cost-effectiveness and optimize the system's performance, we aim to carefully control and size the system's components through a meticulous selection process. The primary objective of this study is to evaluate the technical and economic feasibility of implementing renewable energy systems in the targeted region. Our goals are to reduce the total net cost, minimize energy expenses and unfulfilled load, while simultaneously mitigating CO2 emissions. To achieve these objectives, we employ the HOMER Pro simulation program, which enables us to conduct a comprehensive analysis of the proposed system's performance and economic viability. The proposed system incorporates a hybrid configuration of PV panels and fuel cells, leveraging the complementary nature of these two renewable energy sources. PV panels harness the abundant solar energy during daytime hours, while fuel cells provide a reliable and consistent energy source during periods of low solar irradiance or at night. By integrating these two technologies, we aim to establish a self-sufficient and sustainable energy system capable of meeting the energy demands of the remote agricultural site. Overall, the proposed renewable energy system demonstrates positive results in electrifying remote areas while maintaining a conservative cost of electrical energy. By implementing this system, we not only contribute to the establishment of sustainable regions but also promote environmental stewardship by reducing carbon emissions and dependence on fossil fuels.

Comparative analysis of spatial transport characteristics of spring cold-front-type sandstorms in the Hexi Corridor

AbstractIn this study, we investigate the spatial transport characteristics of cold-front-type sandstorms with different intensities in the Hexi Corridor based on the hourly observation data from 13 ground-based meteorological stations in the Hexi Corridor, the data in the upper and lower layers from the Meteorological Information Comprehensive Analysis And Process System, and the three-dimensional aerosol observation data of the Vertical Feature Mask product obtained from the Cloud-Aerosol Lidar with Orthogonal Polarization of the United States. The results show that the influence range and horizontal transport distance of cold-front-type sandstorms in the Hexi Corridor are determined by the intensity, thickness and width of the upper-level troughs and the intensity of cold fronts. Generally, cold fronts pass through the Hexi Corridor from noon to evening and stay in this region for a long time or move slowly, which is favorable to the horizontal long-distance transport of sandstorms. The intensity of the 200 hPa upper-level jets is directly proportional to both the influence range and transport distance of sandstorms which primarily occur on the left side of the exit area of an upper-level jet. The strengthening of upper-level jets induces the formation of the vertical circulation cell at middle and low levels. The ascending branch of the circulation cell lifts sand-dust particles from the surface to the upper layers, and the descending branch transports upper-level momentum to the near-surface. This circulation situation increases the near-surface wind speed and forms sandstorms. The occurrence time and descending branch of the secondary circulation cell caused by upper-level jets are important indicators for predicting the occurrence time and falling area of sandstorms. Dust aerosols are mainly concentrated at a height of 2–6 km,.,and they can reach the height of 12 km. When the dust is concentrated at the height of 2–8 km, and the wind speed of 500 hPa and 300 hPa reaches 28 and 32 m s− 1 or above respectively, the cold front sandstorm can realize the long distance transport of thousands of kilometers.

Comprehensive thermodynamic and economic analysis of an LNG cold energy recovery system using organic Rankine cycle and freezing-centrifugal desalination for power and water cogeneration

In this study, the LNG cold energy recovery combined with a freezing-centrifugal desalination system is proposed. The thermodynamic and economic analysis is also carried out of the proposed system. To verify the results of sea ice centrifugation process, experimental analysis of sea ice centrifugal desalination is presented based on different types of sea ice: slurry and solid, which are prepared by the method of suspension crystallization method and progressive freezing method, respectively. The experimental outcomes demonstrated that higher rotation speeds and longer centrifugal times enhanced the desalination rate but resulted in decreased freshwater yield in both slurry and solid ice. For the slurry sea ice, achieving a rotation speed of 5000 rpm and a centrifugal time exceeding 3.0 min resulted in melted water with a salinity level below 0.05%, satisfying the standards of drinking water. Moreover, the unit product cost, payback period, and unit cost of CO2 emission reduction for the proposed system are 0.49 $/t, 3.25 years and 18.92 kg/$, respectively. While the unit product cost, payback period, and unit cost of CO2 emission reduction for the conventional system are about 0.84 $/t, 4.80 years, and 15.66 kg/$, respectively. Hence, the proposed system shows higher economic and environmental performance.

Diving into Natural Solvents: A Comprehensive Analysis of Deep Eutectic Systems Based on Terpenes and Flavonoids

Diving into Natural Solvents: A Comprehensive Analysis of Deep Eutectic Systems Based on Terpenes and Flavonoids

High-performance internal circulation anaerobic granular sludge reactor for cattle slaughterhouse wastewater treatment and simultaneous biogas production

This research investigates the efficacy of a high-performance pilot-scale Internal Circulation Anaerobic Reactor inoculated with Granular Sludge (ICAGSR) for treating cattle slaughterhouse wastewater while concurrently generating biogas. The primary objective is to assess the efficiency and performance of ICAGSR in terms of organic pollutant removal and biogas production using granular anaerobic sludge. The research methodology entails operating the ICAGSR system under ambient conditions and systematically varying key parameters, including different Hydraulic Retention Times (HRTs) (24, 12, and 8 h) and Organic Loading Rates (OLRs) (3.3, 6.14, and 12.83 kg COD/m³. d). The study focuses on evaluating pollutants’ removal and biogas production rates. Results reveal that the ICAGSR system achieves exceptional removal efficiency for organic pollutants, with Chemical Oxygen Demand (COD) removal exceeding 74%, 67%, and 68% at HRTs of 24, 12, and 8 h, respectively. Furthermore, the system demonstrates stable and sustainable biogas production, maintaining average methane contents of 80%, 76%, and 72% throughout the experimental period. The successful operation of the ICAGSR system underscores its potential as a viable technology for treating cattle slaughterhouse wastewater and generating renewable biogas. In conclusion, this study contributes to wastewater treatment and renewable energy production by providing a comprehensive analysis of the ICAGSR system’s hydrodynamic properties. The research enhances our understanding of the system’s performance optimization under varying conditions, emphasizing the benefits of utilizing ICAGSR reactors with granular sludge as an effective and sustainable approach. Identifying current gaps, future research directions aim to further refine and broaden the application of ICAGSR technology in wastewater treatment and renewable energy initiatives.

The transformative potential of Artificial Intelligence for Education

Education today faces a range of challenges, but also unique opportunities, vis-à-vis the evolution of digital technologies. Traditional teaching methods struggle to meet the diverse needs of students, as classrooms comprise learners at different levels. The Education AI project represents an initiative aimed at integrating Artificial Intelligence (AI) into the classroom to address the diversity of students and the challenges faced by traditional teaching methods. The project's core objective is to enhance student learning outcomes by providing an adaptive and personalised learning experience, thereby making education more equitable and effective. This paper evaluates the project's impact on student learning, teacher workload, and its broader applicability in education. The study's methodology combines quantitative and qualitative data, employing a mixed-methods approach to provide a comprehensive analysis of the Education AI system's effectiveness. Preliminary findings indicate a notable improvement in student performance, particularly among low and medium-performing students, with quantitative data revealing an average improvement of 23% in assessments for low-performing students. The system's user-friendly interface, gamified elements, and immediate feedback mechanisms have been well-received, with a majority of students reporting enhanced engagement and understanding of mathematical concepts. Teachers have also reported positive experiences, highlighting the system's role in reducing their workload and facilitating personalised and adaptive teaching and learning. Qualitative feedback from interviews with teachers and students provides further insights into the system's impact, emphasising its varied and engaging question types, adaptivity, and the shift from paper-based to digital learning. The feedback also reveals a desire for expansion to other subjects and enhancements in the system's analytics for more targeted support.The Education AI project demonstrates the transformative potential of AI in the classroom, with significant improvements in student performance and positive reception from both students and teachers. The study highlights the importance of addressing technical challenges and expanding the system's capabilities for broader application. The project offers valuable insights into the future of education, where technology and human expertise converge to create a more personalised, adaptive engaging, and effective learning experience for all students. This initiative, underpinned by the transformative potential of AI for education, aims and informing and guiding future innovations in educational technology.

Structural Analysis of an Off-Road Vehicle Suspension System Using CATIA and ANSYS

This project delves into the structural analysis of an off-road vehicle suspension system, employing cutting-edge software tools such as CATIA and ANSYS. Off-road vehicles necessitate robust suspension systems capable of withstanding harsh terrain conditions while ensuring vehicle stability, manoeuvrability, and passenger comfort. The study focuses on evaluating the structural integrity, performance, and behaviour of the suspension system under diverse load conditions and terrain scenarios. This project focuses on conducting a comprehensive structural analysis of an off-road vehicle suspension system using advanced engineering software tools, specifically CATIA and ANSYS. CATIA is a powerful computer-aided design (CAD) software known for its capabilities in creating detailed 3D models, while ANSYS is a leading finite element analysis (FEA) software used for simulating structural behaviour and analyzing mechanical systems. Keywords: suspension system, CATIA v5, ANSYS, iterations, FEA.

A comprehensive analysis of the immune system in healthy Vietnamese people

Lifestyle, diet, socioeconomic status and genetics all contribute to heterogeneity in immune responses. Vietnam is plagued with a variety of health problems, but there are no available data on immune system values in the Vietnamese population. This study aimed to establish reference intervals for immune cell parameters specific to the healthy Vietnamese population by utilizing multi-color flow cytometry (MCFC). We provide a comprehensive analysis of total leukocyte count, quantitative and qualitative shifts within lymphocyte subsets, serum and cytokine and chemokine levels and functional attributes of key immune cells including B cells, T cells, natural killer (NK) cells and their respective subpopulations. By establishing these reference values for the Vietnamese population, these data contribute significantly to our understanding of the human immune system variations across diverse populations. These data will be of substantial comparative value and be instrumental in developing personalized medical approaches and optimizing diagnostic strategies for individuals based on their unique immune profiles.

Pathways to sound management of excavated soil and rock: A case study in Shenzhen

Due to underground infrastructure and earthworks, the excavated soil and rock (ESR) emerges as the primary construction and demolition (C&D) waste stream in numerous urban areas. The transportation and disposal of ESR entail substantial costs and adverse environmental impacts, necessitating optimized management approaches. However, the environmental and economic evaluation of various ESR disposal methods remains insufficiently explored. This study presents a comprehensive analysis of a city-scale ESR management system through a case study in Shenzhen. This rapidly expanding megacity generated an annual average of 77 million m3 of ESR. We dynamically evaluate the sustainability of ESR management by harnessing a synergistic methodology that integrates material flow cost accounting (MFCA) with life cycle thinking. The findings reveal that cross-regional transportation emerges as the prioritized ESR disposal method, accounting for 72 ± 2% of the total ESR treatment, resulting in greenhouse gas (GHG) emissions of 584 ± 35 kt CO2 eq. and economic costs of 8358 ± 64 million USD per year. Scenario analysis reveals that sound recycling routes, accompanied by policy support, can yield environmental and economic benefits by reducing GHG emissions by 574 ± 55 kt CO2 eq. and generating net profits of 248 ± 15million USD per year in the coming decade. This study exemplifies the complementary methodologies supporting sustainable ESR management in Shenzhen while offering valuable insights for other cities in China and beyond.

Методология применения систематического обзора литературы в исследовании онлайн-радикализации молодежи

The article deals with the problems of online youth radicalization in modern conditions. It is indicated that soci-ologists identify a scientific problem as the absence in the Russian research field of a general theoretical and methodological concept of radicalization, especially online radicalization. The author of the article attempts a comprehensive systematic analysis of the phenomenon of online radicalization. Consequently, the article pre-sents a detailed methodology for the application of a systematic review literature in the study of online youth radicalization. The validity and advantages of using this method are pointed out. Based on the qualitative anal-ysis of the sources selected during the study, some approaches to the study of the phenomenon of online radi-calization are presented and analyzed. The necessity of conceptualizing the concept of radicalization, highlight-ing the risk factors of radicalization at various levels is revealed. One of the advantages of this work is the indi-cation of limitations that have been empirically identified and will be taken into account in future works.

Enhancing Biometric Attendance Systems for Educational Institutions

his research paper aims to contribute to the field of biometric attendance systems by conducting a comprehensive analysis of fingerprint-based attendance systems described in the provided abstracts. The focus will be on systems designed for educational institutions with an emphasis on time efficiency, user satisfaction, and accuracy in attendance tracking. The study will compare and contrast the methodologies, technologies, and implementation details presented in the abstracts, identifying strengths and weaknesses of each system. Special attention will be given to the use of Internet of Things (IoT) technology, biometric design processes, and the integration of fingerprint sensors with microcontrollers like ESP32.Furthermore, the research will explore optimization strategies to enhance the overall performance of these systems. This includes investigating the feasibility of incorporating advanced fingerprint identification algorithms, improving user interface design, and streamlining the data collection and storage processes. The experimental aspect of the research will involve implementing the proposed optimization strategies on a prototype system and evaluating its performance against the existing systems. Metrics such as fingerprint identification accuracy, average matching time, and system efficiency will be measured and compared to demonstrate the effectiveness of the proposed enhancements.