Open Access
pengyuan li 
, qingquan xu , cheng huang , haoqing wang , yisen wang , zibo wang , yibo zhang , ao wang , tianci cui , xinyue ni , yutong wang , chen gong |
Available online: 03-29-2024
C4 olefin is an important chemical raw material, but traditional production methods use limited and polluting fossil energy as raw materials. Ethanol stands out from many alternative energy sources because of its wide sources, easy conversion, and low pollution. The preparation of C4 olefins from ethanol has become an effective alternative route for olefin production, which has great environmental and economic value. Metal oxides are the main catalysts for the preparation of C4 olefins from ethanol. In this paper, a $\mathrm{SiO}_2$-$\mathrm{HAP}$ catalyst with both acid and base active sites was designed, and Co metal with dehydrogenation activity was supported on its surface. To improve the catalytic activity and improve the conversion of ethanol and the selectivity of C4 olefin, experiments were carried out by changing the process parameters such as $\mathrm{Co}$ loading (weight ratio of $\mathrm{Co}$ to $\mathrm{SiO}_2$), HAP (hydroxyapatite) mass, ethanol concentration, and reaction temperature. The improved gray relational degree algorithm was used to analyze the relational degree of process parameters with ethanol conversion and C4 olefin selectivity. Experimental results show that the detection accuracy of this algorithm for C4 olefin selectivity is better than that of the traditional algorithm without considering the difference in change rate between data, the detection accuracy is improved by 50%, and the detection accuracy of ethanol conversion is improved by 2%.
The decentralised nature of cryptocurrency, coupled with its potential for significant financial returns, has elevated its status as a sought-after investment opportunity on a global scale. Nonetheless, the inherent unpredictability and volatility of the cryptocurrency market present considerable challenges for investors aiming to forecast price movements and secure profitable investments. In response to this challenge, the current investigation was conducted to assess the efficacy of three Machine Learning (ML) algorithms, namely, Gradient Boosting (GB), Random Forest (RF), and Bagging, in predicting the daily closing prices of six major cryptocurrencies, namely, Binance, Bitcoin, Ethereum, Solana, USD, and XRP. The study utilised historical price data spanning from January 1, 2015 to January 26, 2024 for Bitcoin, from January 1, 2018 to January 26, 2024 for Ethereum and XRP, from January 1, 2021 to January 26, 2024 for Solana, and from January 1, 2019 to January 26, 2024 for USD. A novel approach was adopted wherein the lagging prices of the cryptocurrencies were employed as features for prediction, as opposed to the conventional method of using opening, high, and low prices, which are not predictive in nature. The data set was divided into a training set (80%) and a testing set (20%) for the evaluation of the algorithms. The performance of these ML algorithms was systematically compared using a suite of metrics, including R2, adjusted R2, Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The findings revealed that the GB algorithm exhibited superior performance in predicting the prices of Bitcoin and Solana, whereas the RF algorithm demonstrated greater efficacy for Ethereum, USD, and XRP. This comparative analysis underscores the relative advantages of RF over GB and Bagging algorithms in the context of cryptocurrency price prediction. The outcomes of this study not only contribute to the existing body of knowledge on the application of ML algorithms in financial markets but also provide actionable insights for investors navigating the volatile cryptocurrency market.
To address the rate matching issue between high-bandwidth and high-sampling-rate analog-to-digital converters (ADCs) and low-bandwidth and low-sampling-rate baseband processors, the key technology of digital downconversion is introduced. This approach relocates the intermediate-frequency baseband signal to a vicinity of the baseband, laying a foundation for subsequent Digital Signal Processor (DSP) analysis and processing. In an innovative application of the Coordinate Rotation Digital Computer (CORDIC) algorithm for Numerically Controlled Oscillator (NCO) in a pipeline design, the phase differences of five parallel signals are measured, facilitating real-time parallel processing of the phase and amplitude relationships of multiple signals. The Field Programmable Gate Array (FPGA) design and implementation of the digital mixer module and filter bank for digital downconversion have been accomplished. A test board for the direction-finding application of five digital downconversion channels has been constructed, with the FMQL45T900 as its core. The correctness of the direction-finding data has been validated through practical application, demonstrating a significant improvement in power consumption compared to methods documented in other literature, thereby enhancing overall efficiency. The digital downconversion technology based on the CORDIC algorithm is applicable in various fields, including military communications, broadcasting, and radar navigation systems.
In the context of urban planning, the design of urban spaces is recognized as a pivotal factor influencing urban sustainability, with a particular emphasis on inclusivity for individuals requiring special assistance. This study explores the interconnectedness of urban design with sustainability indicators, focusing on human-centric dimensions and the preservation of heritage within Najaf's traditional urban fabric. Through the application of Structural Equation Modeling (SEM) via the Analysis of Moment Structures (AMOS) software, this research aims to elucidate the significance and interrelations of specific urban design indicators, thereby determining their collective impact on urban sustainability. The methodology adopted herein leverages quantitative analysis to delineate the relationships among urban design parameters and their consequential influence on sustainability outcomes. The findings suggest a substantial correlation between urban design practices and the attainment of sustainability, with a notable emphasis on the design factor as a primary influencer. This research contributes to the discourse on urban sustainability by providing a methodological framework for assessing the role of urban design in fostering inclusive and sustainable urban environments. The study underscores the potential of SEM in elucidating the complex dynamics between urban design and sustainability, thereby offering empirical evidence to support the development of informed urban planning strategies.
An innovative framework is introduced for the enhancement of efficiency within emergency departments (EDs), utilizing an integration of simulation and fuzzy Multi-Criteria Decision-Making (MCDM). A discrete event simulation (DES) model was developed, capturing the intricate dynamics characteristic of ED operations with high fidelity. This model's integration with the Analytic Hierarchy Process (AHP) and the Elimination and Choice Expressing Reality (ELECTRE) method, within a fuzzy context, facilitated a critical evaluation and optimization of the decision-making processes inherent in EDs. The incorporation of these methodologies yielded significant improvements in patient flow and service quality, highlighting the substantial potential of marrying simulation with fuzzy MCDM to achieve operational excellence in healthcare settings. The study stands as a contribution to the enhancement of ED operations, offering a versatile methodology with potential for adaptation across diverse healthcare environments. This approach underscores the imperative of employing a nuanced, integrated strategy to navigate the complexities of healthcare service delivery, ensuring an equilibrium between operational efficiency and the quality of patient care.
In the realm of cybersecurity, the formulation of comprehensive strategies is imperative for multinational corporations to protect against pervasive cyber threats. Recent developments in the field of intuitionistic multi-fuzzy sets (IMFSs) have heralded q-rung orthopair multi-fuzzy sets (MFSs) as a pivotal tool for encapsulating ambiguity and uncertainty within complex scenarios. The essence of this study lies in the introduction of two innovative distance measures tailored for q-rung orthopair MFSs (q-ROM$^{k}$FSs) of dimension k, enhancing the capacity to delineate distinctions between such sets effectively. Employing score functions pertinent to q-ROM$^{k}$FSs, this research extends its application to the sphere of Multi-Attribute Decision Making (MADM), presenting a methodological advancement in decision-making processes. The efficacy of the proposed measures is elucidated through a comparative analysis with existing methodologies in MADM, thereby underscoring the superiority of the introduced approach. This investigation not only contributes to the enrichment of the theoretical underpinnings of q-ROMFSs but also propels their practical application in cybersecurity strategy formulation for multinational entities. The study employs the Euclidean and Hamming distance measures as benchmarks, supplemented by the development of a score and accuracy function, to furnish a comprehensive tool for addressing cybersecurity challenges.
In the realm of global economics, tourism emerges as a pivotal sector, demanding strategic planning and policy formulation for sustainable development. The prosperity of tourism destinations is contingent upon the inclusivity of stakeholder perspectives, especially those impacted by the tourism industry. While substantial research has delved into local residents' perceptions of tourism development, the viewpoints of foreign residents remain conspicuously underexplored. This oversight necessitates an investigation into the nuanced impacts of tourism development, particularly within the Chinese context. A comprehensive questionnaire survey was administered to gauge the perceptions of overseas residents regarding tourism development in Qingdao, a prominent tourist locale in Eastern China. Findings indicate that perceptions among this demographic are heterogeneous, influenced by factors such as age, income, and personal affiliations with the tourism sector. It is demonstrated that the economic, socio-cultural, and environmental impacts of tourism are perceived variably, contingent upon these demographic variables. This analysis underscores the importance of integrating diverse resident perspectives into tourism planning and policy-making, to foster sustainability in tourism destination development. Such an approach is essential for aligning tourism development with the expectations and well-being of both local and foreign residents, thereby ensuring the long-term viability of tourism destinations. This study contributes to the body of knowledge by filling a critical gap in understanding the impacts of tourism development from the perspective of overseas residents in Qingdao, thus offering valuable insights for stakeholders in crafting inclusive and sustainable tourism strategies.
Tuberculosis (TB), an airborne disease caused by Mycobacterium, poses a significant global health challenge due to its rapid transmission through air and interaction with infected individuals. This study presents a comprehensive dynamic model to assess the impact of TB treatment and vaccination strategies in Nigeria, focusing on the comparative analysis of untreated and treated populations, as well as evaluating mortality and recovery outcomes. Through simulations conducted using the Berkeley Madonna Software, it was observed that the populations of latent and susceptible individuals exhibit a near-equivalence, yet the cohort undergoing treatment markedly surpasses other groups. Interestingly, the infected demographic aligns closely with the average values across all compartments. An alarming trend was noted in chronic patients, whose numbers initially increase, followed by a decline over a six-year period, and then a subsequent rise, while the count of treated individuals demonstrates a continuous decrease. The study further reveals a pressing need for treatment among vaccinated individuals, highlighting a nuanced interplay between vaccination and therapeutic interventions. By employing stability and sensitivity analyses, this research underscores the critical importance of treatment in managing TB infection, advocating for enhanced strategies to mitigate the spread of this infectious disease. The findings contribute valuable insights into the dynamics of TB infection and the pivotal role of treatment, underscoring the necessity for integrated approaches in addressing the TB epidemic, particularly in regions burdened by high infection rates.
In the realm of general insurance in India, an econometric investigation was conducted to estimate the revenue efficiency across a selection of 15 prominent, diversified general insurance entities for the fiscal years 2011-12 to 2016-17. Utilizing a semi-parametric methodology, the revenue frontier was constructed under the GAM framework, while the variance components were estimated employing the method of moments. This analysis further explored the influence of revenue efficiency on critical profitability metrics, namely return on equity (ROE) and return on assets (ROA), through the application of instrumental variable regression. The findings provide pivotal insights into the dynamics of revenue efficiency and its consequential impact on the financial performance of general insurance companies in India, offering a substantial contribution to the literature on insurance economics and the methodology of efficiency measurement. The research underscores the significance of adopting semi-parametric models for a nuanced understanding of revenue efficiency, thus paving the way for enhanced strategic decision-making in the insurance sector.
Seismic performance is a critical consideration in the design and assessment of reinforced concrete bridges. Ensuring the structural integrity and safety of bridges under seismic loadings is essential to protect public safety and maintain the longevity of these vital infrastructure components. The objective of this research study was to evaluate the seismic performance of a multi-span reinforced concrete bridge located in Pan Borneo Highway Sarawak. The non-linear static pushover analysis provided valuable insights into the bridge's load resistance. It determined that the bridge could withstand a base shear force of up to 30,130.899 kN before collapsing, indicating its high structural capacity. The capacity curve analysis further demonstrated the ability of bridge to endure spectral accelerations of up to 4.44 g (43.512 m/s$^2$), indicating its robustness against high-intensity ground motions. In addition, the non-linear static time history analysis considered three ground motions and their effects on the bridge's structural performance. The study highlighted the bridge's sensitivity to different external forces, with varying responses observed under different ground motions. Notably, the recorded joint acceleration and displacement values were found to be within acceptable limits, ensuring immediate occupancy and life safety for bridge users. The research study successfully evaluated the seismic performance of a reinforced concrete bridge in Pan Borneo Sarawak using non-linear time history and pushover analyses. The results demonstrated the bridge's satisfactory capacity to withstand seismic loadings. The utilization of CSIBridge software provided valuable insights into the bridge's structural integrity and behavior under seismic conditions. These findings contribute to the advancement of bridge engineering practices.
Solar energy, a ubiquitous and environmentally friendly source, plays a pivotal role in mitigating carbon emissions and reducing air pollution. This study evaluates the performance of a 50-watt monocrystalline solar panel over a thirty-day period in October 2022, within Merauke Regency, South Papua Province, Indonesia. Adopting an experimental research methodology and comprehensive data collection, measurements of solar intensity, temperature, voltage, and current were systematically gathered using temperature sensors, ammeters, and voltmeters. These measurements were obtained by positioning the solar panel at a perpendicular angle to direct sunlight, with data recorded between 9:00 and 16:00 Eastern Indonesia Time. The analysis of the collected data was conducted to ascertain the panel's efficacy, revealing an average output of 20.68 volts, 1.95 amperes, 40.37 watts, and a 9% efficiency. Notably, peak performance was observed on the tenth day, characterized by 21.30 volts, 2.24 amperes, 47.71 watts, and an efficiency of 11.01%. The findings of this investigation are anticipated to inform the installation and utilization strategies of similar solar panel types within Merauke Regency and potentially broader applications. This study underscores the critical influence of solar irradiance on the operational performance of monocrystalline silicon solar panels, contributing valuable insights to the field of renewable energy research.
The accelerating process of globalization has led to an increase in freight transport volumes, exacerbated road congestion, and heightened environmental concerns, underscoring the imperative for sustainable and alternative transport solutions. Intermodal transport, which amalgamates the benefits of various modes of transportation, emerges as a paramount solution to these challenges. At the core of intermodal transport lies the intermodal terminal, whose efficiency and efficacy are critically contingent upon the transshipment technology employed. This investigation is dedicated to the evaluation of transshipment technologies within intermodal terminals. It is recognized that the selection of transshipment technology necessitates consideration of diverse criteria, mandating the application of appropriate multi-criteria decision-making methodologies. To address this complexity, a novel hybrid model, integrating Fuzzy Step-Wise Weight Assessment Ratio Analysis (FSWARA) with Axial-Distance-Based Aggregated Measurement (ADAM), is proposed. The efficacy of this model is demonstrated through its application in assessing various transshipment technologies, with an emphasis on optimizing the decision-making process in the selection of the most appropriate technology. This study contributes to the body of knowledge by providing a comprehensive framework for the evaluation of transshipment technologies in intermodal terminals, facilitating enhanced decision-making in the context of sustainable and efficient intermodal transport systems.
Medical waste is recognized as a significant environmental and public health hazard due to its toxic and chemical constituents. In light of the varying standards for medical waste management within Jordan and comparisons with neighboring countries, this study aims to critically assess the existing management practices in Jordanian hospitals, utilizing a comprehensive database. The study further explores treatment technologies to enhance these practices. The effectiveness of Failure Mode and Effects Analysis (FMEA) in identifying and mitigating potential risks in the disposal process of infectious medical waste is also examined. Findings suggest that management procedures exhibit regional disparities influenced by factors such as the geographical location of the healthcare institution, its operational scale, and prevailing political circumstances. Moreover, the application of FMEA was found to significantly mitigate operational risks, as evidenced by reduced Risk Priority Number (RPN) values. Challenges identified include the need for increased resources, improved training, and enhanced systems for hazardous waste management. The study underscores the importance of public awareness in elevating medical waste management standards. These insights contribute to the broader discourse on environmental health and safety in medical waste management, advocating for systemic improvements in Jordanian healthcare facilities (HCFs).
