Continuous Improvement Of Technology Research Articles

Clinical practice guideline for robotic surgical system-assisted thyroid and parathyroid surgery (2024 edition)

The use of robotic operating systems is an advancement of intelligent precision, minimally invasive surgery. It has been used in the field of thyroid surgery with satisfactory results. .Robotic surgery system assisted thyroid and parathyroid surgical expert consersus(2016) as played an important role in the standardization and clinical popularization of robotic surgical system-assisted thyroid and parathyroid surgery. With the deepening of clinical practice, updates in minimally invasive concept, the replacement of robotic platforms and the continuous improvement of technology, robotic thyroid and parathyroid surgery has been further developed. Notably, it has made substantial progress in expanding indications and the training of robotic surgeons and teams. Based on the 2016 Chinese expert consensus, combined with recent related articles and clinical studies, the Clinical Practice Guideline for Robotic Surgical System-Assisted Thyroid and Parathyroid Surgery (2024 edition) was formed. The surgical team training, indications, preoperative evaluation, patient position and space establishment, thyroidectomy procedures, neck lymph node dissection skills were summarized and recommended. Furthermore, reasonable suggestions on reoperation, parathyroid surgery and management of postoperative complications were also put forward, aiming to better guide clinical practice.

Application of ion chromatographic fingerprint analysis for quality evaluation of tobacco flavors

Tobacco flavor, an important tobacco additive, is an essential raw material in cigarette production that can effectively improve the quality of tobacco products, add aroma and taste, and increase the suction flavor. The quality consistency of tobacco flavors affects the quality stability of branded cigarettes. Therefore, the quality control of tobacco flavors is a major concern for cigarette and flavor manufacturers. Physical and chemical indices, odor similarity, and sensory efficacy are employed to evaluate the quality of tobacco flavors, and the analysis of chemical components in tobacco flavors is usually conducted using gas chromatography (GC) and high performance liquid chromatography (HPLC). However, because the composition of tobacco flavors is complex, their quality cannot be fully reflected using a single component or combination of components. Therefore, establishing an objective analytical method for the quality control of tobacco flavors is of extreme importance. Chromatographic fingerprint analysis is routinely used for the discriminative analysis of tobacco flavors. Chromatographic fingerprints refer to the general characteristics of the concentration profiles of different chemical compounds. In the daily procurement process, fingerprints established by GC and HPLC are effective for the evaluation and identification of tobacco flavors. However, given continuous improvements in aroma-imitation technology, some flavors with high similarity cannot be directly distinguished using existing methods. In this study, a method for the determination of organic acids and inorganic anions in tobacco flavors based on ion chromatography (IC) was developed to ensure the quality consistency of tobacco flavors. A 1.0 g sample of tobacco flavors and 10 mL of deionized water were mixed and vibrated for 30 min. The aqueous sample solution was passed through a 0.45 μm membrane filter and RP pretreatment column in succession to eliminate interferences and then subjected to IC. Standard solutions containing nine organic acids and seven inorganic anions were used to identify the anions in the tobacco flavors, and satisfactory reproducibility was obtained. The relative standard deviations (RSDs) for retention times and peak areas were <0.71% and <6.02%, respectively. The chromatographic fingerprints of four types of tobacco flavors (samples A-D) from five different batches were obtained. Nine tobacco flavor samples from different manufacturers (samples AY1-AY3, BY1-BY2, CY1-CY2, DY1-DY2) were also analyzed to obtain their chromatographic fingerprints. Hierarchical cluster and similarity analyses were used to evaluate the quality of tobacco flavors from different manufacturers. Hierarchical clustering refers to the process of subdividing a group of samples into clusters that exhibit a high degree of intracluster similarity and intercluster dissimilarity. The dendrograms obtained using SPSS 12.0 indicated good quality consistency among the samples in different batches. Samples AY3, BY2, CY2, and DY1 clustered with the batches of standard tobacco flavors. Therefore, hierarchical cluster analysis can effectively distinguish the quality of products from different manufacturers. The Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine (version 2.0) was used to evaluate the similarity between the standard tobacco flavors and products from different manufacturers. Among the samples analyzed, samples AY3, BY2, CY2, and DY1 showed the highest similarity values (>97.7%), which was consistent with the results of the hierarchical cluster analysis. This finding indicates that IC combined with chromatographic fingerprint analysis could accurately determine the quality of tobacco flavors. GC combined with ultrasonic-assisted liquid-liquid extraction was also used to analyze the tobacco flavors and verify the accuracy of the proposed method. Compared with GC coupled with ultrasonic-assisted liquid-liquid extraction, IC demonstrated more significant quality differences among certain tobacco flavors.

Secure and Privacy-Protected Bioinformation Implementation in Air Passenger Transport Based on DLT

Aviation information systems are a key component in ensuring efficient and smooth air transport operations. In this regard, the transfer of passenger information between parties is of paramount importance. With the continuous improvement of biometrics technology, this kind of individual identification that can provide accurate and unforgeable identification is widely used in various fields. This research presents the significance and effective application scenarios of facial recognition in biometrics in air transport operations. Due to the characteristics of aviation information systems, Distributed Ledger Technology (DLT) is used in this study for secure and private transmission of facial recognition information. Distributed systems can give a transparent and secure platform to multiple parties to access sensitive passenger data. This study uses the Corda framework as the DLT that supports CorDapp development. Based on the above techniques, this study proposes two feasible application scenarios. One is a baggage match detection system to prevent misplaced baggage, and the other is an iAPIS system that transmits passenger information in real-time communication between airlines and border control agencies. This article details how to apply the research in these two scenarios, as well as the benefits and implications of the applications. Finally, this article presents an outlook for future development and feasible directions for improvement.

Safety of embryo cryopreservation: insights from mid-term placental transcriptional changes

BackgroundIn recent years, with benefits from the continuous improvement of clinical technology and the advantage of fertility preservation, the application of embryo cryopreservation has been growing rapidly worldwide. However, amidst this growth, concerns about its safety persist. Numerous studies have highlighted the elevated risk of perinatal complications linked to frozen embryo transfer (FET), such as large for gestational age (LGA) and hypertensive disorders during pregnancy. Thus, it is imperative to explore the potential risk of embryo cryopreservation and its related mechanisms.MethodsGiven the strict ethical constraints on clinical samples, we employed mouse models in this study. Three experimental groups were established: the naturally conceived (NC) group, the fresh embryo transfer (Fresh-ET) group, and the FET group. Blastocyst formation rates and implantation rates were calculated post-embryo cryopreservation. The impact of FET on fetal growth was evaluated upon fetal and placental weight. Placental RNA-seq was conducted, encompassing comprehensive analyses of various comparisons (Fresh-ET vs. NC, FET vs. NC, and FET vs. Fresh-ET).ResultsReduced rates of blastocyst formation and implantation were observed post-embryo cryopreservation. Fresh-ET resulted in a significant decrease in fetal weight compared to NC group, whereas FET reversed this decline. RNA-seq analysis indicated that the majority of the expression changes in FET were inherited from Fresh-ET, and alterations solely attributed to embryo cryopreservation were moderate. Unexpectedly, certain genes that showed alterations in Fresh-ET tended to be restored in FET. Further analysis suggested that this regression may underlie the improvement of fetal growth restriction in FET. The expression of imprinted genes was disrupted in both FET and Fresh-ET groups.ConclusionBased on our experimental data on mouse models, the impact of embryo cryopreservation is less pronounced than other in vitro manipulations in Fresh-ET. However, the impairment of the embryonic developmental potential and the gene alterations in placenta still suggested it to be a risky operation.

Automation in Healthcare: Advantages, Prospects, Perceptual Barriers

Introduction. In healthcare, automation refers to the use of technology to replace manual labor with mechanized processes. It offers solutions to some of the biggest challenges in this field by increasing work efficiency and improving the quality of medical care. The aim of the study was to explore the potential impact of automation on healthcare practices and to investigate the perceptions of healthcare professionals towards these innovations. Materials and methods. The study utilized general scientific research methodologies, including analysis and synthesis. Through the examination of secondary data,, the findings of a sociological survey conducted by Google Cloud commissioned by Harris Poll were analyzed and presented in the context of the studied topic. The questionnaire was filled in by 300 doctors across the United States. Results. Over the next years, global automation in healthcare is expected to see a significant growth due to various growth factors including the continuous improvement of healthcare automation technologies, growing public investment, as well as recognition of the benefits of automation in medical logistics processes, laboratory research and other types of medical and related activities. Automation, such as information technology and control systems, reduces individual efforts in multiple procedures.

Research on U-band Fiber Lasers

The U-band fiber laser has attracted the attention of many researchers and industry due to its outstanding performance characteristics. U-band fiber laser is a type of laser that operates in the ultraviolet wavelength range, and its research background mainly stems from the demand for ultraviolet lasers in scientific research, medical treatment, material processing, and other fields. However, traditional ultraviolet lasers often use gas laser media, which have large volume, high power consumption, and high maintenance costs, limiting their promotion in practical applications. Therefore, researchers have begun to explore the use of fiber optic technology to achieve miniaturization, efficiency, and stability improvement of ultraviolet lasers. This article provides an overview of the working principle and application fields of U-band fiber lasers and proposes the future development direction of U-wave fiber lasers. Intended to provide useful references for further research on fiber lasers. Through continuous technological innovation and process improvement, this paper promote the further development of U-band fiber laser technology, expand its application areas, improve performance and stability, and promote its widespread application in industrial and scientific research fields.

Optimization Based on Computer Fluid Dynamics Finite Element Method Automotive Cooling Fan Structure

With the continuous improvement of vehicle comprehensive technology, the heat load generated by automotive engine systems, hydraulic systems, and other devices also increases. Therefore, improving the heat dissipation efficiency of the cooling system is a key technology that cannot be ignored in the current vehicle development process. The car cooling fan is an important component of the cooling system, and its working characteristics will directly affect the cooling effect of the engine. This article uses the finite volume method of computer fluid dynamics to optimize the structure of a heat dissipation fan, analyze and define the flow field control volume required for its numerical calculation, use structured and unstructured grids to discretize the internal flow field region, select pressure inlet and pressure outlet boundary conditions, and simulate and analyze the fluid flow in the radiator based on fluid dynamics theory calculations. The orthogonal experimental method was used to analyze various performance indicators during the experimental process. The results showed that the comprehensive performance of the No. 3 optimized cooling fan was the best, with a 10.45% increase in static pressure efficiency, a 14.03% increase in dynamic pressure efficiency, and an 11.26% increase in maximum flow velocity. The optimized fan flow field streamline became more full and uniform, significantly improving the fluid dynamics performance of the air in the cooling system.

2D Materials‐Based Pulsed Solid‐State Laser: Status and Prospect

AbstractPulsed solid‐state lasers comprise 2D materials as saturable absorbers that contain transparent windows of the atmosphere and characteristic fingerprint spectra of several vital molecules that are significant in various applications and research. Over the past few decades, significant progress has been made in the development of narrow pulse width, high energy, high average output power, high efficiency, and simple construction of passively Q‐switched and mode‐locked lasers with 2D materials as saturable absorbers. This review summarizes the development of 2D materials, including graphene, transition metal dichalcogenides, black phosphorus, topological insulators, and MXenes, as modulator devices for solid‐state lasers owing to their broadband operation, excellent nonlinear optical response, low recovery time, ultrafast dynamic processing, and easy fabrication. Then, some new emerging and representative applications of pulsed solid‐state lasers are introduced and illustrated such as laser surgery, material processing, and lidar. Finally, future challenges and perspectives of pulsed solid‐state lasers with 2D materials‐based saturable absorbers are analyzed and addressed. The rapid development of pulsed solid‐state lasers with the continuous improvement of modulation technology is expected to expand opportunities for application in industry, scientific, medical, and other areas.

THE EFFECTIVENESS OF VIRTUAL REALITY AND AUGMENTED REALITY IN DIGITAL MARKETING CAMPAIGNS

In the rapidly changing landscape of digital marketing now, it is vital to go above the conventional smartphone to find ways to improve engagement rates. Virtual Reality (VR) transports users to digitized worlds and deepens connections with brands and products. Augmented Reality (AR) easily integrates digital components into reality, providing engaging and immersive product interactions. Businesses face obstacles such as technological limitations and high costs, but solutions are not far away. Continuous technological improvements and creative marketing methods are paving the way for immersive consumer experiences. This article explores the effectiveness of AR and VR on digital marketing, reviewing case studies and gauging out challenges to look for solutions. KEYWORDS: Augmented Reality, Virtual Reality, Digital Marketing, Consumer Engagement

Clinical significance and correlation of compliance to thirst in maintenance hemodialysis patients.

The main treatment method for end-stage renal disease (ESRD) is maintenance hemodialysis (MHD). With the continuous improvement of dialysis technology, the survival period of MHD patients has been effectively prolonged, but dialysis technology still cannot completely replace renal function. To study the dietary compliance and its correlation with thirst in MHD patients and to provide guidance for clinical development of corresponding intervention countermeasures. A total of 90 patients who received MHD treatment from March 2021 to March 2022 were selected as objects. The Renal Adherence Attitudes Questionnaire (RAAQ) and the Renal Adherence Behaviour Questionnaire (RABQ) were used to analyze the dietary compliance and thirst status of patients. Pearson correlation analysis was used to analyze the correlation between diet compliance and thirst. Positive correlations were found between VAS and DTI, SXI and TDS (P< 0.05). Social restrictive attitude was positively correlated with VAS, DTI, SXI, TDS, acceptance attitude and compliance in facing difficulties (P< 0.05), and negatively correlated with self-care compliance (r=-0.35, P< 0.05). Health attitude was positively correlated with VAS, DTI and SXI (P< 0.05). Acceptance attitude was positively correlated with DTI, SXI and TDS (P< 0.05). High RAAQ was associated with high VAS (b= 0.11, 95% CI: 0.05, 0.18), DTI (b= 0.28, 95% CI: 0.17, 0.38), SXI (b= 0.24, 95% CI: 0.14, 0.34) and TDS (b= 0.26, 95% CI: 0.13, 0.4). The overall performance of dietary compliance in patients with MHD is at a moderate level, and dietary compliance is negatively correlated with disease perception.

Advancements in Video Deblurring: A Comprehensive Review

Video deblurring is a critical task in computer vision, aimed at restoring the clarity of videos distorted by motion blur or other factors. It holds immense importance across various domains, including surveillance, entertainment, medical imaging, and autonomous driving, where clear visual information is crucial for decision-making and analysis. This article provides an overview of recent advancements in video deblurring techniques, ranging from convolutional neural network (CNN)-based methods to Transformer-based models and event-based reconstruction approaches. By synthesizing insights from recent research, this review delves into the applications and methodologies of these techniques, showcasing their effectiveness in real-world scenarios. By fostering knowledge exchange and inspiring further advancements in the field, this review aims to contribute to the continuous improvement of video processing technologies for enhanced visual quality and analysis. Keywords: Video Deblurring, Video Restoration, deep learning, Convolutional Neural Networks, Event Based Reconstruction, Transformers, Image Processing

The Impact of Blockchain Technology on Financial Markets and Its Future Trends: An Economic Perspective Based on Data Analysis

Based on the economic perspective of data analysis, this paper deeply discusses the impact of blockchain technology on the financial market and its future trend. By using econometrics, we systematically analyze how blockchain technology can change the operation mechanism of financial markets, improve transaction efficiency, reduce transaction costs, and enhance the security and transparency of financial markets. The study has found that the decentralized nature of blockchain technology can help to break down the barriers of the traditional financial market and promote the popularization and inclusiveness of financial services. At the same time, the automatic execution function of smart contracts simplifies the process of financial transactions and reduces the risk of human intervention and fraud. In addition, the blockchain technology has also enhanced the trust mechanism of the financial markets by improving the security and traceability of the data. In terms of econometric analysis, we use a large amount of financial data to empirically test the impact of blockchain technology on the performance of the financial market by building regression models, time series analysis and other methods. The results show that there is a significant positive correlation between the application of blockchain technology and financial market efficiency, liquidity and stability. Looking into the future, with the continuous innovation and improvement of blockchain technology, its application in the financial market will be more extensive and deeper. On the one hand, blockchain technology is expected to promote the further digitalization and intelligence of the financial market, and improve the convenience and personalization of financial services. On the other hand, blockchain technology will also promote the cross-border integration and innovation of the financial market, and inject new vitality into the global economic development.

English Speaking Skill through Rosetta Stone Application at Junior High School: Students’ Voices

This study aims to explore the impact of utilizing the Rosetta stone application as a means to improve English speaking skills among second-grade junior high school students. The study employs a qualitative case study approach and data content analysis to delve deeply into the experiences and perceptions of the students regarding their English-speaking skill development through the application. Using a modified questionnaire and depth interviews, the researcher collected data through share the questionnaires to 34 students and interviews conducted with three participants. The results reveal that the majority of students express positive perceptions about the application's impact on English speaking abilities, citing improvements in confidence, vocabulary, accent, and overall learning experience. Overall, these findings underline the dynamic nature of language learning and the potential for continuous improvement in educational technology through targeted enhancements and further empirical exploration. These findings underscore the effectiveness of integrating technology like Rosetta Stone in language learning environments, offering diverse speaking practice opportunities, immersive learning experiences, and targeted feedback that contribute to overall language proficiency. The findings of this study could be useful for language learners who are seeking to improve their language skills as well as for educators and developers who are interested in creating effective language learning tools.

Application of Machine Learning for Shale Oil and Gas “Sweet Spots” Prediction

With the continuous improvement of shale oil and gas recovery technologies and achievements, a large amount of geological information and data have been accumulated for the description of shale reservoirs, and it has become possible to use machine learning methods for “sweet spots” prediction in shale oil and gas areas. Taking the Duvernay shale oil and gas field in Canada as an example, this paper attempts to build recoverable shale oil and gas reserve prediction models using machine learning methods and geological and development big data, to predict the distribution of recoverable shale oil and gas reserves and provide a basis for well location deployment and engineering modifications. The research results of the machine learning model in this study are as follows: ① Three machine learning methods were applied to build a prediction model and random forest showed the best performance. The R2 values of the built recoverable shale oil and gas reserves prediction models are 0.7894 and 0.8210, respectively, with an accuracy that meets the requirements of production applications; ② The geological main controlling factors for recoverable shale oil and gas reserves in this area are organic matter maturity and total organic carbon (TOC), followed by porosity and effective thickness; the main controlling factor for engineering modifications is the total proppant volume, followed by total stages and horizontal lateral length; ③ The abundance of recoverable shale oil and gas reserves in the central part of the study area is predicted to be relatively high, which makes it a favorable area for future well location deployment.

Study on the detonation wave propagation of shaped charge with three-layer liner and its driving characteristics to liner

With the continuous improvement of various armor protection technologies, the armor protection performance has increased significantly, and then the damage performance requirements of armor-piercing ammunition have also increased. In order to improve the penetration ability of the liner, a new three-layer liner structure is designed in this paper. The jet forming process was simulated by AUTODYN software. The mechanism of shaped jet forming of three-layer liner was studied. The reason why the penetration depth of three-layer liner was higher than that of ordinary liner was explained. The influence of three-layer liner on the propagation of detonation wave and the change of pressure when detonation wave acted on liner were found, which provided a new idea for improving the penetration depth of jet. The influence of liner material, cone angle and stand-off on jet forming and penetration was also studied by orthogonal optimization experiment, and the structural parameters with the best penetration performance were obtained. The results show that the pressure at the convergence point increases first and then decreases during the formation of the jet of the three-layer liner. The pressure at the convergence point when the three-layer liner material is from low impedance to high impedance from the outside to the inside is much larger than the pressure at the convergence point from high impedance to low impedance. When the three-layer liner material is Al 2024-Copper–Tantalum from the outside to the inside, the pressure at the convergence point of the three-layer liner at different times is higher than that of the double-layer liner and the single-layer liner. Reasonable matching of different impact impedance materials in the three-layer liner can greatly improve the pressure value of the detonation wave acting on the cone liner. The maximum pressure at the convergence point on the axis is 39.10 GPa, which is 22.00% higher than that of the double-layer liner at the convergence point, and 53.03% higher than that of the single-layer liner at the convergence point. The orthogonal design test scheme is simulated and analyzed. The penetration depth is taken as the observation index, and the range analysis is adopted. The results show that the material matching of the three-layer liner has the greatest influence on the depth of the jet penetrating the target plate, followed by the cone angle of the three-layer liner. Relatively speaking, the stand-off has the least influence on the result. Reasonable matching of materials with different impact impedances in the three-layer liner can maximize the penetration depth of the jet into the target plate.

Feasibility of Using Seq-GAN Model in Vulnerability Detection of Industrial Control Protocols

Continuous improvement of internet technology has driven the continuous progress and improvement of industrial control systems, and provided more support for security vulnerability detection in this field. Combining the GAN model, the detection model based on Seq-GAN in industrial control protocol vulnerabilities constructed in this article provides more options for further improving the security of industrial control systems, and can detect and analyse security vulnerabilities in industrial control protocols more efficiently and accurately. By comparing the performance of different models for security vulnerability detection, the Seq-GAN model has smaller prediction errors, can also obtain higher G-mean and F1-score values, and has sufficient reliability. At the same time, it can also improve the efficiency of vulnerability detection in industrial control systems, and can achieve better comprehensive detection performance. Therefore, the application of the Seq-GAN model in industrial control protocol vulnerability detection can provide more support for improving security detection in this field.

Construction and Analysis of QPSO-LSTM Model in Network Security Situation Prediction

The continuous improvement of artificial intelligence technology has deepened its application in many fields and provided more support for predicting network security situations. QPSO-LSTM model based on LSTM neural network and fused with QPSO algorithm provides more options for improving network security situation prediction, further enhancing the effectiveness of network security situation prediction, and enabling more efficient and accurate prediction and analysis of network security situations. By comparing the applications of different types of algorithms in network security situation prediction, it was found that the QPSO-LSTM model has smaller prediction errors, can achieve higher prediction accuracy, and can also obtain higher F1-score and AUC values; the shorter identifying runtime also lays the foundation for improving the speed and efficiency of network security situation prediction. Therefore, in the field of network security situation prediction, the application of QPSO-LSTM model can provide more support for further improvement and improvement of security situation prediction performance in this field.

The path from task-specific to general purpose artificial intelligence for medical diagnostics: A bibliometric analysis

Artificial intelligence (AI) has revolutionized many fields, and its potential in healthcare has been increasingly recognized. Based on diverse data sources such as imaging, laboratory tests, medical records, and electrophysiological data, diagnostic AI has witnessed rapid development in recent years. A comprehensive understanding of the development status, contributing factors, and their relationships in the application of AI to medical diagnostics is essential to further promote its use in clinical practice. In this study, we conducted a bibliometric analysis to explore the evolution of task-specific to general-purpose AI for medical diagnostics. We used the Web of Science database to search for relevant articles published between 2010 and 2023, and applied VOSviewer, the R package Bibliometrix, and CiteSpace to analyze collaborative networks and keywords. Our analysis revealed that the field of AI in medical diagnostics has experienced rapid growth in recent years, with a focus on tasks such as image analysis, disease prediction, and decision support. Collaborative networks were observed among researchers and institutions, indicating a trend of global cooperation in this field. Additionally, we identified several key factors contributing to the development of AI in medical diagnostics, including data quality, algorithm design, and computational power. Challenges to progress in the field include model explainability, robustness, and equality, which will require multi-stakeholder, interdisciplinary collaboration to tackle. Our study provides a holistic understanding of the path from task-specific, mono-modal AI toward general-purpose, multimodal AI for medical diagnostics. With the continuous improvement of AI technology and the accumulation of medical data, we believe that AI will play a greater role in medical diagnostics in the future.

Enhanced MADM framework for fuzzy comprehensive evaluation of attack ability of basketball defenders with the triangular fuzzy neutrosophic numbers

With the continuous improvement of modern basketball technology, higher requirements have been put forward for the personal abilities of basketball players. As the core of an organization, the offensive ability of a defender largely determines the team’s performance. Therefore, it is necessary to objectively evaluate the attacking ability of defenders. Traditional techniques cannot objectively reflect the true level of players due to their strong subjectivity. Therefore, establishing a scientific evaluation technique is particularly important. The fuzzy comprehensive evaluation of attack ability of basketball defenders is viewed as the multi-attribute decision-making (MADM). In this paper, the triangular fuzzy neutrosophic number cross-entropy (TFNN-CE) technique is designed with help of cross-entropy and triangular fuzzy neutrosophic sets (TFNSs). Furthermore, Then, TFNN-CE technique is addressed to solve the MADM. Finally, a numerical example for fuzzy comprehensive evaluation of attack ability of basketball defenders is given and some comparisons are conducted to r illustrate advantages of the designed technique. The main contribution of this paper is addressed: (1) The TFNN-CE technique is addressed under TFNSs; (2) the TFNN-CE technique is addressed for MADM under TFNSs; (2) the TFNN-CE technique for fuzzy comprehensive evaluation of attack ability of basketball defenders is addressed; (3) Through the several efficient comparisons, it is addressed that TFNN-CE technique is effective for fuzzy comprehensive evaluation of attack ability of basketball defenders.

Aluminum Continues to Shine in Commercial Aircraft Applications

Abstract During the 20th century, the use of aluminum alloys helped the Allied Powers win World War II and made modern global air travel possible. Continuous improvements in engine technology, alloys, and manufacturing methods enabled the development of practical and efficient aircraft with varying passenger capacity and range capability. Conventional wrought aluminum alloys make up 70-80% of the weight of single-aisle airliners. Aluminum sheet, plate, forgings, extrusions, and castings all continue to be utilized in modern aircraft construction. This article explores a brief history of the special alloys, tempers, and product forms required to meet the unique challenges of flight.