Application Of Technology Research Articles

Cyclohepta[def]fluorene as a bistable molecule: first principles studies on its electronic structure and the effects of benzo-extension, substitution and solvation.

Cyclohepta[def]fluorene 1 and its derivatives have received considerable attention due to possible technological applications as molecular devices. Despite efforts from both theory and experiment, the electronic structure of 1 has remained unclear. Herein, we report advanced first-principles calculations on 1 using a multireference and a coupled-cluster method. We confirm a bistability of 1 between a polar singlet state and a non-polar triplet state. We also study the effects of benzo-extension on the electronic structure and the influences of substitution and solvation on the ground state. Our results suggest that deliberate choice of substituents allows to toggle the multiplicity of the ground state. We also propose that due to its bistability, 1 represents an attractive building block for molecular devices.

Innovative Application of Self-Healing Technology to Masonry: A Proof of Concept

ABSTRACT Cracks represent a prevalent form of damage in masonry structures, posing not only aesthetic concerns but also compromising structural durability; therefore, they are undesirable and need to be repaired. The repointing technique is traditionally implemented in this context, especially in historical masonry. However, this method fails to provide a long-term solution, leaving structures vulnerable to future damage. The paper investigates the applicability of a bio-based self-healing mortar to enable autonomous repair of masonry. This innovative mortar, developed to repair concrete structures, was implemented to explore the capacity of couplets to recover their original bond capacity and aesthetic aspect after multiple damaging events. Specimens built with calcium-silicate and clay bricks were subjected to subsequent cracking cycles using a crack-mouth-opening-displacement controlled bond-wrench test. Experimental results showed that self-repair, in terms of bond restoration and aesthetic filling of cracks, occurs even after multiple cracking cycles when the bio-based mortar is used with both types of bricks, optimizing the autogenous healing (intrinsic) of cement-based mortars. The effectiveness varied also according to the types of brick and healing environment used, e.g. under humid conditions (RH ~ 95%), 50% vs 80% of the original capacity was regained in fully separated couplets made respectively with clay and calcium-silicate bricks.

Research on the construction and application of a smart library services maturity evaluation system based on CMM

Purpose Smart libraries are the result of the application of smart technologies in the era of digital intelligence. The establishment and improvement of its service evaluation system serve as indicators for evaluating the growth of smart libraries. Design/methodology/approach This study introduces and improves the capability maturity model (CMM), creatively constructs a service maturity model specifically designed for smart libraries and combines the Delphi method with the analytic hierarchy process (AHP) to establish a service maturity evaluation system for smart libraries while calculating indicator weights. Finally, two representative smart libraries are selected as case studies, and an empirical application is conducted using the fuzzy comprehensive evaluation method. Findings The empirical study shows that the developed smart libraries service maturity evaluation system holds significant theoretical and practical value in evaluating smart libraries. Originality/value Enhances the CMM and creatively constructs a service maturity model for smart libraries. Combines the Delphi method with AHP to establish a service maturity evaluation system while calculating indicator weights. Uses a fuzzy comprehensive evaluation method to evaluate two representative smart libraries. Demonstrates that the smart library services maturity evaluation system holds significant theoretical and practical value.

Analysis of the Application Value of Dynamic Navigation Guidance in Oral Implantation.

To investigate the application of dynamic navigation guidance technology in different implantation scenarios, and to provide a scientific basis for the innovation and advancement of implantation techniques. Fifteen cases of patients with malocclusions admitted between January 2021 and February 2023 were selected as the study subjects. All patients underwent dynamic navigation-guided oral implantation interventions. CBCT scans were taken after implantation surgery to record deviations of implantation points, including implantation point deviation, endpoint deviation, and angle deviation. Dynamic navigation guidance effectively improves the reliability and stability of implantation in oral implant patients. CBCT effectively evaluated the state of the patient's periodontal implant, analyzed the state of the patient's lesion area, and improved the quality of implant intervention through CBCT guidance. The implantation point deviation was (0.407±0.193) mm, the endpoint deviation was (0.492±0.201) mm, and the implant angle deviation was (2.162±0.283)°. There was no significant difference in implantation point deviation in the anterior and posterior parts of the upper and lower jaws after intervention (P>0.05). However, there were significant differences in endpoint deviation and implant angle deviation among the anterior and posterior parts of the upper and lower jaws (P<0.05). Dynamic navigation guidance effectively improves the reliability and stability of implantation in oral implant patients. However, there is relatively greater endpoint deviation and implant angle deviation in the posterior part of the upper jaw.

Clinicians’ Perspectives of Activity Monitoring Applications and Facilitators for Lower-Limb Prosthetic Service Provision

ABSTRACT Introduction Clinicians involved in lower-limb prosthetic provision and care do not have an objective method for measuring patients’ real-world prosthesis usage. This results in a poor understanding of the full picture of the patient’s everyday life impacting the reporting of outcome following prosthetic prescriptions. There is need to understand what information from wearable prosthetic monitors would be relevant to clinicians and how that information should be displayed to enable health care professionals to make informed decisions. Moreover, clinicians’ perspectives regarding potential use and application of information from monitoring technologies are important in the development and implementation of this technology to improve evidence-based systems for informed clinical decisions for lower-limb prosthesis users. This study aims to explore clinicians’ experiences and perceptions of information obtained from free-living wearable prosthetic monitors used in clinical practice. Methods Seven clinicians from private practice and academia were recruited to participate in the study. A semistructured interview was held for each participant, which lasted about 30–45 minutes. Data and transcripts were analyzed using inductive thematic analysis method. Results Themes of objectiveness and motivation were identified as specific applications of monitoring technologies. Other themes such as awareness and integration were identified as the facilitators of monitoring technologies for prosthetic care in clinical settings. Clinicians identified frameworks in which these monitoring technologies could be incorporated into the clinic, how they can receive feedback as a multidisciplinary team, and the inclusion within patients’ medical records. Conclusions This information from prosthetic monitoring technologies could potentially change clinical practice in several ways: by improving the clinical assessment process, improving the prescription process, and improving the rehabilitation process. To improve the clinical assessment process, prosthetic monitoring information can provide real-world data of prosthesis user’s utilization, which could be used to quantify patients’ self-reports. To improve the prescription process, prosthetic monitoring information can enable clinicians to see if a patient was actively involved in any form of exercise and support them to position their prescription of components within an activity category for their patients. To improve the rehabilitation process, prosthetic monitoring information can be used to plan goals for patients, to measure the progress of patients, and to check for patients’ compliance. Clinical Relevance Statement The impact of these potential changes in clinical practice can ultimately lead to improved clinical decisions in prosthetic care and creation of an evidence-based system.

Application of deep learning-based speech signal processing technology in electronic communication

In recent years, the artificial intelligence boom triggered by deep learning is influencing and changing peoples lifestyles. People are no longer satisfied with human-computer interaction through simple text commands; instead, they look forward to more convenient and faster communication methods like voice interaction. Against the backdrop of innovative development, the application of speech signal processing systems is becoming increasingly widespread. Therefore, it is necessary to study the application of deep learning-based speech signal processing technology in electronic communication. This can provide more valuable references and assistance for future development, promoting the better development of deep learning-based speech signal processing technology in electronic communication. In this paper, we first review the application of deep learning in speech signal enhancement, speech recognition, and speech synthesis from a theoretical analysis perspective. Then, we discuss the application of deep learning-based speech signal processing in electronic communication, including the application of models such as Transformer, LAS (Listen, Attend and Spell), and GFT-conformer in speech signal processing. We also discuss some application scenarios of deep learning-based speech signal processing in electronic communication. Finally, we identify the need for deeper application of deep learning technology in speech signal processing and electronic communication, with continuous optimization and adjustment.

Research on the application and prospect of RFID technology

As Radio Frequency Identification (RFID) has the advantages of fast scanning speed, small size, large data storage, high security, and other technologies, it is widely used in various fields to upgrade working efficiency. Meanwhile, as one of the research topics of the Internet of Things (IoT), Radio Frequency Identification also provides the possibility for the development and application scope of IoT. Based on the application principle of Radio Frequency Identification technology, this paper discusses the application of Radio Frequency Identification in the two fields of intelligent libraries and intelligent logistics through literature review and theoretical analysis. Through the analysis of the current situation of RFID application in the library management and logistics industry, this paper discusses the advantages and shortcomings of radio frequency identification technology, and according to the existing problems the future direction of development of the corresponding improvement proposals to promote the further improvement of the application of radio frequency identification technology.

Exploring graph learning techniques for enhancing cross-domain applications in computer vision and natural language processing

With the rapid development of artificial intelligence, graph learning technology has become a research hotspot because of its unique data processing ability. This paper discusses the application of graph learning technology in computer vision (CV) and natural language processing (NLP), especially, it is applied to image segmentation and recognition, visual relationship detection, dynamic scene understanding, semantic role labeling and knowledge map enhancement. The graph neural network (GNN) can effectively learn the deep features of image or text data by defining pixels or hyperpixels as nodes and constructing the edges between nodes according to the spatial proximity or similarity between pixels. This paper also discusses the application of graph learning technology in improving model interpretability, including feature relation visualization, error analysis and diagnosis, and model decision path interpretation. By comparing the application effects of different graph learning models and algorithms, this paper aims to provide reference and inspiration for the follow-up research and application.

Improved AdaBoost for virtual reality experience prediction based on Long Short-Term Memory network

A classification prediction algorithm based on Long Short-Term Memory Network (LSTM) improved AdaBoost is used to predict virtual reality (VR) user experience. The dataset is randomly divided into training and test sets in the ratio of 7:3.During the training process, the model's loss value decreases from 0.65 to 0.31, which shows that the model gradually reduces the discrepancy between the prediction results and the actual labels, and improves the accuracy and generalisation ability. The final loss value of 0.31 indicates that the model fits the training data well, and is able to make predictions and classifications more accurately. The confusion matrix for the training set shows a total of 177 correct predictions and 52 incorrect predictions, with an accuracy of 77%, precision of 88%, recall of 77% and f1 score of 82%. The confusion matrix for the test set shows a total of 167 correct and 53 incorrect predictions with 75% accuracy, 87% precision, 57% recall and 69% f1 score. In summary, the classification prediction algorithm based on LSTM with improved AdaBoost shows good prediction ability for virtual reality user experience. This study is of great significance to enhance the application of virtual reality technology in user experience. By combining LSTM and AdaBoost algorithms, significant progress has been made in user experience prediction, which not only improves the accuracy and generalisation ability of the model, but also provides useful insights for related research in the field of virtual reality. This approach can help developers better understand user requirements, optimise virtual reality product design, and enhance user satisfaction, promoting the wide application of virtual reality technology in various fields.

THE CRIMINAL JUSTICE SYSTEM AND TECHNOLOGY IN THE DIGITAL AGE

This research utilizes a literature review methodology to examine the legal ramifications of technological advancements on law enforcement practices in the digital era. The burgeoning digital landscape has witnessed a surge in technological developments, such as big data analysis, artificial intelligence, and digital forensics, which have fundamentally reshaped law enforcement operations. This study delves into the positive and negative legal implications of these technologies within the justice system. The findings of this research, gleaned from the reviewed literature, suggest that the application of technology in law enforcement can enhance the efficiency of investigations, facilitate evidence analysis, and improve detainee monitoring. However, there are concomitant legal concerns surrounding privacy and data security that necessitate careful consideration. Furthermore, unequal access to technology and data poses a potential threat to the very concept of equal justice under the law. This research is intended to equip stakeholders in law enforcement and public policy with a comprehensive understanding of the legal ramifications associated with technological innovation. By elucidating these issues, this study also lays the groundwork for future researchers to delve deeper into these complexities through more rigorous empirical research endeavors.

Examining learners’ cognitive processes and learning emotions in digital game-based vocabulary learning: evidence from eye-tracking technology and facial emotion recognition

Purpose This research explored the intersection of cognitive processes, emotions and their impacts on digital game-based vocabulary learning (DGVL) among university students. Recognizing the scant research in this area, especially with integrating innovative technologies, this study aims to understand the influence of these elements using advanced monitoring tools. Design/methodology/approach This inquiry was carried out as an observational study involving 44 university students segmented into three English language proficiency levels: high, intermediate and low based on their English course scores. The methodological tools included a portable eye tracker to observe visual behaviors and deep learning technology to identify and analyze the participants’ emotional responses and engagement with the DGVL during the learning process. Findings The results showed that distinct fixation sequences and variations in visual attention during DGVL were correlated with different levels of competency, suggesting a direct correlation between visual engagement and language competence. In addition, emotional transitions, predominantly from engagement (“flow”) to challenge (“frustration”), were common among participants, reflecting the emotional dynamics of learning. Furthermore, all participants consistently focused on the English vocabulary definitions, indicative of their targeted approach to understanding and test preparation. These findings highlighted the intricate dynamics between emotions and cognitive processes in learning environments. Originality/value Contribution of this study shows the interplay of cognitive engagement and emotional experiences in the context of DGVL. It underscored the complex nature of these factors and their collective influence on learners’ visual and emotional engagement, offering valuable implications for educational strategies and technological applications in language learning.

Research on the technical principles and applications of massage robots

Massage robots, as innovative therapeutic devices, have garnered significant attention and application due to technological advancements and society's increasing focus on wellness. However, amidst this progress, gaps persist in fully understanding their capabilities and optimizing their functionalities, highlighting the need for further research in this domain. This paper focuses on exploring the intricate technical principles, mechanical structure, sensing technology, and intelligent control systems of massage robots, with the aim of providing both theoretical foundations and practical insights for researchers and practitioners in this field. Specific inquiries encompass the elucidation of working principles, emulation of human massage techniques, mechanical design considerations, applications of sensor technology, and implementations of intelligent control systems within massage robots. Employing a blend of literature review, comparative analysis, and experimental validation, this paper aims to not only deepen the understanding of massage robot functionality but also contribute to the advancement and refinement of this technology, fostering innovation and growth within the industry.

Quorum Quenching in Membrane Bioreactors for Fouling Retardation: Complexity Provides Opportunities.

The occurrence of biofouling restricts the widespread application of membrane bioreactors (MBRs) in wastewater treatment. Regulation of quorum sensing (QS) is a promising approach to control biofouling in MBRs, yet the underlying mechanisms are complex and remain to be illustrated. A fundamental understanding of the relationship between QS and membrane biofouling in MBRs is lacking, which hampers the development and application of quorum quenching (QQ) techniques in MBRs (QQMBRs). While many QQ microorganisms have been isolated thus far, critical criteria for selecting desirable QQ microorganisms are still missing. Furthermore, there are inconsistent results regarding the QQ lifecycle and the effects of QQ on the physicochemical characteristics and microbial communities of the mixed liquor and biofouling assemblages in QQMBRs, which might result in unreliable and inefficient QQ applications. This review aims to comprehensively summarize timely QQ research and highlight the important yet often ignored perspectives of QQ for biofouling control in MBRs. We consider what this "information" can and cannot tell us and explore its values in addressing specific and important questions in QQMBRs. Herein, we first examine current analytical methods of QS signals and discuss the critical roles of QS in fouling-forming microorganisms in MBRs, which are the cornerstones for the development of QQ technologies. To achieve targeting QQ strategies in MBRs, we propose the substrate specificity and degradation capability of isolated QQ microorganisms and the surface area and pore structures of QQ media as the critical criteria to select desirable functional microbes and media, respectively. To validate the biofouling retardation efficiency, we further specify the QQ effects on the physicochemical properties, microbial community composition, and succession of mixed liquor and biofouling assemblages in MBRs. Finally, we provide scale-up considerations of QQMBRs in terms of the debated QQ lifecycle, practical synergistic strategies, and the potential cost savings of MBRs. This review presents the limitations of classic QS/QQ hypotheses in MBRs, advances the understanding of the role of QS/QQ in biofouling development/retardation in MBRs, and builds a bridge between the fundamental understandings and practical applications of QQ technology.

Exploring the horizon of AI development: Navigating constraints of chips and power in the technological landscape

With the proliferation of AI technology, machine learning has emerged as a cornerstone of AI systems, facilitating pattern recognition and decision-making through robust data analysis. This encompasses various learning paradigms such as supervised, unsupervised, and reinforcement learning, all of which are indispensable for the advancement of artificial intelligence. Nevertheless, the development of AI necessitates substantial computational resources, with specialized chips serving as the linchpin, particularly in demanding tasks such as deep learning. Dedicated chip development, exemplified by GPUs and TPUs, plays a pivotal role in enhancing the performance of AI systems, notwithstanding challenges related to costs and market monopolies. Moreover, AI systems require significant power support, especially during the training of large-scale models. To address these challenges, this paper reviews the existing literature on modeling techniques aimed at enhancing the efficiency of machine learning and reducing energy consumption. This review encompasses optimal algorithm design, hardware optimization, and spatial modeling. Through the implementation of these approaches, the challenges posed by resource constraints in machine learning scenarios can be effectively mitigated, thereby fostering the continued development and application of AI technology.

The application and development of toon shading technology in mobile games

With the continuous development of the gaming market, the toon shading technology in mobile games is gradually moving towards integration with Physically Based Rendering (PBR) technologies, giving rise to a trend of hybrid toon shading. This paper aims to discuss the development reasons of hybrid toon shading technology in mobile games, and compare the technical requirements and practical applications of toon shading and hybrid toon shading in the mobile gaming context. Firstly, the paper introduces the characteristics of toon shading and its relationship with non-photorealistic rendering (NPR) and PBR. Subsequently, examples of toon shading applications in mobile games are provided, along with a discussion on the two advantages of toon shading technology in the mobile gaming domain. The fundamental process of toon shading is then outlined, followed by an explanation of the limitations of traditional toon shading in contemporary mobile games. Then, introduce the development reasons of hybrid toon shading technology and the general process of hybrid toon shading, compare the technical differences between hybrid toon shading and toon shading. And then introduce the advantages and disadvantages and current application status of hybrid toon shading. In conclusion, the paper emphasizes that hybrid toon shading is a feasible approach warranting active development, and also discusses the future of the two-dimensional game market.

A novel design of multiplexer based on the cellular interaction in quantum dot technology with energy dissipation analysis

This paper addresses the challenge of efficiently designing multiplexer structures in quantum-dot technology circuits. Specifically, it introduces a novel approach to constructing a 2-to-1 multiplexer through a unique gate design that relies on the interaction between cells. The key focus of this design is to minimize the utilization of cells, thereby conserving space and facilitating the creation of more complex combinational and sequential circuits. The methodology involves developing a gate structure that requires the least number of cells compared to existing designs. This reduction in cell usage not only optimizes space utilization but also enhances the scalability of the multiplexer for integration into larger circuits. The proposed gate structure demonstrates a significant improvement, achieving a 15% reduction in the number of cells compared to the previously best-known design. To further evaluate the effectiveness of this novel gate design, the study extends its analysis to include the implementation of various complex circuits such as a 4-to-1 multiplexer, D latch, and T latch. These structures are synthesized using the newly proposed gate design as a fundamental building block. To validate the functionality and performance of these circuits, simulations are conducted using the QCADesigner simulation tool. This comprehensive simulation enables a thorough assessment of the proposed structures across different circuit configurations, providing insights into their operational efficiency and suitability for practical applications in quantum-dot technology.

Can We Still Find an Ideal Memristor?

In 1971, Chua defined an ideal memristor that links magnetic flux φ and electric charge q. In a magnetic lump with a current-carrying conductor, we found that the direct interaction between physical magnetic flux φ and physical electric charge q is memristive by nature in terms of a time-invariant φ-q curve being nonlinear, continuously differentiable and strictly monotonically increasing. Although we succeeded in demonstrating that the “ideal/real/perfect/… memristor” needs magnetism, the structure still suffers from two serious limitations: 1. a parasitic “inductor” effect and 2. bistability and dynamic sweep of a continuous resistance range. Then, we discussed how to overcome these two limitations to make a fully functioning ideal memristor with multiple or an infinite number of stable states and no parasitic inductance. We then gave a number of innovations to the current memristor structure, such as an “open” structure, nanoscale size, magnetic materials with cubic anisotropy (or even isotropy) and sequential switching of the magnetic domains. Contrary to the conjecture that “an ideal memristor may not exist or may be a purely mathematical concept”, we remain optimistic that an ideal memristor will be discovered in nature or will be made in the laboratory. Our finding of the memristive flux–charge interaction may advance the development and application of the memristor technology.

Analysis of problems and countermeasures in computer network engineering security

This paper analyzes the existing security issues in computer network engineering and proposes a series of countermeasures. To address the problems of hacker attacks and information leakage, it is recommended to strengthen network monitoring and the application of encryption technology. For the issues of malware and virus dissemination, it is advised to regularly update antivirus software and enhance employee security awareness training. Regarding network data leakage and privacy breaches, the use of secure transmission protocols and multi-factor authentication technology is advocated. For network security management and maintenance issues, establishing a sound security management system and emergency response plans is recommended to ensure the stable operation of network systems. Through the implementation of the above measures, the security level of computer network engineering can be effectively improved, ensuring the normal operation of network systems and the security of information.

Guidelines for clinical application of tissue matching techniques for kidney transplantation in China

Tissue matching is one of the key factors affecting the success of kidney transplantation and long-term graft survival. At present, the development of tissue matching technology and its application in the laboratory of transplant centers in China is different. In order to promote the standardization of clinical diagnosis and treatment of kidney transplantation tissue matching, the Chinese Transplantation Branch of the Chinese Medical Association, the Kidney Transplantation Branch of the China Medical Care International Exchange Promotion Association, and the Transplantation Technology Branch of the Chinese Medical Biotechnology Association jointly initiated the guidelines for clinical application of tissue matching techniques for kidney transplantation in China. This guide grades the quality of evidence and the strength of recommendation for each clinical issue using the 2009 Oxford Centre for Evidence-Based Medicine Grading and Strength of Recommendation criteria. Aiming at 15 clinical problems related to the laboratory detection technology and clinical application of kidney transplantation tissue matching, a total of 21 recommendations were put forward in line with China's clinical diagnosis and treatment practice, aiming at promoting tissue matching before kidney transplantation, improving the long-term survival time of recipients and transplanted kidneys, and giving play to the application value of precision medicine in the field of kidney transplantation.

Sustainable Production of Dialkoxymethane Ethers and Related Acetals Using CO2, H2 and Alcohols or Diols

Dialkoxymethane (DAM) ethers, so‐called oxymethylene ethers (OMEs), are compounds constituted by a CH2‐unit bound to two identical alkoxy substituents. These compounds have showcased interesting technological applications as fuel additives or fuels, as a consequence of their interesting properties such as their low vapor pressure, high viscosity, cetane number and oxygen content. Moreover, they are also employed as environmentally benign solvents and bulk compounds useful in fine‐chemical industry. In addition, DAM ethers are considered formaldehyde surrogates. Industrially, dimethoxymethane (DMM) is still produced via two‐step protocol involving toxic formaldehyde that consists of a first step of methanol oxidation (Formox process). Hence, the development of more sustainable protocols for their synthesis is highly desired. Among the different strategies reported for DAM synthesis, its production from CO2/H2 and alcohols constitutes a practical, green and attractive alternative route. In this concept, a careful discussion of the developed catalytic protocols for synthesizing DAM from CO2/H2 or formic acid/H2 and alcohols/diols will be summarized. A special emphasis will be performed on the reaction mechanisms involved and the catalyst nature design and characterization.