Application Of Technology Research Articles

Research on Piano Program Programming and Creative Optimization Methods Based on Big Data Technology

This study focuses on the programming and creative optimization methods of piano tracks based on big data technology. Through in-depth analysis of the potential application of big data technology in the field of music creation, we propose an innovative framework for piano repertoire programming and creative optimization. This framework fully utilizes the data processing and analysis capabilities of big data technology to mine massive piano repertoire data, extract music elements, style features, and creative patterns. In terms of piano repertoire programming, we use big data technology to quantitatively analyze music elements and construct a data-driven programming model. This model can automatically generate piano repertoire frameworks that match specific styles, emotions, and rhythms based on user creative needs, providing inspiration and direction for creators. In terms of creative optimization, we utilize machine learning algorithms based on big data technology to intelligently evaluate and improve piano tracks. By comparing and analyzing the advantages and disadvantages of different tracks in terms of melody, harmony, structure, etc., we have discovered and extracted the common characteristics of excellent works, providing guidance for creators to optimize their work. This article will focus on the programming and control capabilities of music programming in the design and composition process of big data technology, as well as optimization methods for music creation. Provide precise services for music teaching and research, promote the prosperity and development of music, and accelerate the integration and innovative development of information technology and music education and teaching. In terms of piano repertoire programming, we utilized big data technology to quantitatively analyze music elements and constructed a data-driven programming model. This model can provide creators with rich inspiration and clear direction based on their creative intentions and specific styles. In addition, our study also considered the recognition rates of training models for different instrument types. Through training and optimizing models, we have ensured that piano program programming and creative optimization processes can achieve high recognition rates and accuracy on different instrument types, thereby further expanding the application scope of big data technology in the field of music creation.

A comparative evaluation of the leadership development needs of basic school leaders in the 4.0 era

School leadership development (SLP) in the context of the Fourth Industrial Revolution (4.0) is a crucial aspect of preparing basic school leaders to navigate the challenges and opportunities presented by the rapid technological advancements and changes in the modern world. The 4.0 is characterized by the fusion of digital, physical, and biological technologies, which profoundly impact various aspects of society, including education. Thus, this study examined basic school leaders’ development needs in 4.0 in Nigeria and South Africa. A comparative research approach of survey design was adopted with the aid of a questionnaire. Descriptive statistics (mean score) and an independent t-test were used to analyze the collected data. Results indicated that basic school leaders in South Africa fared better than their counterparts from Nigeria regarding their 4.0 knowledge. However, no significant difference was found in 4.0 leadership skills. Leaders from both nations indicated the need for development programs in technological applications to enhance competency to perform their administrative tasks. In theory, the study addresses African schools’ 4.0 leadership needs. To lead education into the future, each nation must also understand the specific 4.0 leadership skills its school leaders will need. In light of the study’s findings, policymakers should consider what gaps need to be filled to equip their basic school leaders with Education 4.0 skills.

Facial recognition for disease diagnosis using a deep learning convolutional neural network: a systematic review and meta-analysis.

With the rapid advancement of deep learning network technology, the application of facial recognition technology in the medical field has received increasing attention. This study aims to systematically review the literature of the past decade on facial recognition technology based on deep learning networks in the diagnosis of rare dysmorphic diseases and facial paralysis, among other conditions, to determine the effectiveness and applicability of this technology in disease identification. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for literature search and retrieved relevant literature from multiple databases, including PubMed, on 31 December 2023. The search keywords included deep learning convolutional neural networks, facial recognition, and disease recognition. A total of 208 articles on facial recognition technology based on deep learning networks in disease diagnosis over the past 10years were screened, and 22 articles were selected for analysis. The meta-analysis was conducted using Stata 14.0 software. The study collected 22 articles with a total sample size of 57 539 cases, of which 43 301 were samples with various diseases. The meta-analysis results indicated that the accuracy of deep learning in facial recognition for disease diagnosis was 91.0% [95% CI (87.0%, 95.0%)]. The study results suggested that facial recognition technology based on deep learning networks has high accuracy in disease diagnosis, providing a reference for further development and application of this technology.

A study on the Application of Virtual Reality Technology in Children's Rehabilitation Therapy

This article discusses the value of virtual reality (VR) technology in paediatric rehabilitation therapy, which can create immersive, interactive and imaginative virtual environments and provide children with safe and interesting therapeutic modalities to effectively improve their cognitive, motor, speech and emotional problems. The article analyses the application of VR technology in different scenarios, such as attention training, motor training, verbal expression training and emotion recognition training, and points out that the advantages of VR technology are to increase the interest of treatment, enhance the patient's participation, and achieve personalised treatment. However, VR technology also has challenges such as technical limitations, evaluation difficulties and high equipment costs. With the continuous development and improvement of the technology, VR technology is expected to overcome these challenges and play a greater role in the field of children's rehabilitation therapy, bringing hope for the rehabilitation of more children.

From Web Novel to Film and Television—Analyzing the New Trend of Net Drama in the Era of “Internet +”

As a burgeoning literary form, online novels have rapidly ascended in recent years, captivating a large cohort of youthful readers. Propelled by the "Internet+" era, online novels have gradually expanded their influence, transitioning into popular culture more deeply through adaptation into film and television works. From its inception as online serialization to its contemporary reincarnation in visual media, online novels have not only borne witness to the digital transformation of literary creation, but have also emerged as a fresh wellspring of content for the film and television industry. Throughout the process of visualizing online novels, it is precisely this intermedial collaboration that enables originally virtual narratives to be visually presented, garnering a wider dissemination. Against the backdrop of the "Internet+" era, web series, as a critical form of visualizing online novels, are unveiling their distinctive allure and novel trends. By scrutinizing the diversified thematic creation, the application of new technologies, and the feedback from audience demands, it becomes evident that web series have evolved into a pivotal component of the cultural industry, embodying the new aesthetic and value orientations of the contemporary era.

Optical and scintillation properties of 2D-(BA)2PbBr4 needle-shaped crystals

In this study, we examine the impact of optical and scintillation properties on needle-shaped bis(butylammonium) lead bromide [(BA)2PbBr4] crystals (average length of 12.7 mm and average width of 1.3 mm), considering different growth methods and dimensions. Needle-shaped crystals with a length-to-width aspect ratio greater than 10 are essential for cutting-edge radiation imaging technology. Through X-ray diffraction characterization, we observe lattice expansion in the needle-shaped crystals compared to previous single crystals. This expansion leads to a stronger overlap between absorption and photoluminescence (PL) spectra, resulting in a smaller Stokes shift of 26 meV. Time-resolved PL analysis reveals a fast decay component of 0.8 ns, which is 68 % faster than the previously reported best value of 2.0 ns, with an average decay time of 3.6 ns, 48 % faster than the best reported value of 7.0 ns. Although our measured sample exhibits a comparatively low light yield (LY) of 7.8 ph/keV due to strong self-absorption, the best energy resolution is observed with a value of 11.5 %, similar to the best reported value of 10.9 %. Despite this, the LY is notably superior to that of previously reported benzylammonium lead bromide [(BZA)2PbBr4] crystals (3.7 ph/keV). Additionally, scintillation decay shows a fast component of 9.4 ns and an average decay time of 119 ns. These characteristics make two-dimensional-(BA)2PbBr4 needle-shaped crystals promising candidates for cost-effective and scalable applications in radiation imaging technology.

Efficacy and Economic Benefits of Nano TiO2 Photocatalytic Advanced Oxidation Technology in Solid Waste Treatment

In order to assess the application value of nano TiO2 photocatalytic advanced oxidation technology in the field of solid waste treatment and improve the efficiency of waste disposal, this study investigates the preparation of TiO2/ZnIn2S4 composite photocatalysts using an in-situ growth method. Subsequently, this photocatalyst is applied to urban solid waste treatment schemes to degrade solid waste and organic pollutants in wastewater. The results indicate that, at a pH value of 7.5, the composite material exhibits the optimal photocatalytic degradation performance, achieving a degradation rate of 90%. Following the urban solid waste treatment scheme, the greenhouse gas emissions per ton of municipal solid waste range from 50 to 100 kg, resulting in a 90% reduction in greenhouse gas emissions. The calculated payback period for this waste treatment scheme is 5 years. The findings suggest that this treatment approach effectively reduces greenhouse gas emissions, enhances resource recovery rates, and demonstrates favorable economic benefits for application in small to medium-sized urban areas. This research provides valuable insights for the application of nano TiO2 photocatalytic technology in the field of solid waste treatment.

Application of Multimedia Technology in PE Teaching in Colleges and Universities

With the rapid development of information technology, multimedia technology has gradually penetrated various fields, including physical education in colleges and universities. Traditional physical education teaching methods lack interaction, and it is difficult to ensure the durability and depth of the effect of teaching. In college physical education teaching, multimedia technology can be displayed through various forms of media, and it helps to stimulate students' interest and enthusiasm in learning. Therefore, we need to find a new teaching method to improve current physical education in colleges and universities. This paper expounds the applications of multimedia technology in college physical education from three dimensions: video teaching, audio teaching, and virtual reality technology. The effect of applying multimedia technology in physical education teaching is analyzed through case studies. This paper will provide a useful reference for the innovation and development of physical education teaching in colleges and universities.

The Role of Food Science in Combating Obesity and Related Metabolic Disorders

Obesity and related metabolic disorders have emerged as major global health challenges, affecting millions of individuals worldwide. The prevalence of these conditions has reached epidemic proportions, leading to increased morbidity, mortality, and healthcare costs. Food science plays a crucial role in addressing this complex issue by developing innovative strategies and interventions to promote healthier eating habits and prevent the development of obesity and its associated comorbidities. This comprehensive review explores the multifaceted role of food science in combating obesity and related metabolic disorders, focusing on key areas such as food formulation, processing, preservation, and fortification. We discuss the development of functional foods and ingredients that target specific metabolic pathways, as well as the application of novel technologies to enhance the nutritional quality and sensory appeal of food products. Additionally, we highlight the importance of consumer education and behavior change strategies in promoting healthy food choices and portion control. The review also examines the potential of personalized nutrition approaches, which leverage advances in nutrigenomics and metabolomics to tailor dietary interventions based on individual genetic and metabolic profiles. Furthermore, we explore the role of food science in developing sustainable and equitable food systems that ensure access to nutritious foods for all populations. By synthesizing the latest research findings and identifying gaps in current knowledge, this review provides valuable insights and recommendations for future research and policy initiatives aimed at harnessing the power of food science to combat obesity and related metabolic disorders on a global scale.

Implications of Digitalization on the Persistence of Startups

Digitalization is a transformative phenomenon catalyzing innovative paradigms and operational methodologies within the realm of business, with a pronounced impact on nascent enterprises, notably startups. The aim of this paper is to ascertain the influence of digitalization and the effects of the different sociodemographic variables, both internal and environmental, as key driving elements in the probability of a company’s success and as indicators of business persistence and growth. Taking a representative sample of entrepreneurs from the Community of Madrid, Spain, a statistical structural equation model (SEM) is built. The results show the importance that the degree of adaptation of the startup to digitization acquires in persistence, the effort to generate inter-entrepreneurship within the company, the preparation of the promoting team, their work skills, good practices, and the influence of environmental variables. This study contributes to dispelling the great uncertainty that afflicts entrepreneurs and making them understand the importance of innovation, training, and continuous updating, expansion, and renewal of knowledge and especially the value of learning about applications of new technologies, the transformation of processes within a business model, and the correct adaptation to an environment that is constantly changing.

Identification, Characterization, and Antibacterial Evaluation of Five Endophytic Fungi from Psychotria poeppigiana Müll. Arg., an Amazon Plant

Endophytic fungi, residing within plants without causing disease, are known for their ability to produce bioactive metabolites with diverse properties such as antibacterial, antioxidant, and antifungal activities, while also influencing plant defense mechanisms. In this study, five novel endophytic fungi species were isolated from the leaves of Psychotria poeppigiana Müll. Arg., a plant from the Rubiaceae family, collected in the tropical Amazon region of Bolivia. The endophytic fungi were identified as a Neopestalotiopsis sp., three Penicillium sp., and an Aspergillus sp. through 18S ribosomal RNA sequencing and NCBI-BLAST analysis. Chemical profiling revealed that their extracts obtained by ethyl acetate contained terpenes, flavonoids, and phenolic compounds. In a bioautography study, the terpenes showed high antimicrobial activity against Escherichia coli. Notably, extracts from the three Penicillium species exhibited potent antibacterial activity, with minimum inhibitory concentration (MIC) values ranging from 62.5 to 2000 µg/mL against all three pathogens: Escherichia coli, Staphylococcus aureus, and Enterococcus faecalis (both Gram-positive and Gram-negative bacteria). These findings highlight the potential of these endophytic fungi, especially Penicillium species as valuable sources of secondary metabolites with significant antibacterial activities, suggesting promising applications in medicine, pharmaceuticals, agriculture, and environmental technologies.

Application of Digital Twin Technology in Synthetic Aperture Radar Ground Moving Target Intelligent Detection System

In recent years, the detection performance of SAR-GMTI (synthetic aperture radar-ground moving target indication) algorithm based on deep learning has always been limited by insufficient measured data due to the heavy operation complexity and high cost of real SAR systems. To solve this problem, this paper proposes an overall DT-based implementation framework for SAR ground moving target intelligent detection tasks. In particular, by virtue of a SAR imaging algorithm, a high-fidelity twin replica of SAR moving targets is established in digital space through parameter traversal based on the prior target characteristics of the obtained measured datasets. Then, the constructed SAR twin datasets is fed into the neural network model to train an intelligent detector by fully learning features of the moving targets and preset the SAR scene in the twin space, which can realize the robust detection of ground moving targets in related practical scenarios with no need for multiple and complex field experiments. Moreover, the effectiveness of the proposed framework is verified on the MiniSAR measured system, and a comparison with traditional CFAR detection method is given simultaneously.

Artificial Intelligence Applications in English News Translation: Strategies and Research

The rapid advancement of Artificial Intelligence (AI) is significantly transforming the field of translation, especially in English news translation. This paper aims to explore the application strategies of AI in English news translation and its current research status. First, it introduces the basic concepts of AI and machine translation, reviews the development history of machine translation technology, and analyzes the unique requirements of news translation. Then, it discusses key application strategies of AI in news translation, including data collection and processing, model selection and training, translation quality assessment, and real-time translation technology. Additionally, the paper analyzes major challenges in AI applications, such as translation accuracy, cultural and contextual understanding, and data privacy and security, and proposes corresponding solutions. Finally, it explores future development trends in AI translation technology, including technological advancements, application expansion, and ethical and societal impacts. This paper aims to provide comprehensive references and in-depth insights for researchers in related fields.

Advancements and challenges in plasmon-exciton quantum emitters based on colloidal quantum dots and perovskite nanocrystals

Abstract The Nobel Prizes in Physics (2022) and Chemistry (2023) heralded the recognition of quantum information science and the synthesis of quantum dots, respectively. This acknowledgment has propelled colloidal quantum dots and perovskite nanocrystals to the forefront of quantum technologies. Their distinct emission properties, facilitating the efficient generation of both single photons and photon pairs, render them particularly captivating. Moreover, their adaptability to diverse structures, ranging from traditional electronics to nanopatterned frameworks, underscores their pivotal role in shaping quantum technologies. Despite notable strides in synthesis, certain properties require refinement for enhanced applicability in quantum information, encompassing emission brightness, stability, single-photon indistinguishability, and entanglement fidelity of photon pairs. Here we offer an overview of recent achievements in plasmon-exciton quantum emitters based on luminescent semiconductor nanocrystals. Emphasizing the utilization of the light-matter coupling phenomenon, we explore how this interaction enables the manipulation of quantum properties without altering the chemical structure of the emitters. This approach addresses critical aspects for quantum information applications, offering precise control over emission rate, intensity, and energy. The development of these hybrid systems represents a significant stride forward, demonstrating their potential to overcome existing challenges and advance the integration of quantum emitters into cutting-edge quantum technology applications.

Sustainable Marketing: Integrating Green Marketing Practices into Marketing Strategy

This study aims to explore the impact of Green Marketing practices on the financial performance of companies in Indonesia, focusing on variables such as Environmental Practices, Consumer Knowledge, and Company Commitment. The research method used involves quantitative analysis by collecting data through questionnaires distributed to managers and staff in the banking sector. The data obtained were then analyzed using descriptive and inferential statistical techniques, including validity, reliability, and direct effect hypothesis testing. The results of the study indicate that Company Commitment has the most significant influence on Financial Impact, followed by Environmental Practices. Although Consumer Knowledge does not show a significant direct effect, the application of digital technology and funding of green projects generally contribute positively to financial results. This study also identified that the integration of environmentally friendly practices can improve operational efficiency and brand reputation, as well as meet the demands of consumers who are increasingly concerned about environmental issues. These findings provide strategic guidance for companies to optimize sustainable marketing practices and achieve long-term benefits.

Spatial Information Entropy-Assisted Integrated Sensing and Communication for Integrated Satellite-Terrestrial Networks

To better meet communication needs, 6G proposes Integrated Satellite-Terrestrial Networks. Integrated Sensing and Communication (ISAC) is one of the key technologies of Integrated Satellite-Terrestrial Networks, which can reduce the energy consumption of the system, improve communication efficiency, and increase the utilization rate of spectrum resources. In the existing technology, the Modulated Wideband Converter (MWC) system can provide support for the miniaturization and intelligence of wireless device sensing and communication systems. Therefore, the MWC system can be used as a preliminary application of ISAC technology. However, the reconstruction effect of the conventional MWC system under the influence of noise is not stable. Therefore, we propose a signal processing optimization scheme for the MWC system based on spatial information entropy. First, the subsequent reconstruction algorithm is considered to require the dynamic and flexible processing of the sampled signals to reduce the influence of noise. Second, for the shortcomings of the original Orthogonal Matching Pursuit (OMP) algorithm, the concept of the genetic algorithm is used to optimize the algorithm by constructing the feature factor through spatial information gain and spatial information features. According to the simulation results, compared with the traditional MWC system, the scheme proposed in this paper is improved in all indicators.

Recent strategies to improve the electroactivity of metal–organic frameworks for advanced electrocatalysis

AbstractMetal–organic frameworks (MOFs) have emerged as promising materials in the realm of electrocatalysis due to their high surface area, tunable porosity, and versatile chemical functionality. However, their practical application has been hampered by inherent limitations such as low electrical conductivity and a limited number of active metal sites. Researchers addressed these challenges through various strategies, including enhancing conductivity by incorporating conductive materials and metal nanoparticles, modifying the structure and composition of MOFs by replacing metal nodes and functionalizing linkers, and preparing MOFs catalysts through thermal processes such as decarburization and conversion into metal oxides, phosphides (MPs), and sulfides (MSs). This review provided a comprehensive summary of the strategies that were employed to enhance the electroactivity of MOFs for improved electrocatalytic performance in recent years. It also explored future directions and potential innovations in the design and synthesis of MOF‐based electrocatalysts, offering valuable insights for advancing their application in sustainable energy technologies.

Application of communication technologies in ensuring patient safety in home care: Pros and cons

The Article Abstract is not available.

The Tunable Parameters of Graphene-Based Biosensors

Graphene-based surface plasmon resonance (SPR) biosensors have emerged as a promising technology for the highly sensitive and accurate detection of biomolecules. This study presents a comprehensive theoretical analysis of graphene-based SPR biosensors, focusing on configurations with single and bimetallic metallic layers. In this study, we investigated the impact of various metallic substrates, including gold and silver, and the number of graphene layers on key performance metrics: sensitivity of detection, detection accuracy, and quality factor. Our findings reveal that configurations with graphene first supported on gold exhibit superior performance, with sensitivity of detection enhancements up to 30% for ten graphene layers. In contrast, silver-supported configurations, while demonstrating high sensitivity, face challenges in maintaining detection accuracy. Additionally, reducing the thickness of metallic layers by 30% optimizes light coupling and enhances sensor performance. These insights highlight the significant potential of graphene-based SPR biosensors in achieving high sensitivity of detection and reliability, paving the way for their application in diverse biosensing technologies. Our findings pretend to motivate future research focusing on optimizing metallic layer thickness, improving the stability of silver-supported configurations, and experimentally validating the theoretical findings to further advance the development of high-performance SPR biosensors.

A real-time working memory evaluation system for macaques in microwave fields.

With the development and widespread application of electromagnetic technology, the health hazards of electromagnetic radiation have attracted much attention and concern. The effect of electromagnetic radiation on the nervous system, especially on learning, memory, and cognitive functions, is an important research topic in the field of electromagnetic biological effects. Most previous studies were conducted with rodents, which are relatively mature. As research has progressed, studies using non-human primates as experimental subjects have been carried out. Compared to rodents, non-human primates such as macaques not only have brain structures more similar to those of humans but also exhibit learning and memory processes that are similar. In this paper, we present a behavioral test system for the real-time evaluation of the working memory (WM) of macaques in a microwave environment. The system consists of two parts: hardware and software. The hardware consists of four modules: the operation terminal, the control terminal, the optical signal transmission, and detection module and the reward feedback module. The software program can implement the feeding learning task, the button-pressing learning task, and the delayed match-to-sample task. The device is useful for the real-time evaluation of the WM of macaques in microwave environments, showing good electromagnetic compatibility, a simple and reliable structure, and easy operation.