The Role of Wireless Network Technology in Analysis of Audience Satisfaction of Chinese Web Dramas in the Big Data Era

The continuous development of Wireless network technology has made web dramas popular on a large scale and made the cause of web dramas popular become the focus of research on mobile communication and modern communication. As an essential component of media effect research, analysis of the Audience Satisfaction plays a significant role in web drama research. However, the original click-through rate measurement method can not effectively solve the problem of analyzing the Audience Satisfaction of web dramas in the era of big data, and the accuracy of cause analysis is low. Therefore, this paper proposes an analysis model based on wireless network technology to analyze the popular Audience Satisfaction of web dramas from the perspective of the uses and gratifications theory. Firstly, wireless network technology is used to summarize the data transmission rate of web dramas, and judgment is made according to the popular methods, and reasons for data characteristics, and irrelevant popular data of web dramas is discarded. Then, the results are analyzed according to the data transmission rate and data form of the web drama and compared with the click-through rate measurement method to find out the reasons for the possibility of existence. After simulation test and analysis, Wireless network technology can improve the accuracy of judging the Audience Satisfaction of web dramas, with an accuracy rate of 90.3%, judge the reasons for different types of web drama content and forms, and calculate the cause analysis time, and find that this method can meet the cause analysis of web dramas Multifaceted needs

Optimize Urban Infrastructure Planning Based on Big Data and Enhance Xi'an's Urban Image

Infrastructure is an important support for urban operation, and city image is directly related to the quality of life of citizens and the construction of city brand. Urban infrastructure distribution, equipment types, network switching, and other issues have always restricted the development of communication in Xi'an city, and the addition of big data technology has further increased the communication pressure in Xi’an city and affected the image of Xi'an city. In this paper, we take Xi'an urban infrastructure as the research object, and combine the phyon method to obtain the big data information in the network and the data in the wireless self-organizing sensor. Then, the incomplete data was eliminated, and the data was mapped to the 0~1 interval in a logarithmic manner, and a standardized processing set was formed. set up wireless ad hoc sensor devices, collect infrastructure-related data, and summarize data through big data analysis. At the same time, based on social urban image data, public demand data, and urban infrastructure evaluation results, the content of urban planning is adjusted to better meet the expectations of the public and provide targeted planning solutions. Finally, according to the data fitting, the matching of wireless ad hoc sensor network and city image improvement is realized, and the reasonable planning of infrastructure is promoted. The results of urban image analysis show that wireless ad hoc sensor network and big data technology can simplify the steps of urban infrastructure planning, reduce urban planning costs, and enhance the functionality of the infrastructure,reduce the public complaint rate, and meet the requirements of Xi'an urban image improvement

Analysis of CMOS IC-based Hybrid Architecture for Edge Computing

With the rapid advancement of Internet of Things (IoT), mobile internet, and big data technologies, edge computing has emerged as a novel computing paradigm. In the hybrid architecture of edge computing,Complementary Metal-Oxide-Semiconductor (CMOS) integrated circuits play a pivotal role in empowering edge devices and servers with essential computing, storage, and communication capabilities. Despite their critical importance, CMOS integrated circuits in edge computing environments confront significant challenges in low-power electronics. These challenges include an increase in power density and a decrease in system stability and reliability. This paper delves into the key technologies of the hybrid architecture in edge computing and sheds light on the vital role of CMOS integrated circuits in edge devices. It introduces a novel approach for low-power electronics, which encompasses methods like optimization of double threshold voltage and refinement of algorithmic processes. These methods aim to tackle the power-efficiency issues while maintaining the performance of edge computing systems.Furthermore, the paper presents a detailed analysis of the proposed low-power techniques, focusing on how they can effectively reduce power consumption without compromising the functionality and efficiency of the edge computing systems. It concludes with a comprehensive discussion on the optimization results, highlighting the benefits and potential implications of implementing these low-power strategies in edge computing environments. This discussion not only underscores the importance of energy efficiency in edge computing but also opens new avenues for future research and development in this rapidly evolving field

Mobile Applications Integrated in Blended Learning : A Systematic Literature Review

This systematic literature review explores the use of mobile apps in blended learning, examines the characteristics of the research areas, research objects, and research methods of empirical studies on mobile apps in blended learning, and finally summarizes and organizes the results of the existing empirical studies and suggests implications for future research. This investigation involved a comprehensive search of academic publications within the Web of Science and Scopus databases, focusing on relevant topics. The analysis of the gathered literature indicates that mobile applications serve as efficient, beneficial, and suitable tools for facilitating blended learning. In such learning environments, these applications have the potential to enhance student satisfaction with the course, bolster engagement and motivation, and foster a sense of social connection among learners.However, mobile apps can only be used as an assistive tool for face-to-face learning and cannot replace paper-based assignments or the instructor's guiding role. Future research should attempt to introduce mobile learning apps in blended learning environments and extend the length of experiments to take advantage of mobile apps in order to improve the cognitive depth and breadth of blended learning and engagement

A Study of Innovative Technologies for Energy-Efficient Enterprise Management of Wireless Heterogeneous Networks in Collaborative Communications

Collaborative communication technology has become a popular research area in wireless communications due to its ability to resist varying degrees of channel fading through the collaborative transmission of network nodes. This thesis focuses on energy-efficient collaborative communication systems in increasingly complex environments in heterogeneous wireless networks, with the aim of optimizing energy efficiency and improving user data rates in small areas (e.g., within an enterprise). A brief introduction to the basic technologies of wireless energy-carrying collaborative communication systems is given, summarising relay forwarding strategies, three basic communication models, and energy and information co-transmission reception mechanisms before proposing an ED-OEH relaying protocol at the end of the section that integrates energy classification and opportunity energy harvesting. Immediately afterwards, the heterogeneity of network nodes in terms of computation and storage is pointed out, and a sensor network security protocol based on a hybrid encryption regime is designed. Finally, the problem of intra-enterprise resource allocation and energy efficiency optimization in heterogeneous wireless network scenarios based on deep augmented learning algorithms is investigated. Nature DQN is used as the core algorithm, and the input dimension and loss function in traditional neural networks are improved to reduce the complexity of the algorithm. Experimental results show that the Nature DQN algorithm converges faster than traditional algorithms such as Q-learning, and the energy efficiency ratio can reach up to 300%

The Research into Dark Mode: A Systematic Review Using Two-Stage Approach and S-O-R Framework

Dark mode in mobile applications has drawn widespread attention in business, and many users choose to use dark mode in their mobile applications for different purposes. However, it is still an emerging field in the academic circle, as there are not many systematic studies on the definition and concept of dark mode. Besides, scarce are the scholarly inferences regarding contemporary investigative undertakings and future advancements concerning the dark mode. The pros and cons of dark mode are still debatable. In order to bridge this gap, this paper undertook a comprehensive evaluation of the scholarship concerning dark mode across diverse disciplines, with the aim of building a fundamental research process for studying the concept of dark mode and user experience to establish a more robust groundwork for further exploration of the correlation between dark mode and user experience. Based on the Scopus database, this paper searched and collected articles on dark mode research within the last 30 years via the two-step approach. Thirty-five articles were selected based on the inclusion and exclusion criteria. This paper first analysed the themes, background, theoretical foundations and research methods on dark mode. Then it classified and integrated the variable factors of dark mode research based on the Stimulus-Organism-Response (S-O-R) framework. It ultimately proposed a research framework explaining the more profound concept of dark mode and the relationship between variable factors, user experience, and their behaviours. In addition, this paper also identified existing gaps in the current document studies and outlined potential opportunities for future research on dark mode

Impact of Social Games in Aggregating Relationships in Social Capital through Online Social Media Network

In recent days, social network sites connect people and help them maintain social ties through aggregating and accumulating social capital. This trait is important for organisation and individual success. The literature in the field indicates that there is a wide gap in automating the prediction of aggregation of social capital through online social games in social media networks. The analysis of the impact of social games in facilitating Social Capital (SC) is very vital. The existing mathematical and statistical modelling techniques fail to recognise the inherent and latent associations among the exploratory variables. Hence, this work proposes an ensemble machine learning model that learns the inherent features from the questionnaire collected from online gamers on three genres, namely media technology availability, multimedia communication channels and degree of social connectedness. The base learners explore the data domain in different ways to extract the features. The efficacy of the model's prediction is done by analysing the accuracy, F1 score, precision and recall. The results indicate that the model can effectively classify the instances, whether they positively or negatively contribute to the aggregation of SC. As a future extension of the research, the model can be made to learn more extensive attributes

Skin Cancer Prediction Model Based on Multi-Layer Perceptron Network

Melanoma is acknowledged by the World Health Organization as the most severe type of skin cancer, significantly contributing to skin cancer-related deaths worldwide. This type of cancer manifests through noticeable changes in moles, including their size, shape, colour, or texture. In this study, we introduce an innovative and robust method for detecting and classifying melanoma in various image types, including both basic and clinical dermatological images. Our approach employs the HSV (Hue, Saturation, and Value) colour model, along with mathematical morphology and Gaussian filtering techniques. These methods are used to pinpoint the area of interest in an image and compute four key descriptors crucial for melanoma analysis: symmetry, border irregularity, colour variation, and dimension. Despite the prior usage of these descriptors over an extended period, the manner in which they are calculated in this proposal is a key factor contributing to the improvement of the outcomes. Following this, a multilayer perceptron is utilized for the purpose of categorizing malignant and benign melanoma. The study included three datasets consisting of basic and dermatological photographs that are frequently referenced in academic literature. These datasets were applied to both train and assess the effectiveness of the proposed technique. Based on the results obtained from k-fold cross-validation, it is evident that the proposed model surpasses three existing state-of-the-art approaches. In particular, the model demonstrates remarkable precision, with an accuracy rate of 98.5% for basic images and 98.6% for clinical dermatological images. It exhibits a high level of sensitivity, measuring 96.68% for simple images and 98.05% for dermatological images. Additionally, its specificity stands at 98.15% when analyzing basic images and 98.01% for dermatological images, indicating its effectiveness in both types of image analysis. The findings have demonstrated that the utilization of this gadget as an assistive tool for melanoma diagnosis would enhance levels of reliability in comparison to traditional methods

Data Transmission Security and Legal Regulation in Clinical Application of Human Gene Editing from Perspective of Big Data

Gene editing, as an emerging biotechnology, has enormous potential for application but also brings various risks. Considering the current development status of gene editing technology, the criminal regulation of gene editing is based on the theory of risk criminal law. Ethical safety should be protected as a legal interest, and specific criminalization standards should be used to distinguish gene editing for therapeutic purposes, human embryo gene editing, and other types of gene editing behavior. In view of the many problems currently existing in gene editing legislation, at the legislative level, it is necessary to balance the expansion of legal provisions brought about by risk criminal law theory and the exoneration brought about by allowed risk theory, with administrative legal norms in place, and the criminal law should exercise restraint on emerging technologies; At the judicial level, by referring to the understanding of judicial interpretations of similar crimes, corrections can be made to the elements of criminal composition, serious circumstances, and deficiencies in unit crimes

The Role of Digital Narrative Patterns in the Metaverse Era on Human Machine Learning Interaction Systems: A Comparative Analysis of Pre and Post-Interactive Narratives

This study explores the metaverse's intriguing mysteries, including storytelling patterns and interactive narratives' effects on human-computer interactions. This study examines the influence of user-generated tales in the dynamic digital world and evaluates emotional computing models to propose a metaverse-specific framework. The study incorporates concepts from significant works in emotional computing, digital storytelling, and human-computer interaction to improve educational affective computing research. The literature study examines the emotional involvement of digital stories. The article reviews numerous authors' works on emotion-detecting and reacting AI systems. A foundation has been laid for researching metaverse emotions and narrative features. An analytical comparison approach integrates multiple methodologies. Qualitative methods allow for a complete literature review of metaverse user interactions with pre- and post-interactive narratives. Comparative analysis evaluates current emotional computing models to uncover flaws and inform new frameworks. The study's primary focus is comparing story frameworks with emotional computing models to find patterns, similarities, and contrasts. The research shows how metaverse storytelling frameworks have evolved and how user-generated stories affect human-robot relationships. Examining metaverse emotional computing models shows that there are restrictions. Addressing these issues requires a customised approach. Dynamic adaptability, context-aware computing, and a personalised user experience are proposed to improve the metaverse experience. These elements solve the issues and create a more engaging and effective atmosphere. Given the metaverse's growth, this study sheds light on the ever-changing dynamics of digital narratives and emotional computing. The research highlights the vital link between user-generated tales and machine learning systems, which might change digital storytelling. The emotive computing architecture is customised to the metaverse's dynamic and user-centric nature. Amidst the fast expansion of the digital world, it serves as a basis for discipline research and improvement