Information Processing Technology Research Articles

Revisiting longitudinal qualitative studies in social work: considerations for design and methodological insights

This paper undertakes an analysis and discussion of the methodological challenges and insights derived from three longitudinal qualitative studies, all conducted in Chile during the COVID-19 pandemic and subject to comprehensive theoretical-methodological reflection processes centred on their respective designs. This analysis makes a significant contribution to interdisciplinary discussions within social research, with a particular emphasis on longitudinal trajectories. First, we present a comparative analysis of three studies in social work, utilising Saldaña’s questions addressing changes and learning in longitudinal studies. The first study explores the labour trajectories of researchers, the second focuses on the educational trajectories of students, and the last examines therapeutic alliance trajectories between social workers and families within the child protection system. Following this, we delve into the methodological decisions made by the research group during the execution of these longitudinal studies. This encompasses an examination of participant involvement, temporal definitions of the adopted designs, and the most suitable methodological tools for analysing change processes over time. The outcomes of this comparative analysis reveal the distinctive characteristics of the three longitudinal studies, providing insights into how the time dimension is explored within them. We highlight key criteria essential for consideration in longitudinal qualitative research, particularly regarding participants and methodology. In conclusion, we advocate for an expanded reflection within the realm of longitudinal qualitative methodology, encompassing aspects such as design choices, approaches to data analysis, integration of technology in information processing, and strategies for maintaining participant engagement.

Designing production planning and control in smart manufacturing

To make manufacturing technology productive, manufacturers rely on a production planning and control (PPC) framework that plans ahead and monitors ongoing transformation processes. The design of an appropriate framework has far-reaching implications for the manufacturing organization as a whole. Yet, to date, there has been no unified guidance on key PPC design issues. This is strongly needed, as it has been argued that novel information processing technologies – as part of Industry 4.0 – result in PPC frameworks with decentral structures. This conflicts with traditional works arguing for hierarchical or central structures. Therefore, we review the PPC design literature to create a comprehensive overview and summarize design proposals. Based on our review, we come to the intermediate conclusion that PPC frameworks continue to have a hierarchical structure, although decision-making is shifted more to decentral levels compared to traditional hierarchies. Our analysis suggests that the effect of a decentralization shift has potentially strong and poorly understood implications, both from a decision-making and organizational perspective.

Environmental Studies Based on Lake Sediment Records in China: A Review

Lake sediment records are of great importance for understanding the evolution of watershed environments. Various studies have been carried out to determine the depositional ages of lake sediments and to examine their physical, chemical, and biological characteristics. The aim is to construct the historical vegetation, environment, and climate patterns in Chinese lake watersheds. In this review, we obtained relevant studies on lake sediment records by searching the key word ‘age-depth’ from the following databases: Web of Science and the China National Knowledge Infrastructure (CNKI, the largest Chinese academic database). We analyzed the literature based on its type (published in a Chinese/English journal or as a Master’s/PhD thesis), period of publication, journal (if published in a journal), key authors, study area, dating scale, and main aims. The results suggest that the lakes in the plateau regions are the most popular research topic, typically covering 100–200 years (short-term) and 500–30,000 years (long-term). The literature focuses on a wide range of topics, from past environmental evolution in watersheds to lake ecology, and it provides a solid foundation for a better understanding of the regional climate change and the preservation of lake environments and ecosystems. In the future, the resulting data obtained from environmental reconstructions with lake sediments will need to be integrated with emerging information processing technologies (e.g., artificial intelligence and meta-analysis) to disentangle the complex interplay between the Earth’s surface processes and global climate change; furthermore, strengthening interdisciplinary collaboration will deepen our comprehension of the man-land relationship and promote the sustainable management of lake ecosystems in the context of global climate change.

AI EOS using Wolframe Alpha

Artificial intelligence is crucial to day-to-day life. science of computers defines AI research as the study of intelligent agents. Today, most people, intentionally or not, have turned to some form of computerized information processing technology. Artificial Intelligence (AI) is already changing our lifestyle. A device that senses its surroundings and performs actions that maximize the likelihood of achieving a goal. The input to a database can be a choose to of users and articles, and the output is a ruthless recommendation. Input can be verbally or textually submitted by the user within the system. This paper presents a new approach to intelligent search. Overall, there are many people around the world who use assistants. This paper describes the provocation of applying virtual assistant technology. The paper also introduces the application of virtual assistants that can help open opportunities for humanity in various fields. Voice control is an important growing feature that will change people's lives. Voice assistants are available for laptops, desktops, and mobile phones. Assistant is now available on all electronic devices. A voice assistant is a software agent that can interpret human speech and respond in machine language

Quantum estimation of tripartite coupling in spin-magnon-mechanical hybrid systems

Tripartite interactions play a fundamental role in the quantum information processing and quantum technology. However, it is generally difficult to realize strong tripartite coupling. We investigate the estimation of a tripartite coupling strength in a hybrid setup composed of a single nitrogen-vacancy (NV) center and a micromagnet. A time-independent parametric drive can be utilized to increase the estimation precision of the tripartite coupling strength. By calculating the quantum Fisher information (QFI), we can obtain the optimal estimation precision by measuring the eigenstate of the tripartite system. At the critical position, the QFI is divergent due to that the preparation time of the eigenstate is divergent. When the system is subjected to a dissipation, the QFI near the critical point of the driven-dissipation phase transition is analytically obtained. The direct intensity measurement is the optimal measurement near the dissipation phase transition point. In addition, we quantify the robustness of an imperfect measurement operator by the measurement noise susceptibility based on the error propagation formula. We find that the direct intensity measurement is robust enough against small measurement disturbance from a coherent drive. But it can be disturbed by the nonlinear anti-harmonic measurement noise, especially near the critical point.

Review of Recent Patents on Smart Bearing

Background: Smart bearing is based on the traditional bearing, which is integrated by sensing devices and control devices, formating a unique bearing structure unit. It uses information processing, automatic control, and other technologies to achieve real-time online monitoring of bearing operating conditions, fault detection, and state control. Compared with ordinary bearings, smart bearings have the characteristics of self-awareness, self-decision, and self-regulation. By analyzing the composition of various types of smart bearings and their working principles, it is beneficial to prospect the future development direction of bearings. Objective: We analyze and organize the patents on smart bearings in recent years, summarize the problems encountered in the development of smart bearings, and propose the development direction of smart bearings in the future to provide a reference for future scientific research. Methods: This paper reviews the patents related to smart bearings and analyzes the composition and structure of smart bearings. The main smart bearings include rolling bearing, sliding bearing, magnetic bearing, and rubber bearing. The principle and structure of these bearings are analyzed. Their advantages and shortcomings are discussed. Results: Through the analysis of relevant patents and papers on smart bearings, the current development status and problems of smart bearings are summarized, the future design of smart bearings is proposed, and the research ideas and directions are proposed. Conclusion: The development of smart bearings is conducive to the timely detection of bearing conditions in the operation process. Except for detection, smart bearings have the functions of judgment, feedback, processing, etc. There are many factors that interfere with the accuracy of the smart bearing judgment and information transfer, so it is necessary to improve the structure and working principle of smart bearings through a variety of measures. In the future, more smart bearings will be invented.

Challenges for exploiting nanomagnet properties on surfaces.

Molecular complexes with single-molecule magnet (SMM) or qubit properties, commonly called molecular nanomagnets, are great candidates for information storage or quantum information processing technologies. However, the implementation of molecular nanomagnets in devices for the above-mentioned applications requires controlled surface deposition and addressing the nanomagnets' properties on the surface. This Perspectives paper gives a brief overview of molecular properties on a surface relevant for magnetic molecules and how they are affected when the molecules interact with a surface; then, we focus on systems of increasing complexity, where the relevant SMMs and qubit properties have been observed for the molecules deposited on surfaces; finally, future perspectives, including possible ways of overcoming the problems encountered so far are discussed.

Laser-Induced Ultrafast Spin Injection in All-Semiconductor Magnetic CrI3/WSe2 Heterobilayer.

Spin injection stands out as a crucial method employed for initializing, manipulating, and measuring the spin states of electrons, which are fundamental to the creation of qubits in quantum computing. However, ensuring efficient spin injection while maintaining compatibility with standard semiconductor processing techniques is a significant challenge. Herein, we demonstrate the capability of inducing an ultrafast spin injection into a WSe2 layer from a magnetic CrI3 layer on a femtosecond time scale, achieved through real-time time-dependent density functional theory calculations upon a laser pulse. Following the peak of the magnetic moment in the CrI3 sublayer, the magnetic moment of the WSe2 layer reaches a maximum of 0.89 μB (per unit cell containing 4 WSe2 and 1 CrI3 units). During the spin dynamics, spin-polarized excited electrons transfer from the WSe2 layer to the CrI3 layer via type-II band alignment. The large spin splitting in conduction bands and the difference in the number of spin-polarized local unoccupied states available in the CrI3 layer lead to a net spin in the WSe2 layer. Furthermore, we confirmed that the number of available states, the spin-flip process, and the laser pulse parameters play important roles during the spin injection process. This work highlights the dynamic and rapid nature of spin manipulation in layered all-semiconductor systems, offering significant implications for the development and enhancement of quantum information processing technologies.

A Study on the Relationship between CEO Succession Based on Big Data Information Processing Technology and Corporate Performance

The recognition of data elements as a crucial production factor in China in 2019 highlights the rising importance of leveraging data assets. The pivotal role of big data information processing technology in economic and financial realms cannot be understated. CEO succession is a pivotal event that heavily influences a company’s performance. However, past research on the correlation between CEO succession and corporate performance has yielded inconclusive results. This study addresses this gap by employing advanced analytical models, namely propensity score matching and difference-in-differences techniques, grounded in the realm of big data information processing technology to explore the intricate relationship between CEO succession and corporate performance. By analyzing data from A-share listed companies spanning from 2014 to 2021, noteworthy insights have emerged. Firstly, a notable negative correlation between CEO succession and corporate performance has been observed, affirming the notion of a “disruptive effect” associated with CEO transitions, leading to a decline in corporate performance. Furthermore, the study sheds light on the multi-faceted nature of factors influencing corporate performance, cautioning against attributing poor performance solely to the CEO. Consequently, enhancing corporate performance demands a nuanced approach that goes beyond mere CEO replacements, emphasizing a holistic strategy encompassing various avenues for improvement.

An Anonymous Authenticated Group Key Agreement Scheme for Transfer Learning Edge Services Systems

The visual information processing technology based on deep learning (DL) can play many important yet assistant roles for unmanned aerial vehicles (UAV) navigation in complex environments. Traditional centralized architectures usually rely on a cloud server to perform model inference tasks, which can lead to long communication latency. Using transfer learning (TL) to unload deep neural networks (DNN) to the edge-fog collaborative networks has become a new paradigm for dealing with the conflicts between computing resources and communication latency. However, ensuring the security of edge-fog collaborative networks entity is still challenging. For such, we propose an anonymous authentication and group key agreement scheme for the UAV-enabled edge-fog collaborative networks, consisting of UAV authentication protocol and collaborative networks authentication protocol. Utilizing the AVISPA assessment tool and security analysis, the security requirements and functional features of the proposed scheme are demonstrated. From the performance results of the proposed scheme, we show that it is superior to existing authentication schemes and promising.

Information in The Three Dimensions of Time

The relationship between physics and information processing is a topic of great significance in the realm of science and technology. The laws of physics, which govern the behavior of matter and energy, also have a profound influence on the way information is processed and utilized. While the mathematics governing information processing has not extensively utilized the laws of physics, it is evident that there is potential for these laws to play a more significant role, particularly in the context of quantum systems. based on the equivalence between information and energy, we can explore how other laws of physics can effectively contribute to more efficient and advanced information processing techniques. This research sheds light on the interconnectedness of physics and information processing, and its findings have the potential to significantly enhance our ability to process information, particularly in cutting-edge quantum systems. By recognizing and leveraging the influence of physics, we can pave the way for further advancements in information processing technologies.

Bibliometric Analysis of Development Trends and Research Hotspots in the Study of Data Mining in Nursing Based on CiteSpace.

With the advent of the big data era, hospital information systems and mobile care systems, among others, generate massive amounts of medical data. Data mining, as a powerful information processing technology, can discover non-obvious information by processing large-scale data and analyzing them in multiple dimensions. How to find the effective information hidden in the database and apply it to nursing clinical practice has received more and more attention from nursing researchers. To look over the articles on data mining in nursing, compiled research status, identified hotspots, highlighted research trends, and offer recommendations for how data mining technology might be used in the nursing area going forward. Data mining in nursing publications published between 2002 and 2023 were taken from the Web of Science Core Collection. CiteSpace was utilized for reviewing the number of articles, countries/regions, institutions, journals, authors, and keywords. According to the findings, the pace of data mining in nursing progress is not encouraging. Nursing data mining research is dominated by the United States and China. However, no consistent core group of writers or organizations has emerged in the field of nursing data mining. Studies on data mining in nursing have been increasingly gradually conducted in the 21st century, but the overall number is not large. Institution of Columbia University, journal of Cin-computers Informatics Nursing, author Diana J Wilkie, Muhammad Kamran Lodhi, Yingwei Yao are most influential in nursing data mining research. Nursing data mining researchers are currently focusing on electronic health records, text mining, machine learning, and natural language processing. Future research themes in data mining in nursing most include nursing informatics and clinical care quality enhancement. Research data shows that data mining gives more perspectives for the growth of the nursing discipline and encourages the discipline's development, but it also introduces a slew of new issues that needresearchers to address.

Visualization of movements in sports training based on multimedia information processing technology

Visualization of movements in sports training based on multimedia information processing technology

Left Partisanship, Corporatism, and the Reorientation of the Knowledge Economy in Advanced Capitalist Societies

Abstract While the progress of the knowledge economy is inexorable, this paper argues that partisan politics and labor market institutions can affect the direction in which the knowledge economy progresses. In particular, a combination of corporatist industrial relations systems and left partisanship tends to foster greater wage restraint, and such a wage outcome tends to encourage the greater adoption of communications technology than information-processing technology in the economy. This reorientation of the knowledge economy toward communications technology, in turn, has egalitarian distributive implications. In particular, the greater adoption of communications technology reduces wage inequality across the board in lower 90–10, 50–10, as well as 90–50 wage ratios. These arguments are tested using data across 15–21 OECD countries (1970–2015).

Computer Vision and Deep Learning Transforming Image Recognition and Beyond

Computer vision is a cutting-edge information processing technology that seeks to mimic the human visual nervous system. Its primary aim is to emulate the psychological processes of human vision to interpret and depict objective scenery. This revolutionary field encompasses a wide range of applications, including life sciences, medical diagnosis, military operations, scientific research, and many others. At the heart of computer vision lies the theoretical core, which includes deep learning, image recognition, target detection, and target tracking These elements combine to enable computers to process, analyze, and understand images, allowing for the classification of objects based on various patterns One of the standout advantages of deep learning techniques, when compared to traditional methods, is their ability to automatically learn and adapt to the specific features required for a given problem. This adaptive nature of deep learning networks has opened up new possibilities and paved the way for remarkable breakthroughs in the field of computer vision. This paper examines the practical application of computer vision processing technology and convolutional neural networks (CNNs) and elucidates the advancements in artificial intelligence within the field of computer vision image recognition. It does so by showcasing the tangible benefits and functionalities of these technologies.

Automatic analysis of public health service text based on character level convolutional neural network

The text information processing technology of public health service is one of the hot research topics at present. To improve the defects of public health service texts, such as inaccurate word segmentation, spelling errors and professional vocabulary understanding, this study designed a character-level deep neural network model on the characteristics of public health service texts. In this model, the bidirectional short and short time memory and the attention pooling operation layer are introduced to make the model better classify the text according to the context. In addition, counter perturbation is introduced in this study to improve the robustness and generalization ability of the model, thus improving its classification effect. The performance verification results show that the proposed model has better classification performance on the public health service text data set. The anti-disturbance samples generated by the model are all in the range of 0–0.2 when WMD deviation degree is measured, while most of the other methods are in the range of 0.4–0.6. The experimental object of this study is ultrasonic examination data. The experimental results show that the automatic analysis model of public health service text based on character level convolutional neural network constructed in this study has excellent accuracy and convergence speed, and has excellent performance in the classification of public health service text in different subject areas.

Harmonizing Cross View Image Transformation Through Local and Global Insights- A Review

Hyperspectral and multispectral information processing systems and technologies have demonstrated their value in enhancing agricultural productivity and practices by providing farmers and crop managers with valuable information on factors influencing crop condition and growth. These technologies play a crucial role in various agricultural applications, such as crop management, crop yield forecasting, crop disease detection, and monitoring soil, water, and land usage. To enhance the process of agriculture through effective crop management and assistance to farmer, using an advanced image transformation techniques the study delves into the exploration of techniques for harmonizing cross-view image transformation, with a focus on integrating both local and global insights. Further the study proposes the application of the 2D-DWT (Two-Dimensional Discrete Wavelet Transform) technique for image data preprocessing and the Extreme Learning Machine (ELM) algorithm for image classification in the context of hyperspectral images. Hyperspectral information sensing allows for the coverage of the electromagnetic spectrum in a single acquisition with several hundred spectral bands, resulting in a data cube containing significant spectral and spatial information ELM is particularly effective for classification problems because of its quick training time and capacity to operate with hidden nodes whose parameters are randomly assigned rather than modified iteratively. This comprehensive review aims to provide valuable insights and a critical analysis of the suggested method, shedding light on its potential contributions to the advancement of image transformation techniques for agricultural development.

Exploring data storage innovations: From DNA to holography

This paper delves into the evolution of data storage technologies, focusing on the solid-state drive (SSD) and hard disk drive (HDD) and their working principles. It explores the advancements in DNA as a storage medium, highlighting its exceptional data density and potential as a long-term storage solution. The paper discusses the challenges and limitations of DNA data storage, emphasizing the need for further research to overcome these hurdles. Additionally, the paper introduces holographic data storage as a novel optical storage technology and discusses its potential applications. It also touches on the decline of the optical disc industry due to the emergence of alternative storage technologies. The use of metal nanoparticles (MNPs) in optical disks is explored, emphasizing their role in high-contrast imaging and data storage. The paper concludes by highlighting the diverse methods of data storage, each with its unique advantages and drawbacks, and anticipates future improvements in electronic information processing technology, leading to faster data transmission and increased data storage capacity within the same volume of storage media.

Special Issue on Robotic, Mechatronic and Information Systems for Decommissioning

The Fukushima Daiichi Nuclear Power Plant accident, triggered by the Great East Japan Earthquake and the tsunami, has had a major impact on societies across the world. The subsequent focus on decommissioning efforts has attracted worldwide attention. It has been consistently asserted since the aftermath of the accident that the decommissioning of the Fukushima Daiichi Nuclear Power Plant will take approximately 30 to 40 years. The promotion of decommissioning work means that we will continue to face the problems with uncertainties in the work, as sometimes referred to as Unknown Unknowns. Typical elemental technologies required in remotely operated robotic systems for decommissioning include mobile mechanisms and their control, handling mechanisms and their control, information communication, operation interface (information presentation and command input), shielding (radiation resistance) and so on. Although many tasks using remotely operated robotic systems have been carried out with noteworthy results, there are lingering issues yet to be clarified. To ensure the safe and effective execution of various types of decommissioning tasks over a period of several decades, continuous research and development of each elemental technology are imperative, and these technologies must be integrated into a system appropriately to the assumed tasks. In addition, human resource development and the transfer of knowledge and experience are also important issues. From such viewpoint, this special issue is titled “Robotic, Mechatronic and Information Systems for Decommissioning.” It contains papers and development reports on robotics, mechatronics, information processing, and information systems technologies relevant to decommissioning work. It also features recent activities of nuclear emergency response organizations. We hope this special issue will acquaint readers with the latest research and development results and organizational activities related to decommissioning. At the same time, we hope that we were able to provide readers interested in robots, mechatronics, and information technology with an opportunity to become aware of the issues and necessary perspectives related to decommissioning and nuclear disasters. Lastly, we would like to thank the editorial board of the Journal of Robotics and Mechatronics, the editorial team of Fuji Technology Press, and the reviewers for their support in the planning and publication of this special issue. Their dedicated efforts are deeply appreciated.

Multi-level index construction method based on master-slave blockchains.

Master-slave blockchain is a novel information processing technology that is domain-oriented and uses efficient cryptography principles for trustworthy communication and storage of big data. Existing indexing methods primarily target the creation of a single-structured blockchain, resulting in extensive time and memory requirements. As the scale of domain data continues to grow exponentially, master-slave blockchain systems face increasingly severe challenges with regards to low query efficiency and extended traceback times. To address these issues, this paper propose a multi-level index construction method for the master-slave blockchain (MLI). Firstly, MLI introduces a weight matrix and partitions the entire master-slave blockchain based on the master chain structure, the weight of each partition is assigned. Secondly, for the master blockchain in each partition, a master chain index construction method based on jump consistent hash (JHMI) is proposed, which takes the key value of the nodes and the number of index slots as input and outputs the master chain index. Finally, a bloom filter is introduced to improve the column-based selection function and build a secondary composite index on the subordinate blockchain corresponding to each master block. Experimental results on three constraint conditions and two types of datasets demonstrate that the proposed method reduce the index construction time by an average of 9.28%, improve the query efficiency by 12.07%, and reduce the memory overhead by 24.4%.