Related Technologies Research Articles

An overview of the capabilities of ChatGPT for medical writing and its implications for academic integrity.

The artificial intelligence (AI) tool ChatGPT, which is based on a large language model (LLM), is gaining popularity in academic institutions, notably in the medical field. This article provides a brief overview of the capabilities of ChatGPT for medical writing and its implications for academic integrity. It provides a list of AI generative tools, common use of AI generative tools for medical writing, and provides a list of AI generative text detection tools. It also provides recommendations for policymakers, information professionals, and medical faculty for the constructive use of AI generative tools and related technology. It also highlights the role of health sciences librarians and educators in protecting students from generating text through ChatGPT in their academic work.

Industry 4.0

Industry 4.0 is considered a new industrial stage in which vertical and horizontal manufacturing processes integration and product connectivity can help companies to achieve higher industrial performance. However, little is known about how industries see the potential contribution of the Industry 4.0 related technologies for industrial performance, especially in emerging countries. Based on the use of secondary data from a large-scale survey of 27 industrial sectors representing 2,225 companies of the Brazilian industry, we studied how the adoption of different Industry 4.0 technologies is associated with expected benefits for product, operations and side-effects aspects. Using regression analysis, we show that some of the Industry 4.0 technologies are seen as promising for industrial performance while some of the emerging technologies are not, which contraries the conventional wisdom. We discuss the contextual conditions of the Brazilian industry that may require a partial implementation of the Industry 4.0 concepts created in developed countries. We summarize our findings in a framework, that shows the perception of Brazilian industries of Industry 4.0 technologies and their relations with the expected benefits. Thus, this work contributes by discussing the real expectations on the future performance of the industry when implementing new technologies, providing a background to advance in the research on real benefits of the Industry 4.0.

Community Teens' COVID-19 Experience: Implications for Engagement Moving Forward.

Data collected from pediatric primary care settings during the pandemic suggest an increase in internalizing symptoms and disparities in care based upon minoritized identity status(es). To inform care moving forward, the current study characterized the pandemic and related technology usage experiences of teenaged pediatric patients from communities with high hardship indexes. As part of a larger mixed-methods study, 17 teens (Mean age = 15.99 ± .99) and 10 caregivers independently voiced experiences related to the pandemic during remote focus group and interview sessions. Thematic analyses were used to assess qualitative data; descriptive analyses were used to characterize qualitative data. Despite no direct queries about the pandemic, 41% of teens and 40% of caregivers described their lived experiences during the pandemic. Two subthemes emerged within the primary theme of COVID-19: (1) Wellness/Mental Health and (2) Smartphone Use and Utility. Although distress and negative effects were voiced, questionnaire data indicated normative psychosocial functioning for both teen self-report and caregiver proxy report. Informed by the voiced experiences of teens and their caregivers from communities with high hardship indexes, methods for better assessing and managing internalizing symptoms in teen patients are presented. A multi-modal and multi-informant approach that leverages technology to garner information about teens' experiences and deliver care may help improve the well-being of teens in communities systemically burdened with disparities.

Impact of CEO's Digital Technology Orientation and Board Characteristics on Firm Value: A Signaling Perspective

Integrating signaling research with the institutional perspective on capital markets, we argue that, in conditions of radical technological change, investor perceptions about firm value are enhanced by the CEO's orientation toward digital technologies that exceeds the firm's industry peers. This base relationship is moderated by board characteristics so that the board members’ digital expertise and knowledge diversity enhance the effect of the CEO's relative digital technology orientation on firm value. Furthermore, the monitoring power of independent board members who do not have digital expertise negatively moderates our baseline hypothesis, whereas board monitoring exerted by independent board members with digital expertise has a positive moderating effect. To test our theory, we use advanced natural language processing techniques to develop the CEO's relative digital technology orientation construct combined with a unique, hand-collected set of measures associated with board members’ digital expertise and knowledge diversity in a sample of S&P 500 companies. Our article offers novel insights on how technology-related signals associated with the CEO's communications to shareholders interact with board characteristics in determining investor perceptions of the firm's value in conditions of high technological uncertainty.

Construction method for ecological protection of stone side slopes using composite vegetation concrete

Traditional ecological restoration technology has many challenges in dealing with the greening of steep rock slopes, especially in the case of serious soil erosion. In order to overcome these problems, this study aims to organically combine the traditional protection and reinforcement technology with the new vegetation restoration technology, and put forward a new ecological protection technology of composite vegetation concrete to realize the comprehensive protection and ecological restoration of rocky slopes. In this paper, by analyzing the mechanism and existing problems of ecological protection of rocky slopes, the design requirements of composite vegetation concrete are studied in detail, and the related construction technology is expounded. In the experiment, the vegetation coverage and incidence were systematically analyzed, and the results showed that some samples showed high vegetation coverage and low incidence. Comprehensive consideration shows that the average vegetation coverage rate is 93.7%, and the average incidence rate is only 5.21%, which all meet the corresponding standards. The composite vegetation concrete technology has a wide application prospect in stone slope protection, which significantly improves the slope vegetation coverage and compressive strength, and effectively promotes the slope greening and ecological sustainable development. Through this study, we aim to convey to readers that the comprehensive method of combining traditional and innovative technologies can achieve encouraging results in solving the problem of ecological restoration of steep rock slopes, and provide useful reference for engineering practice in similar environments.

Reform of college students’ teaching management informatization under the background of big data and IoT

With the continuous layout of intelligent processing technology in the teaching field, it has become an important trend to apply big data and Internet of Things technology to the teaching management of college students. Big data technology can comprehensively analyze the teaching situation of students through massive data, and then provide the best solution, the current big data technology already has a strong technical foundation, which can provide assistance for students’ teaching. The Internet of Things technology can collect personal information of students through sensors and miniature portable devices, and provide the data to the background server for big data analysis. In order to analyze the current status of the informationization of teaching management for college students, this paper first surveys some colleges and institutions through questionnaire surveys, and conducts data analysis on the collected questionnaires, and puts forward a solution based on big data and the Internet of Things based on the data analysis results. Finally, the combination of big data and Internet of Things technology for student teaching data collection, effect evaluation scheme, intelligent arrangement of integrated courses, intelligent recommendation scheme for students’ teaching needs, and teaching management information visualization scheme are analyzed, and it is found that the combination of big data and Internet of Things related technologies It can effectively improve the efficiency of teaching management of college students.

Application of titanium suboxide electrode in electrochemical polarity reversal descaling-filtration crystallization coupling system

In the electrochemical (EC) polarity reversal descaling, frequent polarity reversal shortens the electrode's service life, and the large amount of scale peeled off from the cathode can impact the quality of the effluent. To overcome these challenges, this study proposes a periodic polarity reversal electrochemical descaling process coupled with a filtration crystallization (FC) system. Both the anode and cathode utilize titanium suboxide (Ti4O7) electrodes to remove hardness ions from circulating cooling water (CCW). The unique oxygen vacancy structure of Ti4O7 electrodes allows for an extended electrode lifespan which is 2.5 times electrode lifespan under the constant current supply mode through periodic polarity reversal. In comparison to mesh electrodes, plate electrodes exhibit higher water treatment efficiency and scale detachment ability. The optimal descaling efficiency of 17–20 % was achieved at a current density of 8 mA·cm−2 and a flow rate of 2.7 L·h−1. After operating for 420 min and then reversing the polarity, the scale detachment reaches 90.2 % within 20 min of polarity reversal. By coupling the electrochemical process with a filtration crystallization system filled with artificial zeolites, the overall hardness removal of the system can be increased to 25.5 %–27.3 %. Furthermore, the system maintains good treatment performance even after 20 cycles. This study innovatively applies the Ti4O7 electrodes to electrochemical polarity reversal descaling, providing a reference for the development of related descaling technologies.

Overview of Underwater Robots for Hull Cleaning

At present, the cleaning of the hull surface in China is carried out in the dock, and the cleaning equipment that can be successfully used in underwater operations still needs the assistance of divers in the water, which is high cost and low efficiency. This paper introduces the current situation of hull cleaning robots at home and abroad, as well as the advantages and disadvantages of related technologies, such as dock cleaning technology, underwater cleaning technology, etc., and analyzes the development trend of underwater hull cleaning equipment in the future, providing a theoretical basis for the development of hull cleaning equipment. Underwater cleaning robots have been a rapidly developing research and application field in recent years. Its main goal is to perform cleaning, maintenance, and monitoring tasks in an underwater environment by automated or semi-automated means. Such robots are used in a wide variety of application scenarios, including but not limited to oilfield maintenance, marine debris removal, reservoir and pond management, and ship maintenance study design: Underwater cleaning robots are capable of working long hours in harsh environments and their programmability and versatility enable them to perform a variety of complex tasks. In addition, with the development of advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and deep learning, the level of intelligence of these robots is also gradually improving. With global industrialization and population growth, the problem of underwater environmental pollution is becoming more and more serious. Traditional underwater cleanup methods often rely on human labor, which is not only inefficient but also can pose a safety risk to workers. Therefore, automation technology has great potential and value in this field. To sum up, underwater cleaning robot is a multidisciplinary field, involving mechanical engineering, electronic engineering, computer science, and environmental science. It not only contributes to solving current pressing environmental problems but also provides new research and application opportunities in related fields.

Silicon wafer bonding by the glass-like sol-gel formed nanocomposite

We have herein developed a glass-forming composition and the related sol-gel technology for bonding monocrystalline silicon wafers to produce «silicon–insulator–silicon» structures. A possibility to fabricate defect-free glass-like bonding layers at the annealing temperature decreased to 1000–1100 °C is demonstrated. The composites obtained by the sol-gel method can be used in technological processes of formation of the solid compound of silicon wafers.

PNP: A Review Framework towards Efficient NeRF Study and Research

Rapid advancements in techniques and applications of NeRF technologies have been seen in both academia and industry recently. It becomes apparent that there is a need to consolidate and organize the theoretical and practical knowledge on the end to end pipeline of using NeRF technology. To facilitate the study and research on NeRF technologies, we propose a novel framework that identifies the key stages for NeRF related technology, namely: Pre-NeRF, NeRF, Post-NeRF, or ``PNP" in short. Each stage has four attributes: A description of what and how NeRF technology is adapted at that stage, the common challenges faced, software that is used to handle the tasks, and how it is applicable to the next stage in the framework.

Digital workflows part 2: applications of digital technology in orthognathic surgical planning – a case series

This is the second article in a two-part series considering the relevance and clinical uses of digital technologies in relation to orthodontics. The aim is to take a closer look at the application of digital technology in relation to joint orthodontic/orthognathic treatment and present two clinical cases that have undergone treatment by means of a digital workflow. CPD/Clinical relevance: Digital technologies can enhance pre-operative orthognathic planning

Digital workflows part 1: applications of digital technology in orthodontics

This is the first article in a two-part series considering the relevance and clinical uses of digital technologies in relation to orthodontics. The aim is to take a closer look at a ‘point-of-care’ digital service and highlight the key areas in which digital workflows have been employed, along with areas of future development. The second article will document the application of digital workflows in joint orthodontic/orthognathic treatment and present two clinical cases which have undergone treatment by means of a digital workflow. CPD/Clinical relevance: Digital technologies can enhance orthodontic clinical care.

Arduino IOT Based Inventory Management System Using Load Cell and NodeMCU

Nowadays, everything is made simpler with information and communication technological advancements. It is preferable to track and monitor using devices rather than perform it manually. This resulted in the rapid growth of Internet of Things (IoT) technology and relevant markets. Low cost IoT products has made access to IoT much easier and desirable. These low cost IoT devices and related technologies are widely used in areas such as educational, transportation, tracking, inventory management and many more. The use of Arduino and RFID in the inventory management system lacks in some areas including hardware limitations. In conjunction to the limitation of using an Arduino and RFID technology, this project aims to develop an IoT based inventory management system that incorporates the uses of a NodeMCU and a load cell. In comparison of the NodeMCU to an Arduino, the NodeMCU stands out with the built in Wi-Fi module with much higher processor and additional properties of it being much smaller. While the use of a load cell is much more convenient as to suit all kinds of inventory management needs compared to the use of RFID that suits better to larger scale businesses with larger inventory and massive stocks. Towards the end, this project is expected to ease inventory management by the implementation of IoT with IoT Based Inventory Management System using Load Cell and NodeMCU. The project will generate the inventory count and automatically stores data in the cloud platform. These data can be accessed with internet connection. The project also alerts users when the inventory is low or high in balance. The output of the project is that the project’s working prototype was successfully developed. Overall, the project is a success as all the objectives of the project was successfully achieved.

Bias detection and knowledge graph comparison analysis for medical record datasets

Abstract In this paper, firstly, we study the reinforcement learning algorithm, which is a related technology of knowledge graph, and use reinforcement learning to structure the application framework of health care knowledge graph and construct the domain dictionary. And based on the BitLab21 dataset, entity relationship annotation is performed on the named entity recognition result set to realize entity relationship extraction of clinical electronic medical records. Then, the HacRED dataset is used to evaluate the deep learning model, and comparison tests and ablation experiments are conducted to verify the effectiveness of reinforcement learning for constructing knowledge graphs, respectively. Finally, a deviation monitoring method and a data feature extraction method for the case dataset are proposed, and a comparative study of different feature extraction methods is conducted to illustrate the advantages of feature fusion in distinguishing abnormal deviation patterns. All three features have relatively good recognition accuracy for normal patterns, which can reach 100%. The error curves perform well, and their training and testing errors converge very quickly to below 0.01 with less than 5 iterations and achieve nearly 100% recognition accuracy. Reinforcement learning-based knowledge mapping of case datasets and feature fusion-based data machine bias detection can improve the efficiency of medical detection.

Brake-by-wire system for passenger cars: A review of structure, control, key technologies, and application in X-by-wire chassis

Electrification, networking and intelligence are the major development trends of the modern automobile industry for improving efficiency and safety and reducing emissions of the transport system. As an integral component of a wire-controlled chassis in passenger cars, brake-by-wire (BBW) system plays a key role in improving braking energy efficiency, safety, and ride comfort. This paper presents a systematic and complete review on BBW and its related technologies in passenger car applications. First, the architectures and working principles of major BBW systems are covered in details. Then, state-of-art control strategies for BBW systems are expounded. In particular, BBW-involved active safety control are presented. Finally, the remaining challenges and future research directions are discussed. The integrated design of BBW is a major development direction while other BBW systems except the Electro-Hydraulic System still need to solve some key technological challenges. Besides, efficient coordinated control of the X-by-wire chassis remains an open topic. In particular, the application of BBW in the X-by-wire chassis is a research hotspot.

China's CO2 pipeline development strategy under carbon neutrality

Under China's goal of carbon neutrality, there is a huge demand for carbon dioxide (CO2) capture, utilization, and storage technology (CCUS). CO2 transport, as a crucial link in the CCUS industrial chain, will inevitably rely on the construction of long-distance CO2 pipelines and infrastructure. Due to the late start and small scale of China's CO2 pipeline construction, and the relative lag in related technologies and policies, it is urgent to make systematical planning and achieve relevant technological breakthroughs for the future construction of CO2 pipeline network as a public infrastructure. Therefore, a three-stage pipeline development path is proposed based on the analysis of the spatial distribution characteristics and the temporal pattern of large-scale commercial development of CO2 carbon sources and storage in China. Moreover, solutions and suggestions are proposed upon the developments analysis in China from the perspectives of pipeline transportation technology, safety evaluation, materials and equipment, and integrity. The results show that the construction of CO2 pipeline network as an important CCUS infrastructure will accelerate with the development of the global CCUS industry; the spatial distribution of CO2 emission sources and storage is unbalanced in China, with 65.8% from the eastern and central China, and CO2 flooding and geological storage contributing to most of large-scale CO2 pipeline transportation. The conclusions are as follows. First, the three-stage development path is a million-ton demonstration project of supercritical CO2 transmission pipeline before China peaks carbon emissions. After the achievement of carbon peak, a 10 million-ton regional network architecture of CO2 pipeline network centered on basins will be built, and trunk pipelines between regions will be formed before achieving carbon neutrality, establishing the national carbon transmission network with a transportation capacity of 10 × 108 ton, and a total mileage of approximately 6 × 104 km. Second, we should carry out research on the technology and equipment for the full phase CO2 pipeline transportation onshore and offshore, improve the domestic pipeline transportation technology, and promote the continuous improvement of the regulatory system in the pipeline transportation industry, so as to facilitate the implementation of demonstration projects.

Design and experimental study of electromagnetic clutch-type variable pre-pressure ultrasonic motor

The traveling-wave rotary ultrasonic motor (TRUM) technique has become increasingly mature deepening of related research and development of related technology. The unique friction drive mode of a TRUM provides the advantages of a low speed, large torque, fast response, and a self-locking feature for power failure but also suffers from a relatively low efficiency. Regarding speed control, the commonly used approach of combining frequency modulation and speed regulation has provided the advantages of high precision and a wide range. Still, the load-carrying capacity of a TRUM is reduced when the driving frequency deviates from the resonant point, which is not conducive to the characteristics of low speed and large torque of the TRUM. To address this limitation, this study uses the pre-pressure and designs an electromagnetic clutch-type variable pre-pressure self-adjusting ultrasonic motor based on the TRUM-70 hollow ultrasonic motor. In addition, the force and motion analyses are conducted on the core components, and the basic parameters of the electromagnetic mechanism are calculated according to the specific design requirements. Furthermore, the Maxwell equation is used to simulate the designed magnetic circuit, and a prototype is fabricated and assembled. The feasibility of the proposed motor design is verified through experimental study, and the performance of the motor is compared with those of conventional ultrasonic motors. The experimental results show that the proposed electromagnetic clutch-type variable pre-pressure ultrasonic motor can effectively improve the output efficiency of the motor, especially when the load varies significantly.

An efficient framework for solving forward and inverse problems of nonlinear partial differential equations via enhanced physics-informed neural network based on adaptive learning

In recent years, the advancement of deep learning has led to the utilization of related technologies to enhance the efficiency and accuracy of scientific computing. Physics-Informed Neural Networks (PINNs) are a type of deep learning method applied to scientific computing, widely used to solve various partial differential equations (PDEs), demonstrating tremendous potential. This study improved upon original PINNs and applied them to forward and inverse problems in the nonlinear science field. In addition to incorporating the constraints of PDEs, the improved PINNs added constraints on gradient information, which further enhanced the physical constraints. Moreover, an adaptive learning method was used to update the weight coefficients of the loss function and dynamically adjust the weight proportion of each constraint term. In the experiment, the improved PINNs were used to numerically simulate localized waves and two-dimensional lid-driven cavity flow described by partial differential equations. Meanwhile, we critically evaluate the accuracy of the prediction results. Furthermore, the improved PINNs were utilized to solve the inverse problems of nonlinear PDEs, where the results showed that even with noisy data, the unknown parameters could be discovered satisfactorily. The study results indicated that the improved PINNs were significantly superior to original PINNs, with shorter training time, increased accuracy in prediction results, and greater potential for application.

Differences in Learning Effectiveness Across Management Learning Environments: A Cognitive Load Theory Perspective

The extant literature indicates that blended learning leads to better outcomes compared to traditional lectures in management education. However, the working memory, which processes all incoming information, can be assumed to already work at capacity in traditional lectures. As blended environments cannot extend this capacity, they can only improve learning effectiveness if they can influence the mechanics underlying the working memory. Drawing on cognitive load theory from educational psychology, we posit that blended learning, by using technology as a differentiator, provides instructional designers with additional options and tools. When utilized effectively, these choices can reduce learners’ cognitive load related to the design and increase cognitive load related to learning. Our assumptions are based on a case study with two different learning formats, including a blended environment that actively integrates technologies into the curriculum. Empirical evidence supports our hypotheses. We contribute to educational technology research in management education by explicitly considering the mechanics of the cognitive system and the effects of instructional design, curriculum choice, and related technology use. Our results suggest that blended environments can improve learning effectiveness if technologies are well integrated into curricula. Educational technologies, thus, provide entirely new opportunities for management educators but also require faculty development.

Serotonin: A new super effective functional monomer for molecular imprinting. The case of TNF-[formula omitted] detection in real matrix by Surface Plasmon Resonance

Molecular imprinting and related technologies are becoming increasingly appreciated in bioanalysis and diagnostic applications. Among the imprinted polymers, we have already demonstrated that the endogenous neurotransmitters (NTs) dopamine (DA) and norepinephrine (NE) can be efficiently used as natural and sustainable monomers to straightforwardly design and synthesize a new generation of green and “soft” Molecularly Imprinted BioPolymers (MIBPs). Here, we demonstrated for the first time the ability of a further NT, i.e., serotonin (SE), in forming adhesive imprinted nanofilms coupled to label-free optical biosensing. Its imprinting efficiency is compared with those obtained with PDA and PNE. As a model study, tumor necrosis factor-alpha (TNF-α) was selected as a biomolecular target of interest in clinical diagnostics. The biomimetic receptor was coupled to Surface Plasmon Resonance (SPR), and TNF-α detection was performed in label-free and real-time manner both in buffer and biological matrices, i.e. synovial fluid and human serum. The results indicate that, under the same imprinting and binding conditions, the analytical performances of PSE are impressively superior to those of PDA and PNE. The PSE-based MIBP was able to detect TNF-α in human matrices with a good sensitivity, selectivity, and repeatability.