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Coupled with the convolutional neural network (CNN), an intelligent Raman spectroscopy methodology for rapid quantitative analysis of four pharmacodynamic substances and soluble solid in the manufacture process of Guanxinning tablets was established. Raman spectra of 330 real samples were collected by a portable Raman spectrometer. The contents of danshensu, ferulic acid, rosmarinic acid, and salvianolic acid B were determined with high-performance liquid chromatography-diode array detection (HPLC-DAD), while the content of soluble solid was determined by using an oven-drying method. In the establishing of the CNN calibration model, the spectral characteristic bands were screened out by a competitive adaptive reweighted sampling (CARS) algorithm. The performance of the CNN model is evaluated by root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP), coefficient of determination of calibration (Rc2), coefficient of determination of cross-validation (Rcv2), and coefficient of determination of validation (Rp2). The Rp2 values for soluble solid, salvianolic acid B, danshensu, ferulic acid, and rosmarinic acid are 0.9415, 0.9246, 0.8458, 0.8667, and 0.8491, respectively. The established model was used for the analysis of three batches of unknown samples from the manufacturing process of Guanxinning tablets. As the results show, Raman spectroscopy is faster and more convenient than that of conventional methods, which is helpful for the implementation of process analysis technology (PAT) in the manufacturing process of Guanxinning tablets.

The quality assurance of bulk medicinal materials, crucial for botanical drug production, necessitates advanced analytical methods. Conventional techniques, including high-performance liquid chromatography, require extensive pre-processing and rely on extensive solvent use, presenting both environmental and safety concerns. Accordingly, a non-destructive, expedited approach for assessing both the chemical and physical attributes of these materials is imperative for streamlined manufacturing. We introduce an innovative method, designated as Squeeze-and-Excitation Residual Network Combined Hyperspectral Image Analysis (SE-ReHIA), for the swift and non-invasive assessment of the chemical makeup of bulk medicinal substances. In a demonstrative application, hyperspectral imaging in the 389–1020 nm range was employed in 187 batches of Salvia miltiorrhiza. Notable constituents such as salvianolic acid B, dihydrotanshinone I, cryptotanshinone, tanshinone IIA, and moisture were quantified. The SE-ReHIA model, incorporating convolutional layers, maxpooling layers, squeeze-and-excitation residual blocks, and fully connected layers, exhibited Rc2 values of 0.981, 0.980, 0.975, 0.972, and 0.970 for the aforementioned compounds and moisture. Furthermore, Rp2 values were ascertained to be 0.975, 0.943, 0.962, 0.957, and 0.930, respectively, signifying the model’s commendable predictive competence. This study marks the inaugural application of SE-ReHIA for Salvia miltiorrhiza’s chemical profiling, offering a method that is rapid, eco-friendly, and non-invasive. Such advancements can fortify consistency across botanical drug batches, underpinning product reliability. The broader applicability of the SE-ReHIA technique in the quality assurance of bulk medicinal entities is anticipated with optimism.

Purpose The purpose of this paper is to establish mechanisms for process improvement so that production efficiency and product quality can be expected, and create a sustainable development in terms of circular economy. Design/methodology/approach The authors obtain a critical value from statistical hypothesis testing, and thereby construct a process capability indices chart, which both lowers the chance of quality level misjudgment caused by sampling error and provides reference for the processes improvement in poor quality levels. The authors used the bottom bracket of bicycles as an example to demonstrate the model and methods proposed in this study. Findings This approach enables us to plot multiple quality characteristics, despite varying attributes and specifications, onto the same process capability analysis chart. And it therefore increases accuracy and precision to reduce rework and scrap rates (reduce), increase product availability, reduce maintenance frequency and increase reuse (reuse), increase the recycle rates of components (recycle) and lengthen service life, which will delay recovery time (recovery). Originality/value Parts manufacturers in the industry chain can upload their production data to the cloud platform. The quality control center of the bicycle manufacturer can utilized the production data analysis model to identify critical-to-quality characteristics. The platform also offers reference for improvement and adds the improvement achievements and experience to its knowledge management to provide the entire industry chain. Feedback is also given to the R&D department of the bicycle manufacturer as reference for more robust product designs, more reasonable tolerance designs, and selection criteria for better parts suppliers, thereby forming an intelligent manufacturing loop system.

In the present study, we developed a chatbot that helps teachers to deliver writing instructions. By working with the chatbot, the post-secondary writers developed a thesis statement for their argumentative essay outlines, and the chatbot helped the writers to refine their peer review feedback. We conducted a preliminary analysis of the effect of a chatbot on these writers' writing achievement. We also collected several student testimonials about their chatbot experiences. Several important pedagogical and research implications for chatbot-guided writing instructions and the use of learning technology have been addressed.

Applied Intelligence is one of the most important international scientific journals in the field of artificial intelligence. From 1991, Applied Intelligence has been oriented to support research advances in new and innovative intelligent systems, methodologies, and their applications in solving real-life complex problems. In this way, Applied Intelligence hosts more than 2,400 publications and achieves around 31,800 citations. Moreover, Applied Intelligence is recognized by the industrial, academic, and scientific communities as a source of the latest innovative and advanced solutions in intelligent manufacturing, privacy-preserving systems, risk analysis, knowledge-based management, modern techniques to improve healthcare systems, methods to assist government, and solving industrial problems that are too complex to be solved through conventional approaches. Bearing in mind that Applied Intelligence celebrates its 30th anniversary in 2021, it is appropriate to analyze its bibliometric performance, conceptual structure, and thematic evolution. To do that, this paper conducts a bibliometric performance and conceptual structure analysis of Applied Intelligence from 1991 to 2020 using SciMAT. Firstly, the performance of the journal is analyzed according to the data retrieved from Scopus, putting the focus on the productivity of the authors, citations, countries, organizations, funding agencies, and most relevant publications. Finally, the conceptual structure of the journal is analyzed with the bibliometric software tool SciMAT, identifying the main thematic areas that have been the object of research and their composition, relationship, and evolution during the period analyzed.

With the development of manufacturing, machining data applications are becoming a key technological component of enhancing the intelligence of manufacturing. The new generation of machine tools should be digitalized, highly efficient, network-accessible and intelligent. An intelligent machine tool (IMT) driven by the digital twin provides a superior solution for the development of intelligent manufacturing. In this paper, a real-time machining data application and service based on IMT digital twin is presented. Multisensor fusion technology is adopted for real-time data acquisition and processing. Data transmission and storage are completed using the MTConnect protocol and components. Multiple forms of HMIs and applications are developed for data visualization and analysis in digital twin, including the machining trajectory, machining status and energy consumption. An IMT digital twin model is established with the aim of further data analysis and optimization, such as the machine tool dynamics, contour error estimation and compensation. Examples of the IMT digital twin application are presented to prove that the development method of the IMT digital twin is effective and feasible. The perspective development of machining data analysis and service is also discussed.

Principles of typical motion planning algorithms are investigated and analyzed in this paper. These algorithms include traditional planning algorithms, classical machine learning algorithms, optimal value reinforcement learning, and policy gradient reinforcement learning. Traditional planning algorithms investigated include graph search algorithms, sampling-based algorithms, interpolating curve algorithms, and reaction-based algorithms. Classical machine learning algorithms include multiclass support vector machine, long short-term memory, Monte-Carlo tree search and convolutional neural network. Optimal value reinforcement learning algorithms include Q learning, deep Q-learning network, double deep Q-learning network, dueling deep Q-learning network. Policy gradient algorithms include policy gradient method, actor-critic algorithm, asynchronous advantage actor-critic, advantage actor-critic, deterministic policy gradient, deep deterministic policy gradient, trust region policy optimization and proximal policy optimization. New general criteria are also introduced to evaluate the performance and application of motion planning algorithms by analytical comparisons. The convergence speed and stability of optimal value and policy gradient algorithms are specially analyzed. Future directions are presented analytically according to principles and analytical comparisons of motion planning algorithms. This paper provides researchers with a clear and comprehensive understanding about advantages, disadvantages, relationships, and future of motion planning algorithms in robots, and paves ways for better motion planning algorithms in academia, engineering, and manufacturing.

Computer-based scaffolding plays a pivotal role in improving students' higher-order skills in the context of problem-based learning for Science, Technology, Engineering and Mathematics (STEM) education. The effectiveness of computer-based scaffolding has been demonstrated through traditional meta-analyses. However, traditional meta-analyses suffer from small-study effects and a lack of studies covering certain characteristics. This research investigates the effectiveness of computer-based scaffolding in the context of problem-based learning for STEM education through Bayesian meta-analysis (BMA). Specifically, several types of prior distribution information inform Bayesian simulations of studies, and this generates accurate effect size estimates of six moderators (total 24 subcategories) related to the characteristics of computer-based scaffolding and the context of scaffolding utilization. The results of BMA indicated that computer-based scaffolding significantly impacted (g = 0.385) cognitive outcomes in problem-based learning in STEM education. Moreover, according to the characteristics and the context of use of scaffolding, the effects of computer-based scaffolding varied with a range of small to moderate values. The result of the BMA contributes to an enhanced understanding of the effect of computer-based scaffolding within problem-based learning.

In the era of Industry 4.0, integrating digital technologies into industrial processes has become imperative for sustaining growth and fostering innovation. This research paper explores the profound impact of AI-driven intelligent data analytics and predictive analysis on economic efficiency and managerial practices within Industry 4.0. With a focus on knowledge, innovation, technology, and society, this study delves into the transformative potential of these advanced technologies. Intelligent data analytics, powered by artificial intelligence (AI), has revolutionized the way industries harness vast datasets. Uncovering real-time patterns, correlations, and opportunities empowers decision-makers with accurate and timely insights. Predictive analysis, rooted in statistics and machine learning, aids in forecasting trends and managing risks, offering economic stability across sectors. Using a mixed-methods approach, the study combines qualitative interviews with 19 Chinese operations managers and quantitative data from an online survey of 286 managers. The study ranks various Industry 4.0 technologies through ordinal regression based on their impact on environmental sustainability and economic management. Results show that smart sensors, radio-frequency identification, AI, and analytics are the most influential technologies for enhancing economic and environmental outcomes. Conversely, technologies like additive manufacturing and automated robots yield less favorable results. The study also identifies a noticeable gap in professionals’ understanding and adoption of AI and augmented reality. Environmental concerns around the disposal of electronic waste generated by these technologies are also highlighted. The research thus offers significant insights for companies seeking to adopt intelligent data analytics to enhance economic performance and environmental sustainability. On the managerial front, the fusion of these technologies enables agile and responsive frameworks, promoting dynamic strategies in response to changing market dynamics. This culture of continual improvement fosters excellence and foresight in managerial processes. However, challenges exist, including the underutilization of data, data complexity, historical biases, and the need for tailored AI solutions across industries. Ethical considerations, data privacy, and security also pose concerns. Collaborative innovation among stakeholders is crucial to addressing these challenges and seizing opportunities. Governments, academia, and industry players must collaborate to develop technologically advanced, economically viable, and socially responsible solutions. As industries transition to Industry 4.0, this paper advocates a critical approach that embraces technology’s potential while mitigating risks. The future lies in a technologically advanced, economically resilient, and socially inclusive industrial landscape driven by AI-powered knowledge and innovation.

In recent years, driven by Industry 4.0 wave, academic research has focused on the science, engineering, and enabling technologies for intelligent and cyber manufacturing. Using a network science and data mining-based Keyword Co-occurrence Network (KCN) methodology, this work analyzes the trends in data science topics in the manufacturing literature over the past two decades to inform the researchers, educators, industry leaders of knowledge trends in intelligent manufacturing. It studies the evolution of research topics and methods in data science, Internet of Things (IoT), cloud computing, and cyber manufacturing. The KCN methodology is applied to systematically analyze the keywords collected from 84,041 papers published in top-tier manufacturing journals between 2000 and 2020. It is not practically feasible to review this large body of literature through tradition manual approaches like systematic review and scoping review to discover insights. The results of network modeling and data analysis reveal important knowledge components and structure of the intelligent and cyber manufacturing literature, implicit the research interests switch and provide the insights for industry development. This paper maps the high frequency keywords in the recent literature to nine pillars of Industry 4.0 to help manufacturing community identify research and education directions for emerging technologies in intelligent manufacturing.