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Facing turbulent environments, firms have strived to achieve greater supply chain resilience (SCR) to leverage the resources and knowledge of supply chain members. Both SCR and supply chain integration (SCI) require digitization in the supply chain, but their interrelationships have rarely been researched empirically. This paper aims to uncover the impact of digital technology (DT) on SCR and SCI and the role of SCI in mediating between DT and SCR.

China manufacturing enterprises were surveyed through a Web-based questionnaire, and 96 responses were received. Structural equation modeling was used to test the conceptual model.

The level of enterprise digitization is not directly related to supply chain resilience, but the level of enterprise digitization has a positive impact on the improvement of SCI and SCI also has a positive effect on SCR. Therefore, SCI has a complete intermediary effect between the level of DT and SCR.

This is a pioneer study to examine the relationships among DT, SCI and SCR. The findings of this study present that firms need to improve DT, SCI and SCR consequently.

The purpose of this study is to explore the effects of priming on consumer emotions and willingness to pay as consumers experience two services with two opposite valences.

A 2 (service experience sequence: failure–success, success–failure) × 3(priming: positive, negative, no priming) between-subject experiment was conducted with 230 college students in China.

Results indicate that when priming information is included, people give greater decision weight to the second service. Specifically, in the failure–success scenario, priming information between two services increases positive emotions and decreases negative emotions, raising willingness to pay. In the success–failure scenario, priming information decreases positive emotions and increases negative emotions, thus lowering willingness to pay.

First, if businesses discover the possibility of a service failure, then disclosing negative information is better than whitewashing the truth. Second, services following a campaign of positively framed messages should be carefully rendered. The damage of pre-failure positive priming is most certainly irreparable. Finally, in terms of communication, businesses and service providers should cater to consumers exposed to different levels of information accordingly.

Previous investigations focusing on a single purchase have argued that priming effects should cause consumers of varying tastes to react in a more unified manner to a service. This study extends the research scope to more realistic situations ”sequential service experiences with opposite valences” and asserts that differences in service experiences alter the influence of priming information.

The purpose of this paper is to focus on the accurate and steady control on trajectory tracking for wafer transfer robot, suppress the vibration and reduce the contour error.

The wafer transfer robot dynamic model is modeled. Through analyzing the characteristics of wafer transfer robot, cross‐coupled synchronized control is proposed based on the contour error model in task space to improve synchronization of the joints; the shaping for the joints by input shaper in task space is applied to suppress the vibration of the end effector during trajectory tracking. Then combining the cross‐coupled synchronized control with input shaping is proposed to improve accuracy and suppress the vibration.

The combination of cross‐coupled synchronized control and input shaping control method can improve the contour accuracy and reduce the vibration simultaneously during trajectory tracking. And the control method can be used to control the trajectory of wafer transfer robot.

The transfer station is in the center of the robot body. When the transfer station may deviate from the center of the robot body, the synchronizing performance of three axes on the same plane must be considered.

The proposed method can be used to solve the vibration and synchronizing performance problems on similar SCARA robots in semi‐conductor and liquid crystal display industry.

The proposed control method takes advantage of the cross‐coupled synchronized control and input shaping control method. This combination has improved contour accuracy and reduced vibration than applying other methods, and it has achieved better performance than using single one control method only.

Nowadays, the speech emotion recognition (SER) model has enhanced as the main research topic in various fields including human–computer interaction as well as speech processing. Generally, it focuses on utilizing the models of machine learning for predicting the exact emotional status from speech. The advanced SER applications go successful in affective computing and human–computer interaction, which is making as the main component of computer system's next generation. This is because the natural human machine interface could grant the automatic service provisions, which need a better appreciation of user's emotional states.

This paper implements a new SER model that incorporates both gender and emotion recognition. Certain features are extracted and subjected for classification of emotions. For this, this paper uses deep belief network DBN model.

Through the performance analysis, it is observed that the developed method attains high accuracy rate (for best case) when compared to other methods, and it is 1.02% superior to whale optimization algorithm (WOA), 0.32% better from firefly (FF), 23.45% superior to particle swarm optimization (PSO) and 23.41% superior to genetic algorithm (GA). In case of worst scenario, the mean update of particle swarm and whale optimization (MUPW) in terms of accuracy is 15.63, 15.98, 16.06% and 16.03% superior to WOA, FF, PSO and GA, respectively. Under the mean case, the performance of MUPW is high, and it is 16.67, 10.38, 22.30 and 22.47% better from existing methods like WOA, FF, PSO, as well as GA, respectively.

This paper presents a new model for SER that aids both gender and emotion recognition. For the classification purpose, DBN is used and the weight of DBN is used and this is the first work uses MUPW algorithm for finding the optimal weight of DBN model.

The purpose of this study is to determine the cavitation flow characteristics of the high-pressure differential control valve. The relationship between cavitation, flow coefficient and spool angle is obtained. By analyzing the relationship between different spool angles and energy loss, the energy loss at different spool angles is predicted.

A series of numerical simulations were performed to study the cavitation problem of a high-pressure differential control valve using the RNG k–e turbulence model and the Zwart cavitation model. The flow states and energy distribution at different spool angles were analyzed under specific working conditions.

The cavitation was the weakest when the spool angle was 120° or the outlet pressure was 8 MPa. The pressure and speed fluctuations of the valve in the throttle section were greater than those at other locations. By calculating the entropy production rate, the reason and location of valve energy loss are analyzed. The energy loss near the throttling section accounts for about 92.7% of the total energy loss. According to the calculated energy loss relationship between different regions of the spool angle, the relationship between any spool angle and energy loss in the [80,120] interval is proposed.

This study analyzes the cavitation flow characteristics of the high-pressure differential control valve and provides the law of energy loss in the valve through the analysis method of entropy. The relationship between spool angle and energy loss under cavitation is finally proposed. The research results are expected to provide a theoretical basis for the optimal design of valves.

This chapter reports on implementing a transformative business analytics course integrating AI and AI literacy at Gies College of Business, University of Illinois, Urbana-Champaign (UIUC). The course employs a novel teaching approach using Mathematica integrated with AI functionalities, including a GPT-powered chatbot. This integration facilitates an innovative ‘AI Mashup’ method, enhancing students’ ability to analyse diverse data types and produce compelling data narratives. Key course features include practical applications of computational recipes for complex analytics, interactive digital textbooks, and an emphasis on minimal coding for maximum functionality. Feedback from students indicates a high appreciation for the diverse applications enabled by powerful tools and the structured, beginner-friendly curriculum. The findings suggest that AI-integrated tools can enhance business analytics education by simplifying technical complexities and focusing on data storytelling, thereby preparing students more effectively for the digital economy’s demands with increased AI literacy.

In general, Indian Classical Music (ICM) is classified into two: Carnatic and Hindustani. Even though, both the music formats have a similar foundation, the way of presentation is varied in many manners. The fundamental components of ICM are raga and taala. Taala basically represents the rhythmic patterns or beats (Dandawate et al., 2015; Kirthika and Chattamvelli, 2012). Raga is determined from the flow of swaras (notes), which is denoted as the wider terminology. The raga is defined based on some vital factors such as swaras, aarohana-avarohna and typical phrases. Technically, the fundamental frequency is swara, which is definite through duration. Moreover, there are many other problems for automatic raga recognition model. Thus, in this work, raga is recognized without utilizing explicit note series information and necessary to adopt an efficient classification model.

This paper proposes an efficient raga identification system through which music of Carnatic genre can be effectively recognized. This paper also proposes an adaptive classifier based on NN in which the feature set is used for learning. The adaptive classifier exploits advanced metaheuristic-based learning algorithm to get the knowledge of the extracted feature set. Since the learning algorithm plays a crucial role in defining the precision of the raga recognition, this model prefers to use the GWO.

Through the performance analysis, it is witnessed that the accuracy of proposed model is 16.6% better than NN with LM, NN with GD and NN with FF respectively, 14.7% better than NN with PSO. Specificity measure of the proposed model is 19.6, 24.0, 13.5 and 17.5% superior to NN with LM, NN with GD, NN with FF and NN with PSO, respectively. NPV of the proposed model is 19.6, 24, 13.5 and 17.5% better than NN with LM, NN with GD, NN with FF and NN with PSO, respectively. Thus it has proven that the proposed model has provided the best result than other conventional classification methods.

This paper intends to propose an efficient raga identification system through which music of Carnatic genre can be effectively recognized. This paper also proposes an adaptive classifier based on NN.

Free knowledge sharing in the online health community has been widely documented. However, whether free knowledge sharing can help physicians accumulate popularity and further the accumulated popularity can help physicians attract patients remain unclear. To unveil these gaps, this study aims to examine how physicians' popularity are affected by their free knowledge sharing, how the relationship between free knowledge sharing and popularity is moderated by professional capital, and how the popularity finally impacts patients' attraction.

The authors collect a panel dataset from Hepatitis B within an online health community platform with 10,888 observations from April 2020 to August 2020. The authors develop a model that integrates free knowledge sharing, popularity, professional capital, and patients' attraction. The hierarchical regression model is used to for examining the impact of free knowledge sharing on physicians' popularity and further investigating the impact of popularity on patients' attraction.

The authors find that the quantity of articles acted as the heuristic cue and the quality of articles acted as the systematic cue have positive effect on physicians' popularity, and this effect is strengthened by physicians' professional capital. Furthermore, physicians' popularity positively influences their patients' attraction.

This study reveals the aggregation of physicians' popularity and patients' attraction within online health communities and provides practical implications for managers in online health communities.

This study examined the dynamical and positive effects of leader consultation on employee proactivity from a motivational perspective.

Survey data were collected twice a day from 107 employees in a week by using an experience sampling method.

On a daily basis, leader consultation had a positive effect on employees’ state work engagement, which in turn promoted employees’ proactivity. Moreover, authoritarian leadership weakened the positive relationship between leader consultation and employees’ state work engagement.

The findings provided a new perspective regarding the potential dynamic motivational effect of leader consultation on employees and generated interesting implications for paradoxical leadership theory.