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By investigating the thermal-mechanical interaction between the through silicon via (TSV) and the Cu pad, this study aimed to determine the effect of electroplating defects on the upper surface protrusion and internal stress distribution of the TSV at various temperatures and to provide guidelines for the positioning of TSVs and the optimization of the electroplating process.

A simplified model that consisted of a TSV (100 µm in diameter and 300 µm in height), a covering Cu pad (2 µm thick) and an internal drop-like electroplating defect (which had various dimensions and locations) was developed. The surface overall deformation and stress distribution of these models under various thermal conditions were analyzed and compared.

The Cu pad could barely suppress the upper surface protrusion of the TSV if the temperature was below 250 ?. Interfacial delamination started at the collar of the TSV at about 250 ? and became increasingly pronounced at higher temperatures. The electroplating defect constantly experienced the highest level of strain and stress during the temperature increase, despite its geometry or location. But as its radius expanded or its distance to the upper surface increased, the overall deformation of the upper surface and the stress concentration at the collar of the TSV showed a downward trend.

Previous studies have not examined the influence of the electroplating void on the thermal behavior of the TSV. However, with the proposed methodology, the strain and stress distribution of the TSV under different conditions in terms of temperature, dimension and location of the electroplating void were thoroughly investigated, which might be beneficial to the positioning of TSVs and the optimization of the electroplating process.

Unsupervised domain adaptation object detection not only mitigates model terrible performance resulting from domain gap, but also has the ability to apply knowledge trained on a definite domain to a distinct domain. However, aligning the whole feature may confuse the object and background information, making it challenging to extract discriminative features. This paper aims to propose an improved approach which is called intrinsic feature extraction domain adaptation (IFEDA) to extract discriminative features effectively.

IFEDA consists of the intrinsic feature extraction (IFE) module and object consistency constraint (OCC). The IFE module, designed on the instance level, mainly solves the issue of the difficult extraction of discriminative object features. Specifically, the discriminative region of the objects can be paid more attention to. Meanwhile, the OCC is deployed to determine whether category prediction in the target domain brings into correspondence with it in the source domain.

Experimental results demonstrate the validity of our approach and achieve good outcomes on challenging data sets.

Limitations to this research are that only one target domain is applied, and it may change the ability of model generalization when the problem of insufficient data sets or unseen domain appeared.

This paper solves the issue of critical information defects by tackling the difficulty of extracting discriminative features. And the categories in both domains are compelled to be consistent for better object detection.

This paper proposes a model for detecting unexpected examination scores based on past scores, current daily efforts and trend in the current score of individual students. The detection is performed soon after the current examination is completed, which helps take immediate action to improve the ability of students before the commencement of daily assessments during the next semester.

The scores of past examinations and current daily assessments are analyzed using a combination of an ANOVA, a principal component analysis and a multiple regression analysis. A case study is conducted using the assessment scores of secondary-level students of an international school in Japan.

The score for the current examination is predicted based on past scores, current daily efforts and trend in the current score. A lower control limit for detecting unexpected scores is derived based on the predicted score. The actual score, which is below the lower control limit, is recognized as an unexpected score. This case study verifies the effectiveness of the combinatorial usage of data in detecting unexpected scores.

Unlike previous studies that utilize attribute and background data to predict student scores, this study utilizes a combination of past examination scores, current daily efforts for related subjects and trend in the current score.

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.

Because of the various advantages of reinforcement learning (RL) mentioned above, this study uses RL to train unmanned aerial vehicles to perform two tasks: target search and cooperative obstacle avoidance.

This study draws inspiration from the recurrent state-space model and recurrent models (RPM) to propose a simpler yet highly effective model called the unmanned aerial vehicles prediction model (UAVPM). The main objective is to assist in training the UAV representation model with a recurrent neural network, using the soft actor-critic algorithm.

This study proposes a generalized actor-critic framework consisting of three modules: representation, policy and value. This architecture serves as the foundation for training UAVPM. This study proposes the UAVPM, which is designed to aid in training the recurrent representation using the transition model, reward recovery model and observation recovery model. Unlike traditional approaches reliant solely on reward signals, RPM incorporates temporal information. In addition, it allows the inclusion of extra knowledge or information from virtual training environments. This study designs UAV target search and UAV cooperative obstacle avoidance tasks. The algorithm outperforms baselines in these two environments.

It is important to note that UAVPM does not play a role in the inference phase. This means that the representation model and policy remain independent of UAVPM. Consequently, this study can introduce additional “cheating” information from virtual training environments to guide the UAV representation without concerns about its real-world existence. By leveraging historical information more effectively, this study enhances UAVs’ decision-making abilities, thus improving the performance of both tasks at hand.

In drawing on the conservation of resources theory and the broaden-and-build theory, the present research investigates the dynamic of social resources (i.e. servant leadership) and personal resources (i.e. psychological empowerment and positive affect) in the determination of the nurses' optimal performance (i.e. deep acting).

The research involved collecting three waves of data on 481 frontline nurses at a large hospital in Taiwan, each a month apart. The hypotheses were tested using PROCESS mediation and moderated mediation regression models.

The results supported the indirect relationship between servant leadership and deep acting through psychological empowerment as well as the moderating effect of positive affect on the mediation model.

The findings shed new light on the interplay of different resources and also provide practical implications for the development of frontline supervisors and nursing staff to be compatible with a serious orientation toward the quality of their professional functioning.

The availability of daily assessment data in a centralized monitoring system at school provides the opportunity to detect unusual scores soon after the assessment is carried out. This paper introduces a model for the detection of unusual scores of individual students to immediately improve performances that deviate from a normal state.

A student's ability, a subject's difficulty level, a student's specific ability in a subject, and the difficulty level of an assessment in a subject are selected as factor effects of a linear ANOVA model. Through analysis of variance, a case study is conducted based on 330 data points of assessment scores of primary grade students retrieved from an international school in Japan.

The actual score is below the lower control limit, which is recognized as an unusual score, and the score can be detected immediately after sitting for an assessment and is beneficial for students to take immediate remedies based on daily assessment. This is demonstrated through a case study.

The detection of unusual scores based on a linear model of individual students soon after each assessment benefits from immediate remedy aligns with a daily management concept. The daily assessment data in a school system enable detection based on individual students, subject-wise and assessment-wise to improve student performances in the same academic year.

Culture is considered a critical aspect of social media usage. The purpose of this paper is to explore how cultures and languages influence multilingual users' cross-cultural information sharing patterns.

This study used a crowdsourcing survey with Amazon Mechanical Turk to collect qualitative and quantitative data from 355 multilingual users who utilize two or more languages daily. A mixed-method approach combined statistical, and cluster analysis with thematic analysis was employed to analyze information sharing patterns among multilingual users in the Chinese cultural context.

It was found that most multilingual users surveyed preferred to share in their first and second language mainly because that is what others around them speak or use. Multilingual users have more diverse sharing characteristics and are more actively engaged in social media. The results also provide insights into what incentives make multilingual users engage in social media to share information related to Chinese culture with the MOA model. Finally, the ten motivation factors include learning, entertainment, empathy, personal gain, social engagement, altruism, self-expression, information, trust and sharing culture. One opportunity factor is identified, which is convenience. Three ability factors are recognized consist of self-efficacy, habit and personality.

The findings are conducive to promoting the active participation of multilingual users in online communities, increasing global resource sharing and information flow and promoting the consumption of digital cultural content.

This paper aims to study the agent learning from expert demonstration data while incorporating reinforcement learning (RL), which enables the agent to break through the limitations of expert demonstration data and reduces the dimensionality of the agent’s exploration space to speed up the training convergence rate.

Firstly, the decay weight function is set in the objective function of the agent’s training to combine both types of methods, and both RL and imitation learning (IL) are considered to guide the agent's behavior when updating the policy. Second, this study designs a coupling utilization method between the demonstration trajectory and the training experience, so that samples from both aspects can be combined during the agent’s learning process, and the utilization rate of the data and the agent’s learning speed can be improved.

The method is superior to other algorithms in terms of convergence speed and decision stability, avoiding training from scratch for reward values, and breaking through the restrictions brought by demonstration data.

The agent can adapt to dynamic scenes through exploration and trial-and-error mechanisms based on the experience of demonstrating trajectories. The demonstration data set used in IL and the experience samples obtained in the process of RL are coupled and used to improve the data utilization efficiency and the generalization ability of the agent.

This study aims to analyze how restaurants' collaboration with mobile food delivery applications (MFDAs) affects product development efficiency and argues that technological capabilities moderate relational ties impact the joint decision-making and development efficiency of restaurant products.

A product development efficiency model was formulated using a resource-based view and real options theory. In all, 472 samples were collected from restaurants collaborating with MFDAs, and partial least squares structural equation modeling was applied to the proposed model.

The findings of this study indicate three factors are critical to the product development efficiency between restaurants and MFDAs; restaurants must develop a strong connection with the latter to ensure meals are consistently served promptly. Developers of MFDAs should use artificial intelligence analysis, such as order records of different genders and ages or various consumption attributes, to collaborate with restaurants.

To the best of the authors’ knowledge, this study is one of the few that considers the role of MFDAs as a product strategy for restaurant operations, and the factors the authors found can enhance restaurants’ product development efficiency. Second, as strategic artificial intelligence adaptation changes, collaborating firms and restaurants use such applications for product development to help consumers identify products.