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A W-band antennas-in-packages (AIP) module with a hybrid stacked glass-compound wafer level fan-out process was presented. Heterogeneous radio frequency (RF) chips were integrated into one single module with a microscale fan-out process. This paper aims to find a new strategy for 5G communication with 3D integration of multi-function chips.

The AIP module was composed of two stacked layers: the antenna layer and RF layer. After architecture design and performance simulation, the module was fabricated, The 8 × 8 antenna array was lithography patterned on the 12 inch glass wafer to reduce the parasitic parameters effect, and the signal feeding interface was fabricated on the backside of the glass substrate.

AIP module demonstrates a size of 180 mm × 180mm × 1mm, and its function covers the complete RF front-end chain from the antenna to signal to process and can be applied in 5 G communication and automotive components.

With three RF multi-function chips and two through silicon via (TSV) chips were embedded in the 12 inch compound wafer through the fan-out packaging process; two layers were interconnected with TSV and re-distributed layers.

The purpose of this study is to improve the accuracy of industrial robot kinematic parameter identification and position accuracy by solving the problem of insufficient consideration of error sources in the kinematic parameter identification model and optimizing the selection of measurement pose set.

In this study, a kinematic calibration method for industrial robots considering multiple error sources is proposed. Based on the Modified Denavit Hartenberg (MD-H) model, a robot kinematics identification model including joint reduction ratio error, target ball installation error and coordinate system transformation error is established. Taking the optimal observability index O1 and the minimum flexible deformation as the optimization objectives, a measurement pose set optimization method based on Non-dominated Sorting Genetic Algorithm II (NSGA-II) is proposed to obtain a measurement pose set with higher identification accuracy.

Through experiments conducted with the Nantong Zhenkang ZK1400-6 robot as the test subject, the kinematic parameters identified by the optimized measurement pose set are more accurate than the randomly selected measurement pose set, and the positioning accuracy of the robot is improved from 2.11 to 0.31 mm, an increase of 85.3%.

This study introduces a position error model that comprehensively accounts for the error sources causing positioning inaccuracies. Building on this foundation, a novel flexible deformation index is proposed to quantify the flexible deformation in the measurement pose set, thereby reducing the impact of such deformation on the position error in the model. To the best of the authors’ knowledge, for the first time, this study presents an optimization method for the measurement pose set based on NSGA-II, using the flexible deformation index and observability index as objectives for multi-objective optimization, simultaneously optimizing the pose error and Jacobian matrix in the error model.

Accounting scandals and earnings management problems at large firms such as Global Crossing and Enron have resulted in lots of wealth loss not only to corporate investors but also led tremendous damage to societies. Hence, policymakers and academic researchers have started to explore mechanisms to prevent improprieties in financial reporting and further enhance firm value. Using data from United States (US)-listed companies between 2000 and 2018, this article explores the effect of ex-military executives on earnings quality, the role of financial analysts in their interplay and the firm value implication of earnings quality driven by ex-military executives.

This study employs a firm fixed-effects model to validate the main conjecture and adopts the weighted least squares, Granger causality analysis, instrumental variable approach, propensity score matching, entropy balancing approach and dynamic system Generalized Method of Moments (GMM) estimator to address robustness and endogeneity issues.

Authors reveal that companies run by ex-military senior executives exhibit lower levels of accruals-based and real earnings management than those without. The effect of management military leadership on constraining earnings management is more prominent for companies with low analyst coverage, suggesting that the military experience of executives could be a substitute for external monitoring. Authors also find that these ethical managers alleviate the negative impact of earnings management on firm value and that companies managed by these managers exhibit higher firm performance.

This study highlights the importance of the intrinsic motivation behind the effect of military experience on senior managers' personalities and offers essential stakeholder-related implications regarding the effect of military experience. The military experience of senior managers helps facilitate the attainment of broader corporate governance and economic objectives.

This article adds new insights to the literature on the role of managerial military experience in decision-making processes, financial reporting outcomes and firm performance by employing the upper echelons and imprinting theoretical perspectives.

The safety performance of cooperative robots is particularly important. This paper aims to study collision detection and response of cooperative robots, which meet the lightweight requirements of cooperative robots and help to ensure the safety of humans and robots.

This paper proposes a collision detection, recognition and response method based on dynamic models. First, this paper identifies the dynamic model of the robot. Second, an external torque observer is established based on the model, and a dynamic threshold collision detection method is designed to reduce the interference of model uncertainty on collision detection. Finally, a collision position and direction estimation method is designed, and a robot collision response strategy is proposed to reduce the harm caused by collisions to humans.

Comparative experiments are conducted on static threshold and dynamic threshold collision detection, and the results showed that the static threshold only detected one collision while the dynamic threshold could detect all collisions. Conducting collision position and direction estimation and collision response experiments, and the results show that this method can determine the location and direction of collision occurrence, and enable the robot to achieve collision separation.

This paper designs a dynamic threshold collision detection method that does not require external sensors. Compared with static threshold collision detection methods, this method can significantly improve the sensitivity of collision detection. This paper also proposes a collision position direction estimation method and collision separation response strategy, which can enable robots to achieve post collision separation and improve the safety of cooperative robots.

The purpose of the study was to prepare melamine-urea-formaldehyde (MUF) resin that would be resistant to boiling water and high temperature and exhibit low formaldehyde emission.

The authors prepared MUF resin with different F/(M + U) and changed the amount of melamine added, through the analysis of MUF resin properties to get the best reaction parameters, and used different amino acid cure systems including NH₄Cl cured the resin.

Resin’s heat resistance and water resistance are mainly determined by the amount of melamine added, and formaldehyde emission of the plywood can be changed by adjusting F/(M + U). The peak temperature of the curing agent-cured resin increases as compared with the self-curing resin. Stronger the acidity of curing agent, faster the viscosity increased in probation period and lower the bonding strength and heat resistance of the resin.

Melamine improves the heat resistance and water resistance of the resin. When the amount of melamine is more than a certain value, water resistance of the resin decreased.

MUF resin that is resistant to boiling water and exhibits low formaldehyde emission can be used in high temperature, high humidity and strict formaldehyde emission environment and can also be combined with other materials.

It was helpful to reduce the effect of formaldehyde emission on people’s health and environmental pollution and is also beneficial for the expansion of the application range of aldehyde resin.

The originality is twofold: the influence of the acid strength of curing agent on the bonding strength of the resin adhesive and the method for preparing high performance MUF resin by following the traditional process.

Vehicle fault diagnosis is a key factor in ensuring the safe and efficient operation of the railway system. Due to the numerous vehicle categories and different fault mechanisms, there is an unbalanced fault category problem. Most of the current methods to solve this problem have complex algorithm structures, low efficiency and require prior knowledge. This study aims to propose a new method which has a simple structure and does not require any prior knowledge to achieve a fast diagnosis of unbalanced vehicle faults.

This study proposes a novel K-means with feature learning based on the feature learning K-means-improved cluster-centers selection (FKM-ICS) method, which includes the ICS and the FKM. Specifically, this study defines cluster centers approximation to select the initialized cluster centers in the ICS. This study uses improved term frequency-inverse document frequency to measure and adjust the feature word weights in each cluster, retaining the top τ feature words with the highest weight in each cluster and perform the clustering process again in the FKM. With the FKM-ICS method, clustering performance for unbalanced vehicle fault diagnosis can be significantly enhanced.

This study finds that the FKM-ICS can achieve a fast diagnosis of vehicle faults on the vehicle fault text (VFT) data set from a railway station in the 2017 (VFT) data set. The experimental results on VFT indicate the proposed method in this paper, outperforms several state-of-the-art methods.

This is the first effort to address the vehicle fault diagnostic problem and the proposed method performs effectively and efficiently. The ICS enables the FKM-ICS method to exclude the effect of outliers, solves the disadvantages of the fault text data contained a certain amount of noisy data, which effectively enhanced the method stability. The FKM enhances the distribution of feature words that discriminate between different fault categories and reduces the number of feature words to make the FKM-ICS method faster and better cluster for unbalanced vehicle fault diagnostic.

This study focuses on the relationship between top management teams' (TMTs) digital experience and company innovation efficiency. In addition, this study examines the mechanism role of digital transformation processes, including basic capability, chief information officer (CIO) appointments, process management and business management, on the relationship between TMTs' digital experience and innovation efficiency.

Based on the sample of China's A-share listed companies from 2011 to 2021, this article employs data envelopment analysis (DEA) and PSM-DID models to examine the effect of TMTs' digital experience on company innovation efficiency.

The TMTs' digital experience was positively correlated with company innovation efficiency. Artificial intelligence and digital platform are two key technologies that TMTs' digital experience effects company innovation efficiency. The financial ability of companies is a key factor to ensure the effectiveness of these two technologies. Digital foundation only played an important role in the early stages. In addition, TMTs' digital experience is more focused on technological management, not process management and business management. We also found that the CIO did not play a positive role on digital innovation. Finally, within the manufacturing industry and non-high-tech companies, the TMTs' digital experience has proven to be notably more effective in enhancing innovation efficiency.

This study advises that companies in the initial stages of digital transformation should give precedence to recruiting managers with digital experience into TMT, as their role is pivotal in propelling digital transformation in innovation. Futhermore, it is suggested that in the selection of TMT members, firms could consider dual appointments, such as CIO combined with other roles. Finally, this study recommends that TMTs with a digital background should pursue deeper technological competencies before embarking on corresponding business and process managements.

First, this study constructs a theoretical framework for examining how TMTs' digital experience influences innovation efficiency. Second, this study embedded the digital technology into the company innovation input index, elucidating how TMTs' digital experience directly enhances innovation efficiency. Finally, this study reveals how basic capability, CIO appointments, process management and business management affect the relationship between TMT digital experience and innovation efficiency.

User value co-creation behaviors are crucial for the sustainable development of Virtual Brand Communities. This research, grounded in social exchange theory, investigates the impact of community satisfaction and identification on customer value co-creation behaviors and further explores how the reciprocity norm moderates these relationships.

Our research data were collected from users across multiple brand communities, totaling 481 survey responses. Structural equation modeling was performed to test the research hypotheses.

These results provide in-depth insights into the nexus between user-community relationships and customer value co-creation behaviors. While community satisfaction and identification positively influence co-creation, their effects vary across different value co-creation behaviors. Notably, the reciprocity norm within the community dampens the relationship between community satisfaction and value co-creation behaviors.

Unlike previous studies focusing on customer value co-creation behaviors, our research emphasizes social exchange, unveiling the mechanisms behind customer value co-creation. Our findings not only enrich the body of knowledge on customer value co-creation but also deepen our understanding of online collective behavior and knowledge sharing, offering valuable insights for the development of virtual communities.

We aim to empirically investigate the effect of affiliated banker directors (ABDs) on corporate tax avoidance. Furthermore, we conduct cross-sectional analyses on the impact of ABDs and explore the underlying mechanisms through which ABDs might influence corporate tax avoidance.

Using a large sample between 1999 and 2016, we empirically examine the impact of ABDs on corporate tax avoidance. We address the endogeneity concerns through an instrumental variable approach and robustness tests with alternative measures of ABDs and corporate tax avoidance.

Our results demonstrate that firms with ABDs exhibit lower levels of corporate tax avoidance. This negative association persists after controlling for potential endogeneity issues and is robust to alternative measures. We further document that the negative effect is stronger when firms are more bank-dependent and financially constrained. Our results indicate that ABDs limit corporate tax avoidance by strengthening corporate governance, mitigating information risks and protecting their reputational capital.

This research extends the existing literature by exploring the influence of ABDs on corporate accounting policies, particularly tax avoidance. These findings enhance our understanding of how directors’ banking experience bolsters corporate governance, information transparency and reputation, ultimately safeguarding stakeholder interests. This paper offers valuable implications for both financial practitioners and policymakers.

Passive chipless RFID (radio frequency identification) sensors, devoid of batteries or wires for data transmission to a signal reader, demonstrate stability in severe conditions. Consequently, employing these sensors for metal crack detection ensures ease of deployment, longevity and reusability. This study aims to introduce a chipless RFID sensor design tailored for detecting metal cracks, emphasizing tag reusability and prolonged service life.

The passive RFID sensor is affixed to the surface of the aluminum plate under examination, positioned over the metal cracks. These cracks alter the electrical length of the sensor, thereby influencing its amplitude-frequency characteristics. Hence, the amplitude-frequency profile generated by various metal cracks can effectively ascertain the occurrence and orientation of the cracks.

Simulation and experimental results show that the proposed crack sensing tag produces different frequency amplitude changes for four directions of cracks and can recognize the crack direction. The sensor has a small size and simple structure, which makes it easy to deploy.

This research aims to deploy crack detection on metallic surfaces using passive chipless RFID sensors, analyze the amplitude-frequency characteristics of crack formation and distinguish cracks of varying widths and orientations. The designed sensor boasts a straightforward structural design, facilitating ease of deployment, and offers a degree of reusability.