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This paper aims to review recent advances and applications of abrasive processes for microelectronics fabrications.

More than 80 patents and journal and conference articles published recently are reviewed. The topics covered are chemical mechanical polishing (CMP) for semiconductor devices, key/additional process conditions for CMP, and polishing and grinding for microelectronics fabrications and fan-out wafer level packages (FOWLPs).

Many reviewed articles reported advanced CMP for semiconductor device fabrications and innovative research studies on CMP slurry and abrasives. The surface finish, sub-surface damage and the strength of wafers are important issues. The defects on wafer surfaces induced by grinding/polishing would affect the stability of diced ultra-thin chips. Fracture strengths of wafers are dependent on the damage structure induced during dicing or grinding. Different thinning processes can reduce or enhance the fracture strength of wafers. In the FOWLP technology, grinding or CMP is conducted at several key steps. Challenges come from back-grinding and the wafer warpage. As the Si chips of the over-molded FOWLPs are very thin, wafer grinding becomes critical. The strength of the FOWLPs is significantly affected by grinding.

This paper attempts to provide an introduction to recent developments and the trends in abrasive processes for microelectronics manufacturing. With the references provided, readers may explore more deeply by reading the original articles. Original suggestions for future research work are also provided.

The purpose of this paper is to demonstrate a simple fiber sensor for simultaneous measurement of liquid refractive-index (RI) and temperature.

The sensor structure is formed by a long period fiber grating cascaded with a section of thin-core fiber. The long period fiber grating is fabricated on single mode fiber, followed by a section of 20-mm length thin-core fiber which is a modal interferometer.

Cladding mode interference between long period fiber grating and thin-core fiber modal interferometer is weak in the experimental investigation. Both of these two cladding mode type fiber devices are sensitive to surrounding RI and temperature. So the RI and temperature can be measured simultaneously by monitoring the spectral characteristics of the compound sensor. The sensitivity is calibrated and sensor matrix is provided in the experiment.

This proposed fiber sensor is simple, tough, cost-effective and suitable for discriminate the liquid RI and temperature with high sensitivity.

The purpose of the present study is to develop a new numerical framework that can predict the supersonic base flow more accurately, including the development of axisymmetrically separated shear layer and recompression shock. To this end, two aspects are improved and combined, i.e. a newly self-adaptive turbulence eddy simulation (SATES) turbulence modeling method and a high-order discretization numerical scheme. Furthermore, the performance of the new numerical framework within a general-purpose PHengLEI software is assessed in detail.

Satisfactory prediction of the supersonic separated shear layer with unsteady wake flow is quite challenging. By using a unified turbulence model called SATES combining high-order accurate discretization numerical schemes, the present study first assesses the performance of newly developed SATES for supersonic axisymmetric separation flows. A high-order finite differencing-based compressible computational fluid dynamics (CFD) code called PHengLEI is developed and several different numerical schemes are used to investigate the effects on shock-turbulence interactions, which include the monotonic upstream-centered scheme for conservation laws (MUSCL), weighted compact nonlinear scheme (WCNS) and hybrid cell-edge and cell-node dissipative compact scheme (HDCS).

Compared with the available experimental data and the numerical predictions, the results of SATES by using high-order accurate WCNS or HDCS schemes agree better with the experiments than the results by using the MUSCL scheme. The WCNS and HDCS can also significantly improve the prediction of flow physics in terms of the instability of the annular shear layer and the evolution of the turbulent wake.

The small deviations in the recirculation region can be found between the present numerical results and experimental data, which could be caused by the inaccurate incoming boundary layer condition and compressible effects. Therefore, a proper incoming boundary layer condition with turbulent fluctuations and compressibility effects need to be considered to further improve the accuracy of simulations.

The present study evaluates a high-order discretization-based SATES turbulence model for supersonic separation flows, which is quite valuable for improving the calculation accuracy of aeronautics applications, especially in supersonic conditions.

For the first time, the newly developed SATES turbulence modeling method combining the high-order accurate WCNS or HDCS numerical schemes is implemented on the PHengLEI software and successfully applied for the simulations of supersonic separation flows, and satisfactory results are obtained. The unsteady evolutions of the supersonic annular shear layer are analyzed, and the hairpin vortex structures are found in the simulation.

The purpose of this paper is to identify the risks involved in the construction project based on a literature survey (LS), to develop a project risk management (PRM) framework based on Industry 4.0 technologies and to demonstrate the developed framework using Internet of Things (IoT) technology.

A comprehensive LS was carried out to know the different risks involved in the construction project and developed a PRM framework based on Industry 4.0 technologies to increase the effectiveness and efficiency of PRM. Heavy equipment and parameters were identified to demonstrate the developed framework based on IoT technology of Industry 4.0.

This paper demonstrates Industry 4.0 in the various stages of PRM. LS has identified 21 risks for a construction project. The demonstration of the PRM framework has identified the sudden breakdown of equipment and uncertainty of equipment as one of the critical risks associated with heavy equipment of construction project.

The project complexity and features may add a few more risks in PRM.

The PRM framework based on Industry 4.0 technologies will increase the success rate of the project. It will enhance the efficiency and effectiveness of PRM.

The developed framework is helpful for the effective PRM of construction projects. The demonstration of PRM framework using IoT technology provides a logical way to manage risk involved in heavy equipment used in a construction project.

In response to these shortcomings, this paper proposes a dynamic obstacle detection and tracking method based on multi-feature fusion and a dynamic obstacle recognition method based on spatio-temporal feature vectors.

The existing dynamic obstacle detection and tracking methods based on geometric features have a high false detection rate. The recognition methods based on the geometric features and motion status of dynamic obstacles are greatly affected by distance and scanning angle, and cannot meet the requirements of real traffic scene applications.

First, based on the geometric features of dynamic obstacles, the obstacles are considered The echo pulse width feature is used to improve the accuracy of obstacle detection and tracking; second, the space-time feature vector is constructed based on the time dimension and space dimension information of the obstacle, and then the support vector machine method is used to realize the recognition of dynamic obstacles to improve the obstacle The accuracy of object recognition. Finally, the accuracy and effectiveness of the proposed method are verified by real vehicle tests.

The paper proposes a dynamic obstacle detection and tracking method based on multi-feature fusion and a dynamic obstacle recognition method based on spatio-temporal feature vectors. The accuracy and effectiveness of the proposed method are verified by real vehicle tests.

The latest 2019 coronavirus (COVID-2019), which first appeared in December 2019 in Wuhan's city in China, rapidly spread around the world and became a pandemic. It has had a devastating impact on daily lives, the public's health and the global economy. The positive cases must be identified as soon as possible to avoid further dissemination of this disease and swift care of patients affected. The need for supportive diagnostic instruments increased, as no specific automated toolkits are available. The latest results from radiology imaging techniques indicate that these photos provide valuable details on the virus COVID-19. User advanced artificial intelligence (AI) technologies and radiological imagery can help diagnose this condition accurately and help resolve the lack of specialist doctors in isolated areas. In this research, a new paradigm for automatic detection of COVID-19 with bare chest X-ray images is displayed. Images are presented. The proposed model DarkCovidNet is designed to provide correct binary classification diagnostics (COVID vs no detection) and multi-class (COVID vs no results vs pneumonia) classification. The implemented model computed the average precision for the binary and multi-class classification of 98.46% and 91.352%, respectively, and an average accuracy of 98.97% and 87.868%. The DarkNet model was used in this research as a classifier for a real-time object detection method only once. A total of 17 convolutionary layers and different filters on each layer have been implemented. This platform can be used by the radiologists to verify their initial application screening and can also be used for screening patients through the cloud.

This study also uses the CNN-based model named Darknet-19 model, and this model will act as a platform for the real-time object detection system. The architecture of this system is designed in such a way that they can be able to detect real-time objects. This study has developed the DarkCovidNet model based on Darknet architecture with few layers and filters. So before discussing the DarkCovidNet model, look at the concept of Darknet architecture with their functionality. Typically, the DarkNet architecture consists of 5 pool layers though the max pool and 19 convolution layers. Assume as a convolution layer, and as a pooling layer.

The work discussed in this paper is used to diagnose the various radiology images and to develop a model that can accurately predict or classify the disease. The data set used in this work is the images bases on COVID-19 and non-COVID-19 taken from the various sources. The deep learning model named DarkCovidNet is applied to the data set, and these have shown signification performance in the case of binary classification and multi-class classification. During the multi-class classification, the model has shown an average accuracy 98.97% for the detection of COVID-19, whereas in a multi-class classification model has achieved an average accuracy of 87.868% during the classification of COVID-19, no detection and Pneumonia.

One of the significant limitations of this work is that a limited number of chest X-ray images were used. It is observed that patients related to COVID-19 are increasing rapidly. In the future, the model on the larger data set which can be generated from the local hospitals will be implemented, and how the model is performing on the same will be checked.

Deep learning technology has made significant changes in the field of AI by generating good results, especially in pattern recognition. A conventional CNN structure includes a convolution layer that extracts characteristics from the input using the filters it applies, a pooling layer that reduces calculation efficiency and the neural network's completely connected layer. A CNN model is created by integrating one or more of these layers, and its internal parameters are modified to accomplish a specific mission, such as classification or object recognition. A typical CNN structure has a convolution layer that extracts features from the input with the filters it applies, a pooling layer to reduce the size for computational performance and a fully connected layer, which is a neural network. A CNN model is created by combining one or more such layers, and its internal parameters are adjusted to accomplish a particular task, such as classification or object recognition.

This study explores the contributions of fly ash, bottom ash and biomass ash from coal and biomass power plants for enhancing circular economy of construction sectors in emerging economies.

This research investigates their applications in construction, emphasizing their role in reducing environmental impact and promoting circular economy principles. Through a qualitative analysis using data from structured interviews with 41 involved stakeholders, the study highlights the economic and environmental benefits of integrating these by-products into business operations.

Currently, the cement and concrete industries can successfully adopt almost 100% fly ash, but logistic optimization is necessary to address the wet fly ash problem. The practical applications of bottom ash pose disposal challenges due to their poor adoption. Biomass ash can be alternatively implemented as a soil amendment and fertilization in the agriculture industry while current growth seems significant with the shift to a clean energy policy.

This research underscores the importance of policy support and collaboration between industry stakeholders to maximize the sustainable potential of these by-products in an emerging economy context.

The sustainability development goals (SDGs) were well-established in developing economies. Nevertheless, the literature review indicates that there is a lack of understanding regarding their backgrounds, influencing factors, challenges and practical applications for the circular economy.

This study investigates the direct influence of ambidextrous leadership on employees’ innovation behaviour, the mediating role of innovative self-efficacy and harmonious work passion, and the moderating role of Zhong-Yong thinking.

The authors conducted a series of questionnaire surveys to collect data in three time periods and from multiple sources; 332 supervisor–subordinate matched samples were obtained. The hypothesised relationships were tested using structural equation modelling and ProClin.

Ambidextrous leadership is positively associated with employees’ innovation behaviour, while innovative self-efficacy and harmonious work passion play mediating roles. The analysis further confirms that innovative self-efficacy and harmonious work passion play a chained double-mediating role between ambidextrous leadership and employees’ innovation behaviour, while Zhong-Yong thinking plays moderating roles between ambidextrous leadership and innovative self-efficacy and between ambidextrous leadership and harmonious work passion.

This study demonstrates the influence of ambidextrous leadership on employees’ innovation behaviour, specifically the role of ambidextrous leadership, and extends the relationship’s theoretical foundation. It is also expected to provide inspiration and serve as a reference for local Chinese management.

The purpose of this paper is to study the dynamic change of the migrant labor market in China from 2002 to 2007 using two comparable data sets.

To understand the factors behind the wage change, the authors use the Oaxaca-Blinder decomposition (Oaxaca, 1973; Blinder, 1973) method to study the hourly wage change over this five-year period.

The focus is on the rural-urban migration decision, the wage structure of migrants, the labor market segmentation between migrants and urban natives, and the changes of these aspects from 2002 to 2007. The paper finds that prior migration experience is a key factor for the migration decision of rural household members, and its importance keeps increasing from 2002 to 2007. The results show that there is a significant increase in wages among both migrants and urban natives over this five-year period, but migrants have enjoyed faster wage growth, and most of the increase of wages among migrants can be attributed to the increase of returns to their characteristics. The authors also find evidence suggesting convergence of urban labor markets for migrants and for urban natives during this five-year period.

In order to make the 2002 and 2007 data sets comparable, the authors had to restrict the observations with fixed residence only, and can only include seven cities. These limit the representativeness of the sample. When interpret the findings in this paper, it is important to keep this in mind.

Due to the scarcity of data, there are few studies on the dynamics of the migrating population and the migrant labor market. Since the urban natives and migrants are still segmented in the labor market, the migrant labor market may have its own characteristics, and also, because of the increasing importance of the migrants in Chinese society, knowledge of the evolution of the migrant labor market is crucial for grasping the whole story behind the Chinese economic miracle.

The purpose of this paper is to introduce several synchronization test methods of Network‐on‐Chip (NoC) at multi‐clock domains by digital logic circuits.

First, the authors gave the structure of NoC, the test methods for NoC in multi‐clock domains, including Built‐in Self Test (BIST) structure and the architecture of embedded core test. Then the authors approached four different synchronization structures: two‐level trigger, two kinds of lock methods, toggle and pulse synchronization methods. Based on the NoC work conditions, the authors built the experiment structures of different methods, and obtained the experiment results at high frequencies.

From the experiments at high frequency, it can be seen that the methods of toggle and the pulse methods are prone to failed synchronization. Therefore, the lock method is more appropriate for NoC under multiple clock domains.

In this paper, several synchronization test methods of NoC at multi‐clock domains are discussed and compared, and the best one determined.