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A study of the Sn‐Ag‐Cu lead‐free solder reflow profile has been conducted. The purpose of the work was to determine the Sn‐Ag‐Cu reflow profile that produced solder bumps with a thin intermetallic compound (IMC) layer and fine microstructure. Two types of reflow profiles were studied. The results of the experiment indicated that the most significant factor in achieving a joint with a thin IMC layer and fine microstructure was the peak temperature. The results suggest that the peak temperature for the Sn‐Ag‐Cu lead‐free solder should be 230°C. The recommended time above liquidus is 40 s for the RSS reflow profile and 50‐70 s for the RTS reflow profile.

The purpose of this paper is to investigate the effects of reflow time, reflow peak temperature, thermal shock and thermal aging on the intermetallic compound (IMC) thickness for Sn3.0Ag0.5Cu (SAC305) soldered joints.

A four‐factor factorial design with three replications is selected in the experiment. The input variables are the peak temperature, the duration of time above solder liquidus temperature (TAL), solder alloy and thermal shock. The peak temperature has three levels, 12, 22 and 32°C above solder liquidus temperatures (or 230, 240 and 250°C for SAC305 and 195, 205, and 215°C for SnPb). The TAL has two levels, 30 and 90 s. The thermally shocked test vehicles are subjected to air‐to‐air thermal shock conditioning from −40 to 125°C with 30 min dwell times (or 1 h/cycle) for 500 cycles. Samples both from the initial time zero and after thermal shock are cross‐sectioned. The IMC thickness is measured using scanning electron microscopy. Statistical analyses are conducted to compare the difference in IMC thickness growth between SAC305 solder joints and SnPb solder joints, and the difference in IMC thickness growth between after thermal shock and after thermal aging.

The IMC thickness increases with higher reflow peak temperature and longer time above liquidus. The IMC layer of SAC305 soldered joints is statistically significantly thicker than that of SnPb soldered joints when reflowed at comparable peak temperatures above liquidus and the same time above liquidus. Thermal conditioning leads to a smoother and thicker IMC layer. Thermal shock contributes to IMC growth merely through high‐temperature conditioning. The IMC thickness increases in SAC305 soldered joints after thermal shock or thermal aging are generally in agreement with prediction models such as that proposed by Hwang.

It is still unknown which thickness of IMC layer could result in damage to the solder.

The IMC thickness of all samples is below 3 μm for both SnPb and SAC305 solder joints reflowed at the peak temperature ranging from 12 to 32°C above liquidus temperature and at times above liquidus ranging from 30 to 90 s. The IMC thickness is below 4 μm after subjecting to air‐to‐air thermal shock from −40 to 125°C with 30 min dwell time for 500 cycles or thermal aging at 125°C for 250 h.

The paper reports experimental results of IMC thickness at different thermal conditions. The application is useful for understanding the thickness growth of the IMC layer at various thermal conditions.

The purpose of this work is to study the effect of the reflow peak temperature and time above liquidus on both SnPb and SnAgCu solder joint shear strength.

Nine reflow profiles for Sn3.0Ag0.5Cu and nine reflow profiles for Sn37Pb have been developed with three levels of peak temperature (230°C, 240°C, and 250°C for Sn3.0Ag0.5Cu; and 195°C, 205°C, and 215°C for Sn37Pb) and three levels of time above solder liquidus temperature (30, 60, and 90 s). The shear force data of four different sizes of chip resistors (1206, 0805, 0603, and 0402) are compared across the different profiles. The shear forces for the resistors were measured after assembly. The fracture interfaces were inspected using scanning electron microscopy with energy dispersive spectroscopy in order to determine the failure mode and failure surface morphology.

The results show that the effects of the peak temperature and the time above solder liquidus temperature are not consistent between different component sizes and between Sn37Pb and Sn3.0Ag0.5Cu solder. The shear force of SnPb solder joints is higher than that of Sn3.0Ag0.5Cu solder joints because the wetting of SnPb is better than that of SnAgCu.

This study finds that fracture occurred partially in the termination metallization and partially in the bulk solder joint. To eliminate the effect of the termination metallization, future research is recommended to conduct the same study on solder joints without component attachment.

The shear strength of both SnPb and SnAgCu solder joints is equal to or higher than that of the termination metallization for the components tested.

Fracture was observed to occur partially in the termination metallization (Ag layer) and partially in the bulk solder joint. Therefore, it is essential to inspect the fracture interfaces when comparing solder joint shear strength.

This paper aims to propose a method to quickly set the heating zone temperatures and conveyor speed of the reflow oven. This novel approach intensely eases the trial and error in reflow profiling and is especially helpful when reflowing thick printed circuit boards (PCBs) with bulky components. Machine learning (ML) models can reduce the time required for profiling from at least half a day of trial and error to just 1 h.

A highly compact computational fluid dynamics (CFD) model was used to simulate the reflow process, exhibiting an error rate of less than 1.5%. Validated models were used to generate data for training regression models. By leveraging a set of experiment results, the unknown input factors (i.e. the heat capacities of the bulkiest component and PCB) can be determined inversely. The trained Gaussian process regression models are then used to perform virtual reflow optimization while allowing a 4°C tolerance for peak temperatures. Upon ensuring that the profiles are inside the safe zone, the corresponding reflow recipes can be implemented to set up the reflow oven.

ML algorithms can be used to interpolate sparse data and provide speedy responses to simulate the reflow profile. This proposed approach can effectively address optimization problems involving multiple factors.

The methodology used in this study can considerably reduce labor costs and time consumption associated with reflow profiling, which presently relies heavily on individual experience and skill. With the user interface and regression models used in this approach, reflow profiles can be swiftly simulated, facilitating iterative experiments and numerical modeling with great effectiveness. Smart reflow profiling has the potential to enhance quality control and increase throughput.

In this study, the employment of the ultimate compact CFD model eliminates the constraint of components’ configuration, as effective heat capacities are able to determine the temperature profiles of the component and PCB. The temperature profiles generated by the regression models are time-sequenced and in the same format as the CFD results. This approach considerably reduces the cost associated with training data, which is often a major challenge in the development of ML models.

This investigation applied a hybrid method combining a trained artificial neural network (ANN) and the sequential quadratic programming (SQP) method to determine an optimal parameter setting for a reflow soldering process of ball grid array packages in printed circuit boards.

Nine experiments based on an orthogonal array table with three‐controlled inputs and average shear forces of solder spheres as a quality target were utilized to train the ANN and then the SQP method was implemented to search for an optimal setting of parameters.

The ANN can be utilized successfully to predict the shear force under different reflow soldering conditions after being properly trained and the identified optimal parameter setting are capable of striking the balance between the average shear forces and the manufacturing cycle time.

The reflow time and the peak temperature were found to be the most significant factors for the reflow process via analysis of variance.

This study provided an algorithm integrating a black‐box modeling approach (i.e. the ANN predictive model) with the SQP method to resolve an optimization problem. This algorithm offered an effective and systematic way to identify an optimal setting of the reflow soldering process. Hence, the efficiency of designing the optimal parameters was greatly improved.

This paper aims to provide the proper preset temperatures of the convection reflow oven when reflowing a printed circuit board (PCB) assembly with varied sizes of components simultaneously.

In this study, computational fluid dynamics modeling is used to simulate the reflow soldering process. The training data provided to the machine learning (ML) model is generated from a programmed system based on the physics model. Support vector regression and an artificial neural network are used to validate the accuracy of ML models.

Integrated physical and ML models synergistically can accurately predict reflow profiles of solder joints and alleviate the expense of repeated trials. Using this system, the reflow oven temperature settings to achieve the desired reflow profile can be obtained at substantially reduced computation cost.

The prediction of the reflow profile subjected to varied temperature settings of the reflow oven is beneficial to process engineers when reflowing bulky components. The study of reflowing a new PCB assembly can be started at the early stage of board design with no need for a physical profiling board prototype.

This study provides a smart solution to determine the optimal preset temperatures of the reflow oven, which is usually relied on experience. The hybrid physics–ML model providing accurate prediction with the significantly reduced expense is used in this application for the first time.

Introduction: With the proliferation and amalgamation of technology and the emergence of artificial intelligence and the internet of things, society is now facing a rapid explosion in big data. However, this explosion needs to be handled with care. Ethically managing big data is of great importance. If left unmanageable, it can create a bubble of data waste and not help society achieve human well-being, sustainable economic growth, and development.

Purpose: This chapter aims to understand different perspectives of big data. One philosophy of big data is defined by its volume and versatility, with an annual increase of 40% per annum. The other view represents its capability in dealing with multiple global issues fuelling innovation. This chapter will also offer insight into various ways to deal with societal problems, provide solutions to achieve economic growth, and aid vulnerable sections via sustainable development goals (SDGs).

Methodology: This chapter attempts to lay out a review of literature related to big data. It examines the implication that the big data pool potentially influences ideas and policies to achieve SDGs. Also, different techniques associated with collecting big data and an assortment of significant data sources are analysed in the context of achieving sustainable economic development and growth.

Findings: This chapter presents a list of challenges linked with big data analytics in governance and achievement of SDG. Different ways to deal with the challenges in using big data will also be addressed.

China’s population is ageing. Continuing care retirement communities (CCRCs) are an emerging living arrangement of older Chinese. Incorporating social sustainability features into CCRCs helps to create age-friendly residential environments for residents. However, it is still unclear what kinds of social sustainability features are incorporated into the residential environments of CCRCs in China. Therefore, this study aims to address this research gap.

Qualitative content analysis is adopted to analyse the retrieved business information of representative CCRC developers in China.

This study revealed 36 social sustainability features in CCRCs, with the top-ranked ones being health care and management, social connection and engagement, high-quality and diverse services and daily life support and assistance. Additionally, a preliminary social sustainability framework of CCRCs was proposed, and this framework includes the five components of care and health, environment and management, service and facility, age-friendly life philosophy and social support and inclusion.

In theory, this research’s findings clarify the meaning of social sustainability within the context of CCRCs, which supports future relevant explorations in the CCRC research community. In practice, these findings enhance stakeholders’ understanding of the social sustainability in CCRCs, which promotes the development of age-friendly living environments for older people in an ageing society.

Modern thermal and non-thermal pretreatment techniques, namely, enzymatic treatment, gas phase plasma treatment and ohmic heating have become more pronounced over conventional techniques for enhanced coloured phytochemicals (pigments) extraction. Presently, numbers of pretreatment techniques are available with some unique feature. It is difficult to choose best pretreatment method to be employed for phytochemicals extraction from different sources. Therefore, this paper aims to discuss different modern pretreatment techniques for extraction with their potential results over conventional techniques.

Research and review articles targeting to the thermal and non-thermal pretreatment techniques were collected from Google Scholar. The required information has been tabulated and discussed which included qualities of modern pretreatment techniques over conventional techniques, phytochemical extraction and best pretreatment methods for optimized results.

Every pre-treatment has its own advantages and disadvantages for a particular phytochemical and its extraction from various sources. Enzymes can be used in combinations to enhance final yield like extraction of carotenoids (pectinase, cellulase and hemicellulase) from chillies and lycopene (pectinase and cellulase) from tomato. Utilization of each method depends upon many factors such as source of pigment, cost and energy consumption. CO₂ pretreatment gives good results for carotenoid extraction from algae sources. Ohmic heating can yield high anthocyanin content. Modifications in conventional blanching has reduced final waste and improvised the properties of pigment.

This study comprises collective information regarding modern pre-treatment for extraction over conventional pre-treatments. The study also covers future trends and certain new hybrid approaches which are still less flourished.

The purpose of this study is to examine the role of digital infrastructure in supporting compliance with travel restrictions. The purpose of this study is to examine the role of digital infrastructure in supporting compliance with travel restrictions. In response to the COVID-19 pandemic, countries around the world have issued “stay-at-home” orders and curtailed a variety of economic activities. As countries have adopted aggressive policies to limit the spread of COVID-19, varying levels of national infrastructure to provide internet access have limited some nations’ ability to reduce travel requirements. As national policies struggle to address public health issues, location analytics enabled by big data provide unique insights regarding the efficacy of digital infrastructure. These insights can provide valuable tools to public health officials and regulators in understanding how health recommendations are implemented within an economy.

This study analyzes mobile phone movement data during the first half of 2020 and finds that countries that provided greater access to internet capabilities were better able to reduce work-related mobility.

This study’s findings indicate that greater levels of digital infrastructure may better prepare countries to adapt to societal disruptions such as COVID-19.

This study’s findings demonstrate that public health controls regarding movement and person-to-person interaction are less likely to be effective in nations with weaker digital infrastructure, even after accounting for variation attributable to gross domestic product (GDP) and pandemic severity. This could limit public health options in developing countries when faced with future socially disruptive events and encourage national investment in digital infrastructure.

This study’s findings highlight positive externalities associated with reducing the digital divide. Developing better digital business infrastructure globally may reduce human exposure to future pandemic risks.

This research demonstrates the practical development implications of analysis of aggregate data widely available through mobile technology. As institutions develop techniques to ethically and effectively analyze this data, greater opportunities to support economic development may be revealed.