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In recent years, industrial firms have been moving from selling pure products to selling smart services. Yet limited empirical evidence exists about how the new markets for these novel services are created. This paper seeks to extend current theory and create new insights by studying the new services market creation process in nascent industrial fields.

The authors' research design is a multiple‐case, inductive study that uses in‐depth archival and field data to track closely how five industrial firms created new market for new types of services.

The authors find that firms adopt a holistic initiative to address the challenges in new services market creation. In particular, they use three interrelated strategies to create a new market: co‐creating with customers, innovating in different ways and exploiting institutional forces.

The study focused only on life science research services. Moreover, in‐depth field interviews were used only in a small number of firms.

To successfully develop a new market for an industrial service innovation, a firm should innovate within and outside the firm, win over customers for adopting, adapting the service innovation and identifying its new uses, and utilize institutional mechanisms to legitimatize, claim and control the emerging market.

This paper's central contribution is a holistic framework of the longitudinal processes by which successful firms develop new services and construct new markets.

Dimensional quality of sheet metal assemblies is an important factor for the final product. However, the part tolerance is not easily controlled because of the spring back deformation during the stamping process. Selective assembly is a means to decrease assembly tolerance of the assembly from low-precision components. Therefore, the purpose of this paper is to propose a fully efficient method of selective assembly optimization based on an improved genetic algorithm for optimization toolbox (IGAOT) in MATLAB.

The method of influence coefficient is first applied to calculate the assembly variation of sheet metal components since the traditional rigid assembly variation model cannot be used due to welding deformation. Afterwards, the IGAOT is proposed to generate optimal selective groups, which consists of advantages of genetic algorithm for optimization toolbox (GAOT) and simulated annealing.

The cases of two simple planes and the tail lamp bracket assembly are used to illustrate the flowchart of optimizing combinations of selective groups. These cases prove that the proposed IGAOT has better precision than that of GAOT with the same parameters for selective assembly.

The research objective of this paper is to evaluate the changes from rigid bodies to sheet metal parts which are very complex for selective assembly. The method of IGAOT was proposed to the selected groups which has better precision than that of current optimization algorithms.

The purpose of this paper is to propose a new assembly variation analysis model to analyze assembly variation for sheet metal parts. The main focus is to analyze assembly processes based on the method of power balance.

Starting with issues in assembly variation analysis, the review shows the critical aspects of tolerance analysis. The method of influence coefficient (MIC) cannot accurately analyze the relationship between part variations and assembly variations, as the welding point is not a point but a small area. Therefore, new sensitivity matrices are generated based on the method of power balance.

Here two cases illustrate the processes of assembly variation analysis, and the results indicate that new method has higher accuracy than the MIC.

This study is limited to assembly variation analysis for sheet metal parts, which can be used in auto-body and airplane body.

This paper provides a new assembly variation analysis based on the method of power balance.

Functional disorders caused by food intolerance (FI) are prevalent, thus it is important to analyze the FI of healthy people to common foods so as to guide the people for eating the healthy foods. The paper aims to discuss this issue.

In total, 88,436 healthy persons including 60,902 males and 27,534 females at the age ranging from 20 to 70 years old were subjected a normal physical examination. In total, 14 kinds of food-specific IgG antibodies were detected by enzyme-linked immunesorbent assay.

The total positive rate of 14 FIs was as high as 64.16 percent. Five kinds of foods (egg, crab, cod, shrimp and milk) accounted for 84.51 percent of the total positive rate. In more than one kind of FIs, egg took the largest proportion than the others and the proportion was 58.54 percent. The second was crab with a positive rate of 56.19 percent. The antibody positive rate of any food in one kind of FIs was significantly lower than that in more than one kind of FIs (χ²=629.35, p<0.001). Also, younger age subjects displayed the higher positive rate than the older age groups. In addition, there was no significant difference on FI between male and female subjects.

The results would not only prompt us to pay more attention to FI in daily life, but provide theoretical foundation for the early prevention, diagnosis and treatment of related clinical diseases as well as guiding people healthy meals.

The paper aims to study the effect of sintering temperature on the microstructure, shear strength and ratcheting fatigue life of nanosilver sintered lap shear joint. In addition, the Gerber model is used to predict the ratcheting fatigue lives of nanosilver sintered lap shear joints at different sintering temperatures.

In this paper, the nanosilver sintered lap shear joints were prepared at three sintering temperatures of 250 °C, 280 °C and 310 °C. The bonding quality was characterized by scanning electron microscopy, X-ray diffraction, transmission electron microscope and shear tests, and the long-term reliability was studied by conducting ratcheting fatigue tests. In addition, three modified models based on Basquin equation were used to predict the ratcheting fatigue life of nanosilver sintered lap shear joint and their accuracies were evaluated.

When the sintering temperature is 250°C, the nanosilver sintered lap shear joint shows the porosity of 22.9 ± 1.6 %, and the shear strength of 22.3 ± 2.4 MPa. Raising the sintering temperature enhances silver crystallite size, strengthens sintering necks, thus improves shear strength and ratcheting fatigue life in joints. In addition, the ratcheting fatigue lives of the joints sintered at different temperatures are effectively predicted by three equivalent force models, and the Gerber model shows the highest life prediction accuracy.

The sintered silver bondline is suffering a complex stress state. The study only takes the shear stress into consideration. The tensile stress and the combination of shear stress and tensile stress can to be considered in the future study.

The paper provides the experimental and theoretical support for robust bonding and long-term reliability of sintered silver structure.

The introduced model can predict the ratcheting fatigue lives of the joints sintered at different temperatures, which shows a potential in engineering applications.

The study revealed the relationship between the sintering temperature and the microstructure, the shear strength and the ratcheting fatigue life of the joint. In addition, the Gerber model can predict the ratcheting fatigue life accurately at different sintering temperatures.

To achieve sustainable development in shipping, accurately identifying the impact of artificial intelligence on shipping carbon emissions and predicting these emissions is of utmost importance.

A multivariable discrete grey prediction model (WFTDGM) based on weakening buffering operator is established. Furthermore, the optimal nonlinear parameters are determined by Grey Wolf optimization algorithm to improve the prediction performance, enhancing the model’s predictive performance. Subsequently, global data on artificial intelligence and shipping carbon emissions are employed to validate the effectiveness of our new model and chosen algorithm.

To demonstrate the applicability and robustness of the new model in predicting marine shipping carbon emissions, the new model is used to forecast global marine shipping carbon emissions. Additionally, a comparative analysis is conducted with five other models. The empirical findings indicate that the WFTDGM (1, N) model outperforms other comparative models in overall efficacy, with MAPE for both the training and test sets being less than 4%, specifically at 0.299% and 3.489% respectively. Furthermore, the out-of-sample forecasting results suggest an upward trajectory in global shipping carbon emissions over the subsequent four years. Currently, the application of artificial intelligence in mitigating shipping-related carbon emissions has not achieved the desired inhibitory impact.

This research not only deepens understanding of the mechanisms through which artificial intelligence influences shipping carbon emissions but also provides a scientific basis for developing effective emission reduction strategies in the shipping industry, thereby contributing significantly to green shipping and global carbon reduction efforts.

The multi-variable discrete grey prediction model developed in this paper effectively mitigates abnormal fluctuations in time series, serving as a valuable reference for promoting global green and low-carbon transitions and sustainable economic development. Furthermore, based on the findings of this paper, a grey prediction model with even higher predictive performance can be constructed by integrating it with other algorithms.

This paper aims to use nano-silver paste to design a new bonding method for super-large-area direct-bonded-aluminum (DBA) plates. It compared several frequently used bonding methods and proved the feasibility of an optimized low-pressure-assisted double-layer-printed silver sintering technology for large-area bonding to increase the thermal conductivity of power electronic modules with high junction temperature, higher power density and higher reliability.

The bonding profile was optimized by using transparent glasses as substrates. Thus, the bonding qualities could be directly characterized by optical observation. After sintering, the bonded DBA samples were characterized by nondestructive X-ray computed tomography system, scanning electron microscopy equipped with an energy dispersive spectrometer. Finally, bonding stress evolution was characterized by shear tests.

Low-pressure-assisted large-area double-layer-printed bonding process consisting of six-step was successfully developed to bond DBA substrates with the size of 50.8 × 25.4 mm. The thickness of the sintered-silver bond-line was between 33  and 74 µm with the average porosity of 12.5 per cent. The distribution of shear strength along the length of DBA/DBA bonded sample was from 9.7  to 18.8 MPa, with average shear strength of 15.5 MPa. The typical fracture primarily propagated in the sintered-silver layer and partially along the Ni layer.

The bonding stress needs to be further improved. Meanwhile, the thermal and electrical properties are encouraged to test further.

If nano-silver paste can be used as thermal interfacial material for super-large-area bonding, the thermal performance will be improved.

The paper accelerated the use of nano-silver paste for super-large-area DBA bonding.

The proposed bonding method greatly decreased the bonding pressure.

Most green building (GB) materials, which are used widely in the construction sector in Malaysia, perform poorly in terms of energy efficiency and sustainability. Nevertheless, during maintenance planning of these materials, the focus is often directed towards comfort and design instead. However, as GB material construction projects grow in scale and complexity, interconnections between the activities and processes can be noticed during problematic planning performance management to monitor the GB material components for corrective and preventive maintenance actions.

The concept of GB material maintenance planning for sustainable development and the main features of information and communication technology tools and techniques are based on analysis of literature reviews of GB material scenarios.

The results show how decision-making support in maintenance planning can be unsuccessful and how planning decisions can frame the content of an integrated system to analyse information and reduce risks of GB material failure.

The paper concludes that implementing a research framework for developing such a system can help improve the sustainable performance of maintenance planning of GB material economic, social and environmental issues.

Green building (GB) maintenance is increasingly accepted in the construction industry, so it can now be interpreted as an industry best practice for maintenance planning. However, the performance competency and design knowledge of the practice's building control instrument process can be affected by its evaluation and the information management of building information modelling (BIM)–based model checking (BMC). These maintenance-planning problems have not yet been investigated in instances such as the Grenfell Tower fire (14 June 2017, approximately 80 fatalities) in North Kensington, West London.

This study proposes a theoretical framework for analysing the existing conceptualisation of BIM tools and techniques based on a critical review of GB maintenance environments. These are currently employed on GB maintenance ecosystems embedded in project teams that can affect BMC practices in the automation system process. In order to better understand how BMC is implemented in GB ecosystem projects, a quantitative case study is conducted in the Malaysian public works department (Jabatan Kerja Raya (JKR)).

GB ecosystem projects were not as effective as planned due to safety awareness, design planning, inadequate track insulation, environmental (in) compatibility and inadequate building access management. Descriptive statistics and an ANOVA were applied to analyse the data. The study is reinforced by a process flow, which is transformed into a theoretical framework.

Industry practitioners can use the developed framework to diagnose BMC application issues and leverage the staff competency inherent in an ecosystem to plan GB maintenance environments successfully.

This paper aims to identify the different system approach using building information modelling (BIM) technology that is equipped with automated evaluation processes. BIM research has mainly focused on theoretical models of acceptance in the green building (GB) maintenance industry. However, BIM has the potential to the competency’s performance and design knowledge of building control instrument. Realising this potential requires a study of BIM at the maintenance planning level, which is considered to be BIM-based model checking (BMC). BMC and its effect in the maintenance planning have not been sufficiently investigated.

The aim of this paper is to present a critical review of literature on the theoretical background of BMC practices and the main features of information and communication technology tools and techniques in the GB maintenance projects.

A theoretical framework of BMC is developed and presented. The proposed model incorporates requirement for maintaining a competency’s performance on maintenance planning schemes of GB projects and the importance of early integration of BMC in the design phase to identify alternative methods to cogenerate, monitor and optimise BMC.

It is found that variables facilitating BMC are integrated at different GB maintenance environments levels and are shaped by the context. Directions for future research are presented.