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There is an increasing demand for higher-accuracy dimensional measurements of nano- and micro-structures. Recently, the authors presented a fiber Bragg grating (FBG) sensor-based dynamic nano-coordinate-measuring machine (CMM) probe for true three-dimensional coordinate measurement, in which a specific mechanical structure with several FBG sensors was developed to provide the probe with sensitivity to loading in all directions.

The study presents a three-dimensional sensing and demodulation system based on an improved matched filter design and the time division multiplexing technique that helps solve the problem of multiplex FBG-signals conflicts. In addition, the application of the dynamic mode of the probe system effectively solves the problem presented by the surface interaction forces.

Consequently, this FBG-based vibrating probe system has increased sensitivity to strain, while maintaining smaller contact force. The experiments for testing probe performance show that the prototype yielded a measurement resolution of 13 nm, a repeatability of 50 nm and a vertical measurement force of less than1.5 mN.

The force tests in the horizontal directions are difficult to conduct because both the probe and the dynamometer are only adaptable to vertical use.

Development of the FBG-based dynamic nano-coordinate-measuring machine probe will achieve a new and inexpensive method for higher-accuracy dimensional measurements of nano- and micro-structures, such as micro-electromechanical systems, micro-fluidic chips, inkjet and diesel engine injector nozzles that are in overall dimensions within the micrometer scale.

The study presents a three-dimensional sensing and demodulation system for the vibrating nano-coordinate-measuring machine probe based on FBG sensors. The prototype yielded a measurement resolution of 13 nm, a repeatability of 50 nm and a vertical measurement force of less than1.5 mN.

The frictional behavior of a Mo alloy‐coated piston ring sliding against cast iron cylinder bore was recorded as a function of temperature using a reciprocating tribotester and a fully formulated synthetic engine oil, with and without a friction modifier. It was observed that, as temperature increased in a stepwise mode, friction coefficients in the presence of MoDTC exhibited two local minimal values. Only one minimal friction coefficient value at 340–355 °C (μ = 0.065) was observed in the absence of MoDTC. Chemical characterization of worn tracks of the cylinder bore using reflected FTIR spectroscopy, Raman spectroscopy and ESCA, indicated that both the base stock and the tribological additives, ZDTP and MoDTC, experienced tribochemical reactions yielding MoO₃, MoS₂ and carbonaceous species as temperature ramped up stepwise. MoO₃ and MoS₂ reaction film formation are partially responsible for the local minimal friction coefficient found at the lower temperature and the ratio of the ordered carbon species accounts partially for the other local minimal friction coefficient found at the higher temperature.

In gearbox fault diagnosis, identifying the fault type and severity simultaneously, as well as the compound fault containing multiple faults, is necessary.

To diagnose multiple faults simultaneously, this paper proposes a multichannel and multi-task convolutional neural network (MCMT-CNN) model.

Experiments were conducted on a bearing dataset containing different fault types and severities and a gearbox compound fault dataset. The experimental results show that MCMT-CNN can effectively extract features of different tasks from vibration signals, with a diagnosis accuracy of more than 97%.

Vibration signals at different positions and in different directions are taken as the MC inputs to ensure the integrity of the fault features. Fault labels are established to retain and distinguish the unique features of different tasks. In MCMT-CNN, multiple task branches can connect and share all neurons in the hidden layer, thus enabling multiple tasks to share information.

This paper aims to guide the implementation of noise reduction measures in hoistway and reduce the aerodynamic noise generated by elevator operation, this paper aims to propose an aerodynamic noise analysis method that can solve the flow field in hoistway.

A turbulence-acoustic model solving the flow field in a hoistway and a numerical wind hoistway model of the ultra-high-speed elevator were established by using large eddy simulation (LES) and Curle acoustic theory.

The characteristics of pulsating flow field and aerodynamic noise around ultra-high-speed elevator are analyzed. The asymmetric characteristics of the flow field could be observed using the turbulent kinetic energy and the instantaneous vortexes in the wind hoistway model. Vortex shedding, air flow separation and recombination around the car were the key factors for aerodynamic noise generation. The sound pressure level was approximately linear to the logarithm of car speed. The increase of car deflection angle in a certain range would reduce the peak frequency of wake noise and increase the sound pressure level (SPL) value.

This paper provides important guidance for researches studying the aerodynamic noise in the hoistway and the technical personnel that look for the reduction measures, which greatly improves the shortcomings in the numerical simulation of the aerodynamic noise of the hoistway.

Facility location decisions are critical and should be taken after strategic evaluations. Globalization and integration of economies make such decisions further complex and risk prone. The purpose of this paper is to identify and assess the risk factors to be considered while taking new facility location decision associated with global supply chain and device the methodology. A grey-based multi-criteria decision-making (MCDM) approach is used for this purpose, which also takes in to account the uncertainty in decision making. Such approach enables final decision to be more real and practical. The paper also highlighted and discussed the criteria on the basis of which the management can select the best suitable site.

The risk factors related to facility location for a global firm are identified. To select the location of a global facility with least risk, grey-based MCDM approach is formulated. This grey-based MCDM is demonstrated using the hypothetical case of an industrial valve manufacturing global firm. The grey approach is used to analyse location alternatives based on various decision criteria for extracting comparative ranking.

The paper presents a tool for strategic and planning level. It helps supply chain managers to identify the risks related to a candidate location. Then it guides the supply chain manager at strategic level to find the least risky location for a manufacturing facility.

This paper demonstrates the grey-based MCDM approach for determining less risky location to locate a new manufacturing unit so that practitioners can use this approach for taking other strategic decisions. The supply chain configuration can be decided subsequently which will yield more practical results and the decision taken will be more fruitful for firm.

The extensive literature review reveals that there are many models in the literature that addressed the issue of risk minimization in supply chain, but it was also noticed that there are limited number of models that minimize risk in locating a global facility considering the uncertainty of data in decision making. This is the first time that grey-based MCDM approach is formulated and used to find most suitable facility location under risk.

The paper aims to evaluate how progressive stakeholders view the adoption of contemporary techniques such as virtual technology in driving sustainable quality in an emerging economy context.

The authors adopted a systematic literature review to develop the theoretical framework for virtual reality (VR) technology adoption in sustaining quality in agriculture production. The framework was refined after discussion with a panel of academic experts. The refined theoretical framework was further empirically validated using Partial Least Square Structure Equation Modelling.

The study focuses on the future perspective of the perception for progressive farming with the adoption of VR technology in an emerging economy. The data were collected from the stakeholders (farmers, collectives, cooperative, etc.), for their future perspectives for the adoption of VR technology and sustainable quality agriculture production. The study may help build up VR technology in emerging economies which may take years to be established.

The perception of the future perspective of VR technology study conducted has limitations. The findings are well established on technology adoption; however, the technology used will take many extra years to find its application in the agriculture sector. The study offers insightful theoretical, managerial and policy implications for sustainable quality in agriculture production through the adoption of virtual reality (VR) technology. The authors found very few works that focused on VR technology adoption.

The study discusses VR, which has an impact on sustaining the quality of agriculture production. The study has notable managerial and policy implications that suggest the future perspective for VR technology in agriculture production. The study is an unexplored area that needs research to capture future perspectives.

Safety and productivity are key concerns in the construction projects. While safety looks to the construction workers need to work in a safe environment, productivity affects the project’s profitability and is of a paramount importance from the project owner’s view. The different perspective to the safety and productivity from these two major players in construction projects poses a potential for the conflict between the two. This problem can be fundamentally addressed by methods concurrently improving project safety and productivity. The paper aims to discuss this issue.

To this aim, a discrete event simulation (DES) based framework applicable was proposed for complex and hazardous operations. The utility of the framework was tested using a case study of an eight-story residential building in the north-east part of Tehran, Iran. The excavation and stabilization operation was identified as the most hazardous and critical operation in this case. The framework could improve safety and productivity of this operation by 38 and 4 percent, respectively.

This framework is a complement to the conventional construction project safety and productivity planning methods. Its main application is in complex and hazardous construction operations.

For the first time, a comprehensive framework for concurrently improving safety and productivity of an entire project was proposed in this research. DES was used as the main modeling tool in the framework to provide an ex-ante evaluation foundation applicable to a wide range of construction projects.

Unlike traditional industries, the e-cigarette is an epoch-making innovative product originating in China and occupying an absolute competitive advantage in the international market. The traditional A-U model describes the laws and characteristics of technological innovation in developed countries. In contrast, the inverse A-U model depicts the process of “secondary innovation” in late-developing countries through digestion and absorption. This paper aims to find out that if the e-cigarette, as a “first innovation” industry in a late-developing country, conform to the A-U model or conform to the “inverse A-U model”.

This paper takes the patent data of e-cigarettes from 2004 to 2021 as the research object, and uses Python’s Jieba segment words to divide product innovation and process innovation, and then uses statistical analysis methods to conduct empirical analyses on these data.

Thus, an improved A-U model suitable for the e-cigarette industry is proposed. In this model, product innovation in the e-cigarette industry appeared earlier than process innovation, but the synchronous development of product and process innovation is not lagging. The improved A-U model in the e-cigarette industry is not only different from the traditional A-U model but also does not conform to the inverse A-U model.

It is conducive to expanding and clarifying the theoretical contribution and applicable boundaries of the A-U model and has sparked thinking and exploration of the A-U model in e-cigarettes and emerging industries.

On this basis, suggestions on the development path and countermeasures of the e-cigarette industry are put forward.

Based on the e-cigarette industry, this paper takes patents as the research object and provides the method of dividing product innovation and process innovation, and proposes an A-U model suitable for the e-cigarette industry on this basis. By comparing the traditional A-U model with the inverse A-U model in latecomer countries, the background and causes of e-cigarette A-U model heterogeneity are analyzed from different stages and overall morphology. Based on this, the heterogeneity characteristics of e-cigarette innovation are summarized and sorted out.