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This study aims to analyze the development direction of track geometry inspection equipment for high-speed comprehensive inspection train in China.

The development of track geometry inspection equipment for high-speed comprehensive inspection train in China in the past 20 years can be divided into 3 stages. Track geometry inspection equipment 1.0 is the stage of analog signal. At the stage 1.0, the first priority is to meet the China's railways basic needs of pre-operation joint debugging, safety assessment and daily dynamic inspection, maintenance and repair after operation. Track geometry inspection equipment 2.0 is the stage of digital signal. At the stage 2.0, it is important to improve stability and reliability of track geometry inspection equipment by upgrading the hardware sensors and improving software architecture. Track geometry inspection equipment 3.0 is the stage of lightweight. At the stage 3.0, miniaturization, low power consumption, self-running and green economy are co-developing on demand.

The ability of track geometry inspection equipment for high-speed comprehensive inspection train will be expanded. The dynamic inspection of track stiffness changes will be studied under loaded and unloaded conditions in response to the track local settlement, track plate detachment and cushion plate failure. The dynamic measurement method of rail surface slope and vertical curve radius will be proposed, to reveal the changes in railway profile parameters of high-speed railways and the relationship between railway profile, track irregularity and subsidence of subgrade and bridges. The 200 m cut-off wavelength of track regularity will be researched to adapt to the operating speed of 400 km/h.

The research can provide new connotations and requirements of track geometry inspection equipment for high-speed comprehensive inspection train in the new railway stage.

The purpose of this paper is to present a novel and effective fabricating method of 3D ceramic photonic crystals with diamond structure.

The reverse diamond‐structure resin molds are fabricated by stereolithography (SL), then ceramic slurry is prepared and injected into the molds under vacuum condition. Subsequently, ceramic photonic crystals are obtained after vacuum freeze‐drying and sintering.

The combination of SL, gel‐casting and freeze‐drying could be used to fabricate the 3D ceramic photonic crystals with diamond structure which have intact structure and minimal shrinkage. The samples have been tested and the experimental results indicate that their band gap is in the range of 10.14‐12.20 GHz, consistent with the simulation results.

The influence of fabrication process on the photonic band gap needs further study.

This paper presents a novel fabricating method of 3D diamond‐structure ceramic photonic crystals based on SL, gel‐casting and freeze‐drying. The method fabricates complex ceramic photonic crystals with high accuracy and helps further research in this field.

This research aims to examine the relationships between supply chain quality management (SCQM), organizational learning capability (OLC) and product innovation performance (PIP) among small and medium-sized manufacturing enterprises (SMMEs) in Malaysia.

This is a quantitative study in which 163 valid responses were empirically collected from SMMEs in Malaysia via self-administered structured questionnaires.

Performing a partial least squares–structural equation modelling analysis, the findings revealed that the relationships between SCQM, OLC and PIP are positive and significant. Moreover, OLC partially mediates the relationship between SCQM and product innovation. Serving as a practical guideline, the results of this study stress the fact that managers of SMMEs need to look into the role-specific context of the firm before determining which practices would be effective for their companies.

The value-added additional testing of the mediating effect of OLC is the highlight of this study. This research represents another leap towards redefining and advancing SCQM, especially for SMMEs in the Asian context.

In the previous decade, a substantial amount of research has been undertaken to measure the digitalized supply chain (DSC) performance. This paper aims to present a systematic literature review on DSC performance measurement metrics to apprehend current practices, recognize gaps and advocate future research itineraries.

To guarantee a replicable, rigorous and transparent research process, the authors used a systematic literature review methodology to synthesize the research. A combination of 25 keywords was used to obtain 248 scientific studies in the first step. The balance scorecard (BSC) model was used to categorize 299 gathered performance metrics into 4 BSC perspectives.

The review highlighted the need for qualitative performance measuring metrics for DSC. During the review, only one study was identified that primarily focused on developing performance metrics for DSC. Additionally, the review identified that metrics related to internal and financial perspectives received the most attention while the “growth and learning” perspective received the least attention. The review also identified that external partners, such as distributors and suppliers, were virtually ignored in previous literature.

Although numerous literature reviews have been conducted in the past on the performance measuring metrics for supply chain management, no literature review aiming to synthesize the measuring metrics for DSC has yet been undertaken.

This study aims to develop a polymer cement-based waterproof coating with self-healing capability to efficiently and intelligently solve the building leakage caused by cracking of waterproof materials, along with excellent durability to prolong its service life.

Ion chelators are introduced into the composite system based on ethylene vinyl acetate copolymer emulsion and ordinary Portland cement to prepare self-healing polymer cement-based waterproof coating. Hydration, microstructure, wettability, mechanical properties, durability, self-healing performance and self-healing products of polymer cement-based waterproof coating with ion chelator are investigated systematically. Meanwhile, the chemical composition of self-healing products in the crack was examined.

The results showed that ion chelators could motivate the hydration of C₂S and C₃S, as well as the formation of hydration products (C-S-H gel) of the waterproof coating to improve its compactness. Compared with the control group, the waterproof coating with ion chelator had more excellent water resistance, alkali resistance, thermal and UV aging resistance. When the dosage of ion chelator was 2%, after 28 days of curing, cracks with a width of 0.29 mm in waterproof coating could fully heal and cracks with a width of 0.50 mm could achieve a self-healing efficiency of 72%. Furthermore, the results reveal that the self-healing product in the crack was calcite crystalline CaCO₃.

A novel ion chelator was introduced into the composite coating system to endow it with excellent self-healing ability to prolong its service life. It has huge application potential in the field of building waterproofing.

Agricultural carbon productivity considers the dual goals of “agricultural economic growth” and “carbon emission reduction”. Improving agricultural carbon productivity is a requirement for promoting green and low-carbon development of agriculture. Agricultural production agglomeration is widespread worldwide, but the relationship between agricultural production agglomeration and agricultural carbon productivity is inconclusive. This paper aims to study the impact of agricultural production agglomeration on agricultural carbon productivity, which is conducive to a better understanding of the relationships among agglomeration, agricultural economic development and carbon emission, better planning of agricultural layout to build a modern agricultural industrial system and achieve the goal of carbon peaking and carbon neutrality.

Based on China's provincial data from 1991 to 2019, this paper uses non-radial directional distance function (NDDF) and Metafrontier Malmquist–Luenberger (MML) productivity index to measure total factor agricultural carbon productivity. Subsequently, using a panel two-way fixed effect model to study the effect and mechanism of agricultural production agglomeration on agricultural carbon productivity, and the two-stage least squares method (IV-2SLS) is used to solve endogeneity. Finally, this paper formulates a moderating effect model from the perspective of the efficiency of agricultural material capital inputs.

The empirical results identify that Chinese provincial agricultural carbon productivity has an overall growth trend and agricultural technological progress is the major source of growth. There is an inverted U-shaped relationship between agricultural production agglomeration and agricultural carbon productivity. The input efficiency of agricultural film, machine and water resources have moderating effects on the inverted U-shaped relationship. Agricultural production agglomeration also promotes agricultural carbon productivity by inhibiting agricultural carbon emissions in addition to affecting agricultural input factors and its internal mechanisms are agricultural green technology progress and rural human capital improvement.

This paper innovatively adopts the NDDF–MML method to measure the total factor agricultural carbon productivity more scientifically and accurately and solves the problems of ignoring group heterogeneity and the shortcomings of traditional productivity measurement in previous studies. This paper also explains the inverted U-shaped relationship between agricultural production agglomeration and agricultural carbon productivity theoretically and empirically. Furthermore, from the perspective of agricultural material capital input efficiency, this paper discusses the moderating effect of input efficiency of fertilizers, pesticides, agricultural film, agricultural machines and water resources on agricultural production agglomeration affecting agricultural carbon productivity and answers the mechanism of carbon emission reduction of agricultural production agglomeration.

This paper aims to analyze the influence of institutional pressures on Indian Food Supply Chain (FSC) actors’ intention to adopt strategies for Sustainable Development Goals (SDGs). By focusing on an agrarian state, this paper explores the prioritizations of SDGs by FSC actors and analyzes the relative impact of institutional pressures in adopting strategies for SDGs.

Quantitative data was collected using questionnaires from 303 respondents engaged in the food industry in an agrarian state in India.

The SDG prioritizations of FSC actors are evidenced using SDG models, thereby suggesting the presence of tradeoffs and synergies within SDGs in FSCs. By using institutional theory, this study defines the impact of sustainability drivers on Indian FSCs, and contrary to previous studies, normative institutional pressures are found not significant – this paper explicates the reasons for this.

Differing stakeholder groups and their prioritizations can result in ranking one SDG over another, thereby resulting in SDG tradeoffs. Such tradeoffs imply that the achievement of one SDG could negate the achievement of another SDG, and therefore, this study explicates the need for a holistic managerial approach to adopting SDGs.

Tech start-ups (TSs) functioning in different domains have a responsibility of ensuring that domestic knowledge and capabilities are leveraged to minimize dependence on foreign organizations. Despite the growth of the ecosystem, while numerous TSs emerge, very few of them are able to survive, and of those that survive, very few scale up. The aim of this study is to identify the factors influencing the competitiveness of technological start-ups and to study the interrelationship and interdependence of these factors.

Modified total interpretative structural modeling (m-TISM) was employed for the current research. The analysis of what factors have an effect on competitiveness, how they affect it and why they affect it should be explored. The study begins by developing the list of factors through literature search, and further it is validated by expert opinion. A hierarchical model has been developed using m-TISM and MICMAC analysis to analyze the driving and dependency power of factors at each level.

Results show that the competitiveness of TSs is affected by organizational agility and internationalization. Factors present at the bottom level, namely entrepreneurial intensity, act as a strong driver for TSs. Team member commitment, transformational leadership, strategic alliances, knowledge sharing and organizational ambidexterity are middle-level factors.

This study is among the few articles that have explored competitiveness of TSs in the Indian context.

The purpose of this paper is to identify and screen continuous innovation capability enablers (CICEs) in Indonesia’s manufacturing sectors, develop a relationship among these enablers and determine their driving power and dependence power in the sector.

The initial CICEs identification process is based on a literature review, while a fuzzy Delphi method (FDM) was used for the screening process of CICEs. Total interpretive structural modelling (TISM) was used to develop contextual relationships among various CICEs. The results of the TISM are used as an input for the matrix of cross-impact multiplications applied to classification (MICMAC) to classify the driving power and dependence powers of the CICEs.

This paper selected 16 CICEs classified in seven dimensions. TISM results and MICMAC analysis show that leadership, as well as climate and culture, are enablers with the highest driving power and lowest dependence powers; followed by information technology. The results of this study indicate that efforts to continuously develop innovation capabilities in the Indonesian manufacturing industries are strongly influenced by their leadership capability, climate and culture, also information technology-related capability.

The framework assessed in this study provides business managers and policymakers to obtain a bigger picture in developing policies with evidence-based strategy and priority in regard to continuous innovation capability.

The results will be useful for business managers and policymakers to understand the relationship between CICEs and identify key CICEs in Indonesia’s manufacturing sectors, which were previously non-existent.

The purpose of this paper is to identify and analyse the interactions among various enablers which are critical to the success of big data initiatives in operations and supply chain management (OSCM).

Fourteen enablers of big data in OSCM have been selected from literature and consequent deliberations with experts from industry. Three different multi criteria decision-making (MCDM) techniques, namely, interpretive structural modeling (ISM), fuzzy total interpretive structural modeling (fuzzy-TISM) and decision-making trial and evaluation laboratory (DEMATEL) have been used to identify driving enablers. Further, common enablers from each technique, their hierarchies and inter-relationships have been established.

The enabler modelings using ISM, Fuzzy-TISM and DEMATEL shows that the top management commitment, financial support for big data initiatives, big data/data science skills, organizational structure and change management program are the most influential/driving enablers. Across all three different techniques, these five different enablers has been identified as the most promising ones to implement big data in OSCM. On the other hand, interpretability of analysis, big data quality management, data capture and storage and data security and privacy have been commonly identified across all three different modeling techniques as the most dependent big data enablers for OSCM.

The MCDM models of big data enablers have been formulated based on the inputs from few domain experts and may not reflect the opinion of whole practitioners community.

The findings enable the decision makers to appropriately choose the desired and drop undesired enablers in implementing the big data initiatives to improve the performance of OSCM. The most common driving big data enablers can be given high priority over others and can significantly enhance the performance of OSCM.

MCDM-based hierarchical models and causal diagram for big data enablers depicting contextual inter-relationships has been proposed which is a new effort for implementation of big data in OSCM.