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This paper aims to gain a greater insight into the molecular level changes occurring in lac‐epoxy blends, at elevated temperatures for different intervals of time. It is also to determine the changes in key physico‐chemical parameters of these blends, before and after applying thermal stress on these blends.

Films of lac‐epoxy blends, applied on tin panels, were baked at 200°C for different time intervals. The baked films were examined by specular reflectance spectroscopy, as these were otherwise difficult to examine through conventional IR techniques, using KBr pellet method. The results obtained were compared and reported. The blends were also tested for different physico‐chemical parameters like scratch hardness, flexibility, adhesion, acid resistance, etc.

On baking the blends of lac‐epoxy resin at 200°C for different time intervals, the strained three membered epoxy ring apparently break open, releasing some free hydroxyl groups, which react with carboxylic groups in lac resin to form ester linkages, resulting in condensation. In terms of different physico‐chemical parameters, blends of 70 per cent lac:30 per cent epoxy were found to be better in performance than blends of 50 per cent lac:50 per cent epoxy and blends of 30 per cent lac:70 per cent epoxy resins, baked at 200°C for different time intervals. Adhesion and acid resistance of lac‐epoxy blends were very good compared with the parent resins.

The spectral studies on lac‐epoxy blends were done qualitatively. Although desirable, quantitative studies could not be carried out, due to the inherent difficulties in handling the resins.

Lac‐epoxy blends may find an important role in printing inks, varnishes and finishing industries owing to their very good adhesion, gloss, scratch hardness and resistance to the action of acids.

As of now, there is no record of specular‐reflectance and derivative spectrometric studies on lac‐synthetic resin blends. This paper represents the first attempt to obtain and correlate reflectance data with physico‐chemical changes. It also highlights the convenience of the method and the scope of sophisticated data analysis, including derivative spectrometry and spectral subtraction techniques.

Behavioral pattern mining for intelligent system such as SmEs sensor data are vitally important in many applications and performance optimizations. Sensor pattern mining (SPM) is also dynamic and a hot research issue to pervasive and ubiquitous of smart technologies toward improving human life. However, in large-scale sensor data, exploring and mining pattern, which leads to detect the abnormal behavior is challenging. The paper aims to discuss these issues.

Sensor data are complex and multivariate, for example, which data captured by the sensors, how it is precise, what properties are recorded or measured, are important research issues. Therefore, the method, the authors proposed Sequential Data Mining (SDM) approach to explore pattern behaviors toward detecting abnormal patterns for smart space fault diagnosis and performance optimization in the intelligent world. Sensor data types, modeling, descriptions and SPM techniques are discussed in depth using real sensor data sets.

The outcome of the paper is measured as introducing a novel idea how SDM technique’s scale-up to sensor data pattern mining. In the paper, the approach and technicality of the sensor data pattern analyzed, and finally the pattern behaviors detected or segmented as normal and abnormal patterns.

The paper is focussed on sensor data behavioral patterns for fault diagnosis and performance optimizations. It is other ways of knowledge extraction from the anomaly of sensor data (observation records), which is pertinent to adopt in many intelligent systems applications, including safety and security, efficiency, and other advantages as the consideration of the real-world problems.

This paper presents a conceptual framework based on an extensive literature review. The aim of this study is to deepen understanding of the relationship between carbon performance and the financial market by applying qualitative research approaches.

The investigation has identified 372 articles sourced from Scopus databases, subjecting the bibliographic data to a comprehensive qualitative–quantitative analysis. The research uses established protocols for a structured literature review, adhering to PRISMA guidelines, machine learning-based structural topic modelling using Python and bibliometric citation analysis.

The results identified the leading academic authors, institutions and countries concerning carbon performance and financial markets literature. Quantitative studies dominate this research theme. The study has identified six knowledge clusters using topic modelling related to environmental reporting; price drivers of carbon markets; environmental policy and capital markets; financial development and carbon emissions; carbon risk and financial markets; and environmental performance and firm value. The results of the study also present the opportunities associated with carbon performance and the financial market and propose future research agendas on research through theory, characteristics, context and methodology.

The results of the study offer insights to practitioners, researchers and academicians regarding scientific development, intricate relationships and the complexities involved in the intersection of carbon performance and financial markets. For policymakers, a better understanding of carbon performance and financial markets will contribute to designing policies to set up priorities for countering carbon emissions.

The study highlights the critical areas that require attention to limit greenhouse gas emissions and promote decarbonisation effectively. Policymakers can leverage these insights to develop targeted and evidence-based policies that facilitate the transition to a more sustainable and low-carbon economy.

The study initially attempts to discuss the research stream on carbon performance and financial markets literature from a systematic literature review.

The paper proposes a framework for the successful deployment of Industry 4.0 (I4.0) principles in the aerospace industry, based on identified success factors. The paper challenges the perception of I4.0 being aligned with de-skilling and personnel reduction and instead promotes a route to successful deployment centred on upskilling and retaining personnel for future role requirements.

The research methodology involved a literature review and industrial data collection via questionnaires to develop and validate the framework. The questionnaire was sent to a purposive sample of 50 respondents working in operations, and a response rate of 90% was achieved. Content analysis was used to identify patterns, themes, or biases, and the data were tabulated based on specific common attributes. The proposed framework consists of a series of gates and criteria that must be met before progressing to the next gate.

The proposed framework provides a feedback mechanism to review minimum standards for successful deployment, aligned with new developments in capability and technology, and ensures quality assessment at each gate. The paper highlights the potential benefits of I4.0 implementation in the aerospace industry, including reducing operational costs and improving competitiveness by eliminating variation in manufacturing processes. The identified success factors were used to define the framework, and the identified failure points were used to form mitigation actions or controls for inclusion in the framework.

The paper provides a framework for the successful deployment of I4.0 principles in the aerospace industry, based on identified success factors. The framework challenges the perception of I4.0 as being aligned with de-skilling and personnel reduction and instead promotes a route to successful deployment centred on upskilling and retaining personnel for future role requirements. The framework can be used as a guideline for organizations to deploy I4.0 principles successfully and improve competitiveness.

While rapid increase in demand for foods but limited availability of croplands has forced to adopt input-intensive farming practices to increase yield, there are serious long-term ecological implications including degradation of biodiversity. It is increasingly recognised that ensuring agricultural sustainability under the changing climatic conditions requires a change in the production system along with necessary policies and institutional arrangements. In this context, this chapter examines if climate-smart agriculture (CSA) can facilitate adaptation and mitigation practices by improving resource utilisation efficiency in India. Such an attempt has special significance as the existing studies have very limited discussions on three main aspects, viz., resource productivity, adaptation practices and mitigation strategies in a comprehensive manner. Based on insights from the existing studies, this chapter points out that CSA can potentially make significant contribution to enhancing resource productivity, adaptation practices, mitigation strategies and food security, especially among the land-constrained farmers who are highly prone to environmental shocks. In this connection, staggered trench irrigation structure has facilitated rainwater harvesting, local irrigation and livelihood generation in West Bengal. However, it is necessary to revisit the existing approaches to promotion of CSA and dissemination of information on the design of local adaptation strategies. This chapter also proposes a change in the food system from climate-sensitive to CSA through integration of technologies, institutions and policies.

Idea of circular economy defies the classical “make-use-dispose” approach of linear economic model. In the context of health-care industry, it relies heavily on the supply chain practices implemented by industry stakeholders. The purpose of this study is to explore such relationships, study their structure and put it across for attaining sustainability at large.

This study is an empirical research conducted on 145 health-care firms. The collected data is analysed to develop structural and measurement model. The five constructed hypotheses are examined and tested through structural equation modelling.

The study illustrates the latent relationships that exist among the stakeholders involvement, sustainable supply chain practices, sustainable performance and circular economy for health-care industry. It is found that the adoption of sustainable supply chain practices improves health-care performance, which, in turn, have positive influence on circular economy.

The structural and measurement model is developed in the context of circular health-care economy. It can be validated or improvised by conducting similar research in other industry using different methods. This research work fulfils the long existing gap in research by offering a linkage between various constructs to achieve health-care circular economy. Based on the research results, future researchers can build theories of circular economy and sustainability for health-care industry.

The study attempts to study the supply chain ways to achieve circular economy for Indian health-care sector. It considered latent relationships among the set of constructs, which are needed for theory building at later stage.