Search results

International climate politics are gradually changing in terms of new and ground-breaking policies and decision-making spearheaded by national governments. The growing global demand to combat climate change reflects the current challenges the world is facing. India’s negotiations at United Nations Conference on Climate Change are based on “equity,” “historical responsibility” and the “polluter pays” agenda, until a shift in the voluntary reduction of carbon emissions takes place. The purpose of this study is to understand why India, a “deal breaker”, is seen as a “deal maker” in climate governance?

For a state like India, domestic preferences are equally important in introducing climate policies alongside its concerns over poverty reduction and economic development, which also stand with its sustainable development goals. This paper explains India’s decision-making using a two-level approach focusing on “domestic preferences.” This rationale is based on India’s historical background as well as new upcoming challenges.

This paper shows that India has both the domestic needs and long-term benefits of combating climate change to cut carbon emissions, which gives the responsibility primarily to domestic audiences and international societies.

This paper uses an international political lens to critically analyze India’s climate positions and politics from both domestic and international levels, demonstrating the importance of considering both short- and long-term goals. The outcome benefits not only the policymakers in India but also stakeholders in the Asia-Pacific and beyond.

The purpose of this paper is to investigate and quantify the impact of product complexity, including architectural complexity, on operator learning, productivity and quality performance in both assembly and disassembly operations. This topic has not been extensively investigated in previous research.

An extensive experimental campaign involving 84 operators was conducted to repeatedly assemble and disassemble six different products of varying complexity to construct productivity and quality learning curves. Data from the experiment were analysed using statistical methods.

The human learning factor of productivity increases superlinearly with the increasing architectural complexity of products, i.e. from centralised to distributed architectures, both in assembly and disassembly, regardless of the level of overall product complexity. On the other hand, the human learning factor of quality performance decreases superlinearly as the architectural complexity of products increases. The intrinsic characteristics of product architecture are the reasons for this difference in learning factor.

The results of the study suggest that considering product complexity, particularly architectural complexity, in the design and planning of manufacturing processes can optimise operator learning, productivity and quality performance, and inform decisions about improving manufacturing operations.

While previous research has focussed on the effects of complexity on process time and defect generation, this study is amongst the first to investigate and quantify the effects of product complexity, including architectural complexity, on operator learning using an extensive experimental campaign.

This work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.

The proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.

The proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.

Thanks to the abnormal risk panel; human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.

The monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products’ waste avoidance.

Poor quality in building projects is high and increasing. Poor quality can increase the cost of a building by up to more than 50% and can delay a project by up to 50%. This research investigated the poor quality of building elements/components.

The site operatives were requested to rate the frequency of poor quality in 25 building elements/components. The frequencies of the poor quality were scored on a five-point Likert scale, ranging from least often to extremely often. The survey forms were administered to construction site operatives by hand delivery.

The data revealed that poor quality occurred in more than 80% of the building projects completed. Approximately 40% of the cost of a building project is attributed to poor quality. In total, 70% of the respondents measured the poor quality of building elements as being high and frequent. The size and frequency of poor quality are higher in concrete, plaster, brick, foundations and roof trusses.

The research findings would help to reduce claims, disputes, maintenance costs and waste on sites.

This research provides fresh information on poor quality in building projects and provides a systemic process for anticipating poor quality in building projects. The findings also provide an option to increase maintenance span and a means to reduce claims and disputes in the construction sector.