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To ensure the stable operation of ironmaking process and the quality and output of sinter, the multi-objective optimization of sintering machine batching process was carried out.

The purpose of this study is to establish a multi-objective optimization model with iron taste content and batch cost as targets, constrained by field process requirements and sinter quality standards, and to propose an improved balance optimizer algorithm (LILCEO) based on a lens imaging anti-learning mechanism and a population redundancy error correction mechanism. In this method, the lens imaging inverse learning strategy is introduced to initialize the population, improve the population diversity in the early iteration period, avoid falling into local optimal in the late iteration period and improve the population redundancy error correction mechanism to accelerate the convergence rate in the early iteration period.

By selecting nine standard test functions of BT series for simulation experiments, and comparing with NSGA-?, MOEAD, EO, LMOCSO, NMPSO and other mainstream optimization algorithms, the experimental results verify the superior performance of the improved algorithm. The results show that the algorithm can effectively reduce the cost of sintering ingredients while ensuring the iron taste of sinter, which is of great significance for the comprehensive utilization and quality assurance of sinter iron ore resources.

An optimization model with dual objectives of TFe content and raw material cost was developed taking into account the chemical composition and quality indicators required by the blast furnace as well as factors such as raw material inventory and cost constraints. This model was used to adjust and optimize the sintering raw material ratio. Addressing the limitations of existing optimization algorithms for sintering raw materials including low convergence accuracy slow speed limited initial solution production and difficulty in practical application we proposed the LILCEO algorithm. Comparative tests with NSGA-III MOEAD EO LMOCSO and NMPSO algorithms demonstrated the superiority of the proposed algorithm. Practical applications showed that the proposed method effectively overcomes many limitations of the current manual raw material ratio model providing scientific and stable decision-making guidance for sintering production operations.

As technology aspect of agriculture becomes more and more important with the time to increase agricultural productivity in a sustainable and smart way, agriculture practices become more interdisciplinary. Furthermore, agricultural practices are affected by urban and rural planning enabling urban and rural farming. Architecture, engineering and construction (AEC) industry can support food security through the integration of agricultural practices and technologies into the built environment, its interior design, and greenhouses supporting urban and rural farming. Based on the literature review, this chapter aims to investigate ways for enhancing AEC industry’s and its professionals’ contribution to food security and sustainable agricultural practices. This chapter highlights roles of the AEC industry in enhancing food security and sustainable agricultural practices. This chapter emphasizes the importance of undergraduate and graduate curriculums of future AEC industry professionals (e.g., architects, interior architects, civil engineers) to equip them with the skills and knowledge of sustainable agricultural practices and technologies integrated greenhouses, built environment and indoor environment, and interior design. For this reason, agricultural policies need to cover food security-related interdisciplinary education and training (e.g., renewable energy-based agriculture integrated built environment) of AEC industry professionals. Agricultural policies need to be designed with the contribution of and considering AEC industry professionals as they are among the main stakeholders of food security and renewable energy-based agriculture-integrated built environment. Furthermore, this chapter highlights how AEC industry, in compliance with United Nations Sustainable Development Goals and countries sustainable and resilient development plans, can contribute to food security and sustainability. This chapter can be beneficial to all stakeholders of the sustainable agricultural practices.

Green and digital transitions (twin transition) help in achieving climate neutrality, cutting down the pollution level and reviving biodiversity. Organizations can mitigate their ecological footprint, improve their digital portfolio and fulfil their ethical responsibilities by fostering a sustainable global community and adhering to a well-defined plan. In such backdrop, this study aims to propose strategies by identifying the strengths, weaknesses, opportunities and threats of the twin transition and to categorize and evaluate the internal and external factors for achieving environmental sustainability.

Following a comprehensive review of the literature, 27 factors have been identified and analyzed using the strengths, weakness, opportunities and threat matrix (SWOT). Threats, opportunities, weakness, strength matrix analysis approach is used to limit risk, overcome weaknesses and take advantage of opportunities. Effective strategies are designed and implemented using strategic planning action and evaluation matrix (SPACE) and quantitative strategic planning (QSPM) approach.

The study found that aggressive strategies considering economic strength and global opportunities must be used to accomplish green growth objectives. Out of three strategies, the focus on green technological innovation by taking advantage of financial mechanisms at the international level is highly recommended to achieve environmental sustainability.

Through promoting green and digital transformation, the current study provides insightful policy recommendations for economic development. It may help to improve operational efficacy and to obtain a competitive edge by successfully limiting their adverse environmental effects in the global market through the implementation of twin transition. The process of developing a strategy aids stakeholders, including society, workers and other parties, in giving environmental sustainability a top priority while making financial decisions.

Strategy development helps society, employees and all the other stakeholders to prioritize their green and digital transition decisions for environmental sustainability.

The current study identifies the factors formulating strategies using SWOT, TOWS, SPACE and QSPM matrix analysis for achieving environmental sustainability in the pervasive twin transition.

The aim of this study is to examine the relationships between taking charge, bootlegging innovation and innovative job performance, and to explore the moderating roles of felt responsibility for constructive change (FRCC) and creative self-efficacy (CSE).

Data for this research was collected from 503 employees working in a chain company. Through a longitudinal study design, a three-wave survey with 397 valid data provided support for the proposed theoretical model.

The results maintain a positive association between taking charge, bootlegging innovation and innovative job performance, indicating the mediating effect of bootlegging innovation. Additionally, both the FRCC and CSE facilitate the indirect effect of taking charge on innovative job performance through bootlegging innovation. Furthermore, the integrated moderated mediation model analysis suggested that FRCC is more vital in improving employees' innovative job performance.

This research aims to break the black box between taking charge and innovative job performance, which has been relatively unexplored. Drawing from self-determination theory (SDT) and the proactive motivation model, the authors verify the bridge-building role of bootlegging innovation and the dual-facilitating effects of FRCC and CSE while employees conduct taking charge. This study’s results provide new insight for managers to foster, encourage and support employees' proactive behavior.

In the brain imaging based on electrical impedance tomography, it is sometimes not able to attach 16 electrodes due to space restriction caused by craniotomy. As a result of this, the number of boundary measurements decreases, and spatial resolution of reconstructed conductivity distribution is reduced. The purpose of this study is to enhance reconstruction quality in cases of limited measurement.

A new data expansion method based on the shallow convolutional neural network is proposed. An eight-electrode model is built from which fewer boundary measurements can be obtained. To improve the imaging quality, shallow convolutional neural network is constructed which maps limited voltage data of the 8-electrode model to expanded voltage data of a quasi-16-electrode model. The predicted data is compared with the quasi-16-electrode data. Besides, image reconstruction based on L1 regularization method is conducted.

The results show that the predicted data generally coincides with the quasi-16-electrode data. It is found that images reconstructed with the data of eight-electrode model are the poorest. Nevertheless, imaging results when the limited data is expanded by the proposed method show large improvement, and there is a minor difference with the images recovered with the quasi-16-electrode data. Also, the impact of noise is studied, which shows that the proposed method is robust to noise.

To enhance reconstruction quality in the case of limited measurement, a new data expansion method based on the shallow convolutional neural network is proposed. Both simulation work and phantom experiments have demonstrated that high-quality images of cerebral hemorrhage and cerebral ischemia can be obtained when the limited measurement is expanded by the proposed method.

Emergencies have become a growing concern for organizations, which require flexibility to respond to changes in emergencies based on their contingency, dynamic evolution rapidly and other characteristics. In order to enhance the ability of engineering project organizations to cope with emergencies, this study explores the mechanism of its influence on knowledge innovation under emergencies from the perspective of bricolage theory, and provides a new perspective for the traditional preplanning-based handling of emergencies by improvising to enhance the ability and results of improvisation.

Firstly, a structural equation model of the relationship between bricolage and knowledge innovation was constructed by introducing improvisational behavior and serendipity as mediating and moderating variables of the relationship between bricolage and knowledge innovation based on bricolage theory; secondly, drawing on previous well-established measurement scales about bricolage, improvisational behavior, knowledge innovation and serendipity, a questionnaire survey was conducted with different types of engineering project managers and technicians in Gansu Province as the research subjects, and 238 valid questionnaires were returned; finally, validation factor analysis and correlation analysis were performed, and the hypothesized relationships were verified using AMOS 24.0 software.

The results show that bricolage positively influences improvisational behavior; improvisational behavior positively influences knowledge innovation; bricolage positively influences knowledge innovation; bricolage influences knowledge innovation through the mediating role of improvisational behavior and serendipity positively moderates the impact of resource bricolage on knowledge innovation.

It reveals the mechanism of knowledge innovation of engineering project organizations in response to emergencies and the innovation mechanism of the episodic nature of emergency decision-making, extends the applicable context of bricolage theory and provides a new perspective for engineering project organizations in response to emergencies.

This study aims to assess the effect of both apigenin-loaded zinc oxide nanoparticles (ZnONPs) and apigenin only against cisplatin (CP)-induced experimental cardiotoxicity.

A total of 32Wister rats (male) were randomly divided (n = 8) into four groups. Normal control group, CP group received CP (20 mg/kg); treated group I received CP and then received apigenin (0.78 mg/kg/day) orally; treated group II received CP and then received apigenin loaded ZnONPs. At the end of the experiment (10 days), samples were extracted from each rat for the assessment of complete blood picture, lipid profile, atherogenic indices, oxidative status, inflammatory and cardiotoxicity markers as well as histological examination.

The results indicated that CP produced significant alterations in the complete blood picture, lipidemic profile, atherogenic indices, antioxidation capacity and cardiac inflammatory markers as well as function enzymes as compared with the control group. Administration of apigenin only showed a non-significant change in the atherogenic indices, oxidative status and cardiotoxicity parameters, indicating incomplete cardio-protection against CP upon. Additionally, all the observed alterations in CP group were reversed when apigenin nanoparticle at lower dose was used with ZnONPs which was also confirmed by histopathological investigation.

The apigenin loaded ZnONPs exert protective effects against CP-induced experimental cardiotoxicity and improved cardiac function, suggesting a potential adjuvant role of apigenin nanoparticles against cardiotoxicity.

This paper aims to describe a case study of a formative study for the development of a social marketing campaign for increased biowaste sorting. In following the social marketing principles, it provides insights for practitioners willing to implement behaviour change interventions.

The case study describes the scoping review, expert interviews, online survey and focus groups, which were integrated to comprehend the specificities of biowaste sorting in the South Suburbs of Athens. This mixed-method research design enabled a comprehensive understanding of biowaste sorting practices that would not have been attainable through a single method.

This study found that the “descriptive norm” and “perceived controllability” significantly influenced biowaste sorting. Differential challenges of at-home and out-of-home sorting were also identified. The demand for more information was also highlighted. The tailored intervention comprises of a mix of behavioural modelling, persuasive communication, education and enablement.

By concretely showcasing how the integration of multiple research methods through the application of social marketing principles can guide the design of a tailored behaviour change intervention, this paper offers an outline for informed decision-making and strategic planning in the realm of (environmental) behaviour change.

This paper aims to offer a comprehensive review and categorization of production optimization throughout the additive manufacturing lifecycle in a cloud environment. It aims to provide a structured approach to identifying and addressing issues.

This paper systematically reviews 75 technical papers on cloud manufacturing, nesting, scheduling and postprocessing in additive manufacturing. This includes a detailed discussion of the key issues.

This paper introduces a production framework for the entire lifecycle of additive manufacturing in a cloud environment. This framework aids in problem identification and decision-making based on the process flow. It provides an integrated view from cloud to postprocessing, examining decision interdependencies and enhancing problem identification and organization.

To the best of the authors’ knowledge, this paper is the first to review the complete lifecycle of additive manufacturing, emphasizing the often-overlooked aspects of postprocessing and cloud manufacturing. It offers a comprehensive study of lifecycle optimization challenges and suggests ways to streamline the production process.

This chapter explores the symbiotic relationship between digitalization and sustainability in the context of Industry 4.0. Examining key technologies such as blockchain, artificial intelligence (AI), and the Internet of Things (IoT), this chapter unveils their transformative impact on industries, emphasizing the role of data-driven decision-making, supply chain transparency, and circular economy principles. Real-world case studies illustrate successful implementations, showcasing how organizations leverage digital twins, blockchain for supply chain transparency, and extended reality for sustainable training. The regulatory landscape emerges as a crucial factor, shaping the adoption of digital technologies for sustainability, while emerging trends like 5G, edge computing, and AI promise to redefine the future. As a conclusion, policymakers are urged to strike a balance between innovation and regulation, fostering an environment conducive to responsible digital practices. Industries are encouraged to embrace emerging trends, and researchers are invited to explore the synergies between 5G, edge computing, and AI for holistic sustainability solutions. Together, these efforts aim to propel Industry 4.0 toward a resilient and sustainable future.