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Handbook of research on applications of AI, digital twin, and internet of things for sustainable development
\"The increasing interest in Artificial Intelligence (AI), Digital Twin (DT) and Internet of Things (IoT) is progressively playing a wider impact on many different sectors, and and this book offers several applications and challenges in various vital roles such as Product design and lifecycle, Smart Cities, Agriculture, Environment automation, Healthcare, Farming, Wearable, Climate, Sensors, Transportation, Electrical generation, E-governance renewable energy, and eco-system for sustainable growth\"-- Provided by publisher.
BOOK
Bolstering green supply chain integration via big data analytics capability: the moderating role of data-driven decision culture
PurposeBased on organizational information processing theory, this research explores how big data analytics capability (BDAC) contributes to green supply chain integration (GSCI) and the contingency role that data-driven decision culture plays.Design/methodology/approachUsing the two-wave survey data collected from 317 Chinese manufacturing firms, the authors validate the hypotheses.FindingsThe results show that big data managerial capability has positive impacts on three dimensions of GSCI, while big data technical capability has positive impacts on green internal and customer integration. Moreover, green internal integration mediates the impacts of big data technical capability and managerial capability on green supplier and customer integration. Finally, data-driven decision culture alleviates the positive impacts of big data technical and managerial capability on green internal integration.Practical implicationsThe findings suggest that firms can leverage big data technical and managerial capability to enhance information processing capability for achieving a higher degree of GSCI. Further, the critical role of data-driven decision culture in affecting the link between BDAC and GSCI should not be overlooked.Originality/valueThis research contributes to literature on green supply chain management by revealing the role of BDAC in improving GSCI.
Journal Article
Green and sustainable AI research: an integrated thematic and topic modeling analysis
This investigation delves into Green AI and Sustainable AI literature through a dual-analytical approach, combining thematic analysis with BERTopic modeling to reveal both broad thematic clusters and nuanced emerging topics. It identifies three major thematic clusters: (1) Responsible AI for Sustainable Development, focusing on integrating sustainability and ethics within AI technologies; (2) Advancements in Green AI for Energy Optimization, centering on energy efficiency; and (3) Big Data-Driven Computational Advances, emphasizing AI’s influence on socio-economic and environmental aspects. Concurrently, BERTopic modeling uncovers five emerging topics: Ethical Eco-Intelligence, Sustainable Neural Computing, Ethical Healthcare Intelligence, AI Learning Quest, and Cognitive AI Innovation, indicating a trend toward embedding ethical and sustainability considerations into AI research. The study reveals novel intersections between Sustainable and Ethical AI and Green Computing, indicating significant research trends and identifying Ethical Healthcare Intelligence and AI Learning Quest as evolving areas within AI’s socio-economic and societal impacts. The study advocates for a unified approach to innovation in AI, promoting environmental sustainability and ethical integrity to foster responsible AI development. This aligns with the Sustainable Development Goals, emphasizing the need for ecological balance, societal welfare, and responsible innovation. This refined focus underscores the critical need for integrating ethical and environmental considerations into the AI development lifecycle, offering insights for future research directions and policy interventions.
Journal Article
Prediction of crime occurrence from multi-modal data using deep learning
In recent years, various studies have been conducted on the prediction of crime occurrences. This predictive capability is intended to assist in crime prevention by facilitating effective implementation of police patrols. Previous studies have used data from multiple domains such as demographics, economics, and education. Their prediction models treat data from different domains equally. These methods have problems in crime occurrence prediction, such as difficulty in discovering highly nonlinear relationships, redundancies, and dependencies between multiple datasets. In order to enhance crime prediction models, we consider environmental context information, such as broken windows theory and crime prevention through environmental design. In this paper, we propose a feature-level data fusion method with environmental context based on a deep neural network (DNN). Our dataset consists of data collected from various online databases of crime statistics, demographic and meteorological data, and images in Chicago, Illinois. Prior to generating training data, we select crime-related data by conducting statistical analyses. Finally, we train our DNN, which consists of the following four kinds of layers: spatial, temporal, environmental context, and joint feature representation layers. Coupled with crucial data extracted from various domains, our fusion DNN is a product of an efficient decision-making process that statistically analyzes data redundancy. Experimental performance results show that our DNN model is more accurate in predicting crime occurrence than other prediction models.
Journal Article
Deep grading of mangoes using Convolutional Neural Network and Computer Vision
The grading of mangoes is an essential aspect of providing quality fruits to consumers and control the needs of the fruit processing industry. Manual visual inspection leads to inconsistencies, and it is human labour intensive. This paper is focused on improving the accuracy of the automatic mango grading system by doing multi-level grading using Deep Learning, Computer Vision and Image processing techniques. The proposed system is based on the mango maturity ripening stage, shape, texture features, colour and defects to identify the mango variety and classify based on quality. The maturity ripening stage of the mango is extracted using the Convolutional Neural Network (CNN). Computer Vision and Image processing techniques are used to extract shape, texture features and defects. The extracted features are input to the Random Forest classifier to identify the mango variety and grade the mango quality into three classes Notfit, Average and Good. The system has been validated on the dataset created for this study across three different varieties, Banganapalli, Neelam and Rumani, the most popular in Tamil Nadu. The proposed system using features extracted from CNN enhanced the system's efficiency with an accuracy of 93.23% for variety recognition and 95.11% for quality grading. Hence the proposed system is fully automated, commercially viable and has improved accuracy in variety recognition and quality grading of mangoes across different varieties.
Journal Article
How to leverage manufacturing digitalization for green process innovation: an information processing perspective
PurposeThe aim of this study was to examine how manufacturing digitalization can be leveraged to promote green innovation in the digital era by investigating the effects of manufacturing digitalization on green process innovation, and thus firm performance. The authors also explored how the role of manufacturing digitalization varies with horizontal information sharing, vertical bottom-up learning and technological modularization.Design/methodology/approachFive hypotheses were examined by performing regression analyses on survey data from 334 manufacturing firms in China.FindingsManufacturing digitalization positively affects green process innovation, and thus firm performance. Furthermore, this positive effect is strengthened by horizontal information sharing and technological modularization and weakened by vertical bottom-up learning.Originality/valueThis study extends the literature rooted in the natural-resource-based view by identifying the crucial role of green process innovation and investigating the value of manufacturing digitalization for developing green capabilities in the digital era. It also contributes to this line of research by revealing contingent factors to leverage manufacturing digitalization from the information processing perspective. Furthermore, this study extends information processing theory to the digital context and identifies the interaction of organizational design (vertical bottom-up learning and horizontal information sharing) and digital investment (manufacturing digitalization).
Journal Article
Internet of Nonthermal Food Processing Technologies (IoNTP): Food Industry 4.0 and Sustainability
With the introduction of Industry 4.0, and smart factories accordingly, there are new opportunities to implement elements of industry 4.0 in nonthermal processing. Moreover, with application of Internet of things (IoT), smart control of the process, big data optimization, as well as sustainable production and monitoring, there is a new era of Internet of nonthermal food processing technologies (IoNTP). Nonthermal technologies include high power ultrasound, pulsed electric fields, high voltage electrical discharge, high pressure processing, UV-LED, pulsed light, e-beam, and advanced thermal food processing techniques include microwave processing, ohmic heating and high-pressure homogenization. The aim of this review was to bring in front necessity to evaluate possibilities of implementing smart sensors, artificial intelligence (AI), big data, additive technologies with nonthermal technologies, with the possibility to create smart factories together with strong emphasis on sustainability. This paper brings an overview on digitalization, IoT, additive technologies (3D printing), cloud data storage and smart sensors including two SWOT analysis associated with IoNTPs and sustainability. It is of high importance to perform life cycle assessment (LCA), to quantify (En)—environmental dimension; (So)—social dimension and (Ec)—economic dimension. SWOT analysis showed: potential for energy saving during food processing; optimized overall environmental performance; lower manufacturing cost; development of eco-friendly products; higher level of health and safety during food processing and better work condition for workers. Nonthermal and advanced thermal technologies can be applied also as sustainable techniques working in line with the sustainable development goals (SDGs) and Agenda 2030 issued by United Nations (UN).
Journal Article
Crop stress detection from UAVs: best practices and lessons learned for exploiting sensor synergies
IntroductionDetecting and monitoring crop stress is crucial for ensuring sufficient and sustainable crop production. Recent advancements in unoccupied aerial vehicle (UAV) technology provide a promising approach to map key crop traits indicative of stress. While using single optical sensors mounted on UAVs could be sufficient to monitor crop status in a general sense, implementing multiple sensors that cover various spectral optical domains allow for a more precise characterization of the interactions between crops and biotic or abiotic stressors. Given the novelty of synergistic sensor technology for crop stress detection, standardized procedures outlining their optimal use are currently lacking.Materials and methodsThis study explores the key aspects of acquiring high-quality multi-sensor data, including the importance of mission planning, sensor characteristics, and ancillary data. It also details essential data pre-processing steps like atmospheric correction and highlights best practices for data fusion and quality control.ResultsSuccessful multi-sensor data acquisition depends on optimal timing, appropriate sensor calibration, and the use of ancillary data such as ground control points and weather station information. When fusing different sensor data it should be conducted at the level of physical units, with quality flags used to exclude unstable or biased measurements. The paper highlights the importance of using checklists, considering illumination conditions and conducting test flights for the detection of potential pitfalls.ConclusionMulti-sensor campaigns require careful planning not to jeopardise the success of the campaigns. This paper provides practical information on how to combine different UAV-mounted optical sensors and discuss the proven scientific practices for image data acquisition and post-processing in the context of crop stress monitoring.
Journal Article
Conflict translates environmental and social risk into business costs
Sustainability science has grown as a field of inquiry, but has said little about the role of large-scale private sector actors in socio-ecological systems change. However, the shaping of global trends and transitions depends greatly on the private sector and its development impact. Market-based and command-and-control policy instruments have, along with corporate citizenship, been the predominant means for bringing sustainable development priorities into private sector decision-making. This research identifies conflict as a further means through which environmental and social risks are translated into business costs and decision making. Through in-depth interviews with finance, legal, and sustainability professionals in the extractive industries, and empirical case analysis of 50 projects worldwide, this research reports on the financial value at stake when conflict erupts with local communities. Over the past decade, high commodity prices have fueled the expansion of mining and hydrocarbon extraction. These developments profoundly transform environments, communities, and economies, and frequently generate social conflict. Our analysis shows that mining and hydrocarbon companies fail to factor in the full scale of the costs of conflict. For example, as a result of conflict, a major, world-class mining project with capital expenditure of between US$3 and US$5 billion was reported to suffer roughly US$20 million per week of delayed production in net present value terms. Clear analysis of the costs of conflict provides sustainability professionals with a strengthened basis to influence corporate decision making, particularly when linked to corporate values. Perverse outcomes of overemphasizing a cost analysis are also discussed.
Journal Article
Reviewing and Integrating AEC Practices into Industry 6.0: Strategies for Smart and Sustainable Future-Built Environments
This article explores the possible ramifications of incorporating ideas from AEC Industry 6.0 into the design and construction of intelligent, environmentally friendly, and long-lasting structures. This statement highlights the need to shift away from the current methods seen in the AEC Industry 5.0 to effectively respond to the increasing requirement for creative and environmentally sustainable infrastructures. Modern building techniques have been made more efficient and long-lasting because of AEC Industry 6.0’s cutting-edge equipment, cutting-edge digitalization, and ecologically concerned methods. The academic community has thoroughly dissected the many benefits of AEC Industry 5.0. Examples are increased stakeholder involvement, automation, robotics for optimization, decision structures based on data, and careful resource management. However, the difficulties of implementing AEC Industry 6.0 principles are laid bare in this research. It calls for skilled experts who are current on the latest technologies, coordinate the technical expertise of many stakeholders, orchestrate interoperable standards, and strengthen cybersecurity procedures. This study evaluates how well the principles of Industry 6.0 can create smart, long-lasting, and ecologically sound structures. The goal is to specify how these ideas may revolutionize the building industry. In addition, this research provides an in-depth analysis of how the AEC industry might best adopt AEC Industry 6.0, underscoring the sector-wide significance of this paradigm change. This study thoroughly analyzes AEC Industry 6.0 about big data analytics, the IoT, and collaborative robotics. To better understand the potential and potential pitfalls of incorporating AEC Industry 6.0 principles into the construction of buildings, this study examines the interaction between organizational dynamics, human actors, and robotic systems.
Journal Article