Decision-making Process Research Articles

Cracking the silent gallstone code: Wait or operate?

The widespread availability of abdominal ultrasound has revealed the common occurrence of asymptomatic gallstones. While the treatment for symptomatic gallstones is clear, the benefits of minimally invasive laparoscopic cholecystectomy have sparked debate about the best approach to managing silent gallstones. The potential for asymptomatic gallstones to become symptomatic or lead to complications complicates the decision-making process regarding surgical intervention, as it's uncertain when or which patients might develop complications. Consequently, risk stratification appears to play a critical role in guiding decisions about silent gallstones. However, there is no definitive evidence to direct management, and a consensus-based on high-quality evidence is yet to be established.

Optimization of energy use efficiency and environmental assessment in soybean and peanut farming using the imperialist competitive algorithm

This study proposes using input-output analysis to improve energy use efficiency and assess environmental indicators in soybean and peanut farming. The research findings show that soybean production consumes 43170.20 MJ ha−1 of total energy and yields 40417.28 MJ ha−1 of output energy, while peanut production requires 28677.36 MJ ha−1 of total energy and produces 28677.36 MJ ha−1 of output energy. The LCA results indicate that on-farm emissions are associated with production inputs for both soybean and peanut systems, with diesel fuel and chemical fertilizers being the main contributors. Soybean farming shows higher levels of diesel fuel pollutants due to reduced usage, while peanuts have a higher prevalence of contaminants associated with diesel fuel. Using ICA to calculate optimized energy consumption results in 36672.77 MJ ha−1 for soybean production and 23485.45 MJ ha−1 for peanut production, leading to energy savings of 6075.33 MJ ha−1 and 5191.91 MJ ha−1, respectively. Additionally, the environmental impact category quantities for ICA analysis of soybean and peanut production reveal the resources per ton for soybean production to be 111.25 USD2013 and for peanut production to be 45.43 USD2013 in terms of energy use. The use of ICA demonstrates potential in maximizing energy efficiency and evaluating environmental indicators in soybean and peanut farming. By optimizing resource allocation and decision-making processes, ICA can help farmers reduce energy consumption and minimize environmental impact, ultimately leading to more sustainable and eco-friendly farming practices that benefit both the environment and the agricultural industry. Further research and implementation of ICA in farming practices have the potential to significantly improve energy efficiency and environmental sustainability in the soybean and peanut farming industry.

A novel method of detecting malware on Android mobile devices with explainable artificial intelligence

The increasing prevalence of malware targeting android mobile devices has raised significant concerns regarding user privacy and security. In response, effective methods for malware classification and detection are crucial to protect users from malicious applications. This paper presents an approach that leverages deep learning techniques and explainable artificial intelligence (XAI) for android mobile malware classification and detection. Convolutional neural networks (CNNs) are deep learning model that has shown impressive performance in several application areas, including image and text classification. In the context of android mobile malware, CNNs have shown promising results in capturing intricate patterns and features inherent in malware samples. By training these models on large datasets of benign and malicious applications, accurate classification can be achieved. To enhance transparency and interpretability, XAI techniques are integrated into the classification process. These techniques provide insights into the decision-making process of the deep learning models, enabling the identification of critical features and characteristics that contribute to the classification results. This research, by combining deep learning and XAI methods, presents a fresh strategy for identifying and categorizing Android malware. This research paper will focus on a fascinating CNN-based malware categorization technique.

The role of chat generative pre-trained transformer in facilitating decision-making and the e-learning process in higher education

Digital technology and artificial intelligence technologies have been progressing rapidly, thus giving rise to intelligent chatbots such as chat generative pre-trained transformer (ChatGPT). These chatbots make searching for information more efficient and provide higher education institutions with assistance in decision-making. The goal of this research is to explore the capabilities of ChatGPT technology and its role in enhancing the e-learning process. Moreover, it seeks to determine whether ChatGPT can provide useful suggestions to improve the decision-making process in higher education. ChatGPT is effective in an e-learning environment for the following reasons: it facilitates personalized learning experiences, offers real-time support, and enhances decision-making by leveraging natural language processing capabilities. As suggested by the findings, ChatGPT has significant potential in higher education, as demonstrated by its ability to improve interactive participation, educational strategies, and educational outcomes. This study highlights the importance of incorporating ChatGPT into higher education settings to improve e-learning and decision-making.

Bibliometric analysis highlighting the role of women in addressing climate change challenges and achieving sustainable development goals for greener future

Fossil fuel consumption increased quickly, contributing to climate change that is evident in unusual flooding and draughts, and global warming. Over the past ten years, women's involvement in society has grown dramatically, and they succeeded in playing a noticeable role in reducing climate change. A bibliometric analysis of data from the last ten years has been carried out to examine the role of women in addressing the climate change. The analysis's findings discussed the relevant to the sustainable development goals (SDGs), particularly SDG 7 and SDG 13. The results considered contributions made by women in the various sectors while taking geographic dispersion into account. The bibliometric analysis delves into topics including women's leadership in environmental groups, their involvement in policymaking, their contributions to sustainable development projects, and the influence of gender diversity on attempts to mitigate climate change. This study's results highlight how women have influenced policies and actions related to climate change, point out areas of research deficiency and recommendations on how to increase role of the women in addressing the climate change and achieving sustainability. To achieve more successful results, this initiative aims to highlight the significance of gender equality and encourage inclusivity in climate change decision-making processes.

Optimizing launch window opportunities for ESA's comet Interceptor mission using primer vector theory

Comet Interceptor (Comet-I) was selected in June 2019 as the first ESA F-Class mission and aims to explore a pristine comet, which will visit the inner Solar System for the first time. Comet-I will hitch a ride to a Sun-Earth L2 quasi-halo orbit, as a co-passenger in ESA's M4 ARIEL's launch. At the time of writing, this is scheduled for 2029. It will then remain there awaiting the right departure conditions to leave definitively and intercept a newly discovered comet. Comet-I will be the first mission to be designed and launched without an already identified target. Given the latter, the Comet-I Target Identification Working Group is tasked with the continuous follow-up of newly discovered comets, to analyse their characteristics as virtual targets for Comet-I. The goal of the paper is to inform the decision-making process that will eventually be necessary to commit Comet-I spacecraft to its final target.Within this context, this paper presents a trajectory optimization process based on Primer Vector theory that considers intercept trajectories with multiple revolutions and multiple deep space manoeuvres (DSM). The process is applied to 21 long period comets, which were discovered since July 1,1st July 2020 and whose characteristics are of interest for the Comet-I mission. In fact, one of the latest of these recently discovered comets, C/2023 H2, is the most accessible virtual target discovered at the time of writing, with delta-V intercept costs well within Comet-I capabilities. A complete launch window analysis is thus presented for C/2023 H2, which accounts not only for the most optimal delta-V intercept transfer, but also considers all launch options that satisfy all Comet-I's current boundary conditions and flyby constraints. A systematic launch window analysis is made possible by a grid search approach on which departure and fly-by dates are fixed and the remaining design variables, including the times for manoeuvres and their Δv impulse vector, are optimized via Primer Vector theory. This allows to understand the latest possible departure date, but also the sensitivity to departure and arrival dates of key encounter characteristics such as relative fly-by speed, the solar aspect angle and the Earth distance.

Artificial intelligence and internet of things in manufacturing decision processes

This paper explores the influence of the internet of things (IoT) and artificial intelligence (AI) on the decision-making processes of modern manufacturing systems. With the proliferation of IoT devices and the development of AI technologies, manufacturing companies increasingly leverage these technologies to improve their decision-making abilities. This study aims to investigate the potential benefits, difficulties, and ramifications of integrating IoT and AI in manufacturing systems. The review employs the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method with a systematic mapping process with four research questions. A total of 1282 articles were collected between 2017 and 2023, reviewed in accordance with the inclusion and exclusion criteria, and 66 articles were chosen. The research on IoT and AI technologies influentially affects other research in the production control layer manufacturing area based on the top-ten cited articles. In contrast, the research in this area focused on the operations management layer, specifically manufacturing analytics processes. This paper’s findings contribute to a greater understanding of the impact of IoT and AI on decision-making in modern multi-domain manufacturing systems and provide direction for future research in this field.

Understanding the factors that influence CT utilization for mild traumatic brain injury in a low resource setting - a qualitative study using the Theoretical Domains Framework

IntroductionIn low resource settings (LRS), utilization of Computed Tomography scan (CTS) for mild traumatic brain injuries (mTBIs) presents unique challenges and considerations given the limited infrastructure, financial resources, and trained personnel. The Theoretical Domains Framework (TDF) offers a comprehensive theoretical lens to explore factors influencing the decision-making to order CTS for mTBI by imaging referrers (IRs). ObjectivesThe primary objective was to explore IRs’ beliefs about factors influencing CT utilization in mTBIs using TDF in Uganda.Differences in the factors influencing CTS ordering behavior across specialties, levels of experience, and hospital category were also explored. Materials and MethodsIn-depth semi-structured interviews guided by TDF were conducted among purposively selected IRs from 6 tertiary public and private hospitals with functional CTS services. A thematic analysis was performed with codes and emerging themes developed based on the TDF. ResultsEleven IRs including medical officers, non-neurosurgeon specialists and neurosurgeons aged on average 42 years (SD+/-12.3 years) participated.Identified factors within skills domain involved IRs’ clinical assessment and decision-making abilities, while beliefs about capabilities and consequences encompassed their confidence in diagnostic abilities and perceptions of CTS risks and benefits. The environmental context and resources domain addressed the availability of CT scanners and financial constraints. The knowledge domain elicited IRs’ understanding of clinical guidelines and evidence-based practices while social influences considered peer influence and institutional culture. For memory, attention & decision processes domain, IRs adherence to guidelines and intentions to order CT scans were cited. ConclusionUsing TDF, IRs identified several factors believed to influence decision making to order CTS in mTBI in a LRS. The findings can inform stakeholders to develop targeted strategies and evidence-based interventions to optimize CT utilization in mTBI such as; educational programs, workflow modifications, decision support tools, and infrastructure improvements, among others.

Title- Digital Ad-fluence: Unravelling the Influence of Online Ads on Consumer Inclinations

Abstract: This dissertation investigates the influence of online advertising on consumer inclinations and purchasing behaviour in the digital age. As marketing efforts increasingly shift toward digital platforms, understanding the complex dynamics between online advertisements and consumer decision-making processes has become crucial. The study aims to unravel the intricate relationship between these two elements, providing insights into how online ads shape consumer preferences, attitudes, and purchasing decisions. The research employs a mixed-method approach, combining quantitative data from a survey of 100 respondents with a qualitative review of relevant literature spanning the past 10-20 years. The survey data offers valuable insights into consumers' perceptions, attitudes, and experiences with online advertising, while the literature review provides a comprehensive understanding of the theoretical underpinnings and empirical findings from prior research. Through factor analysis, the study identifies three key dimensions that collectively explain a significant portion of the variance in consumer behaviour toward online advertisements: the influence of price discounts, satisfaction and engagement levels, and the visual appeal of advertisements. These findings shed light on the critical factors that drive consumer responses to digital marketing campaigns. The dissertation contributes to the growing body of knowledge in digital marketing and consumer behaviour, offering practical implications for businesses and marketers seeking to navigate the ever-evolving landscape of online advertising. By uncovering the underlying mechanisms through which online ads influence consumer inclinations, this research provides valuable insights for developing effective digital marketing strategies that resonate with target audiences and contribute to business success in the digital era

Predictive Analysis for Big Mart Sales Using Machine Learning Algorithms

Abstract: Supermarkets and retail giants like Big Mart rely heavily on tracking individual item sales data to anticipate consumer demand and optimize inventory management. By mining data warehouses, anomalies, and trends can be identified, providing valuable insights for forecasting future sales volumes. Leveraging advanced machine learning techniques, such as Decision Tree Regression, predictive models can be developed to forecast sales accurately. Through the implementation of such a model, it has been observed that it surpasses the performance of existing forecasting models. This innovative approach not only enhances decision-making processes but also enables businesses like Big Mart to stay ahead in a competitive market landscape.

Исследование модернизации морского пассажирского порта на основе цифрового двойника и оценка инфраструктуры с помощью динамических данных от беспилотных авиационных систем

The current state of the maritime passenger transportation industry is considered, as well as the factors influencing the chosen industry are investigated (competition between ports in the sea region, new environmental requirements and a shortage of space for new ports and terminals in megacities). Models of the evolution of seaports and terminals in the regions of the seas are analyzed and the need to solve the problem of forecasting development, reasonable modernization is revealed infrastructure. The importance of integrating passenger ports into urban transport systems and ensuring effective interconnection of maritime transport with regional transport systems is noted. Special attention is paid to the growth and development potential of sea ferries and cruises in the Baltic Sea region, the historical importance of St. Petersburg for cruise tourism and the need for the gradual restoration of the port and the development of new route networks are noted. The necessity of developing new methods for modernizing the infrastructure of passenger seaports based on the creation of digital models and the use of dynamic data is substantiated. As a solution, it is proposed to create a new digital model of a marine passenger port using information obtained from unmanned aircraft systems. This model will accurately take into account the infrastructural features of passenger ports and terminals, as well as the established priority of berths for the service of cruise and ferry ships. For multi-scenario modeling, a new digital model of the marine passenger port of St. Petersburg “Marine Façade” is proposed. To analyze the simulation results, the developed software modules in the Python programming language are proposed to describe the behavior strategies of the decision maker (port manager). To describe the decision-making process in the conditions of uncertainty that arises when making a decision on the modernization of the port infrastructure, the choice of the Wald criterion was justified as a criterion. A series of experiments was performed, based on the data, digital 2D and 3D models of a marine passenger port were formed for subsequent use in the AnyLogic environment, and a digital twin was built. A distinctive feature of the model is the up-to-date information on the state of the transport infrastructure and integration with data on ship calls. The presented model, methods of working with the results of multi-dimensional modeling, the use of unmanned aircraft systems, and the development of a model based on decision-making under uncertainty can be applied to other passenger seaports and terminals, which emphasizes the universality of the proposed approach.

Clinical Utility of Blood Culture Identification 2 Panel in Flagged Blood Culture Samples from the Intensive Care Unit of a Tertiary Care Hospital.

The availability of rapid diagnostic platforms for positive blood cultures has accelerated the speed at which the clinical microbiology laboratory can identify the causative organism and facilitate early appropriate antimicrobial therapy. There is a paucity of data regarding the clinical utility of the blood culture identification 2 (BCID2) panel test and its correlation with phenotypic drug susceptibility testing (DST) in flagged blood culture bottles from intensive care units (ICUs) in countries such as India, which have high rates of multidrug-resistant gram-negative bacteria (MDR-GNB). We conducted a retrospective observational study in a tertiary care ICU on 200 patients above 18 years of age in whom a BCID2 test was ordered when blood cultures flagged positive. We found 99% concordance between BCID2 and cultures in the identification of bacteria and yeasts and 96.5% concordance between phenotypic and genotypic DST. Furthermore, BCID2 was available about 1.5 days earlier than conventional ID and DST and played a key role in tailoring antimicrobials in 82.5% of the patients. Polymyxin-based therapy was discontinued earlier after an empiric dose in 138 patients (69%) based on BCID2 reports. In critically ill patients with monomicrobial bacteremia, BCID2 rapidly identifies bacteria and antimicrobial resistance (AMR) genes and is significantly faster than conventional culture and sensitivity testing. Antibiotics were escalated in more than a third of patients and de-escalated in almost a fifth on the same day. We recommend that all ICUs routinely incorporate the test in their antibiotic decision-making process and in antimicrobial stewardship. Vineeth VK, Nambi PS, Gopalakrishnan R, Sethuraman N, Ramanathan Y, Chandran C, et al. Clinical Utility of Blood Culture Identification 2 Panel in Flagged Blood Culture Samples from the Intensive Care Unit of a Tertiary Care Hospital. Indian J Crit Care Med 2024;28(5):461-466.

Comparative analysis of treatment decision-making in patients with localized prostate and cervical cancer: what influences receiving surgery or radiotherapy?

PurposeThis study focused on identifying the factors influencing the decision-making process in patients with localized prostate and cervical cancer in Japan and specifically examining the choice between surgery and radiotherapy.MethodsPatients with specific cancer stages registered with a healthcare research company for whom radical surgery or radiotherapy was equally effective and recommended participated in this cross-sectional online survey.ResultsThe responses of 206 and 231 patients with prostate and cervical cancer, respectively, revealed that both groups relied heavily on the physicians’ recommendations (prostate: odds ratio (OR) = 40.3, p < 0.001; cervical: OR = 5.59, p < 0.001) and their impression of radiotherapy (prostate: OR = 9.22, p < 0.001; cervical: OR = 2.31, p < 0.001). Factors such as hypertension (OR = 6.48, p < 0.05), diabetes mellitus (OR = 9.68, p < 0.05), employment status (OR = 0.08, p < 0.01), and impressions of surgery (OR = 0.14, p < 0.01) also played a significant role in patients with prostate cancer. In contrast, the specialty of the physician (OR = 4.55, p < 0.05) proposing the treatment influenced the decision-making process of patients with cervical cancer. Information sources varied between the two groups: patients with prostate cancer were more inclined towards printed materials, whereas patients with cervical cancer were more inclined towards interpersonal relationships.ConclusionAlthough several limitations, such as the sample and recall bias, were noted, this study emphasizes the role of psychosocial factors in the decision-making process and the requirement for tailored information sources.

The Business Judgment Rule as a Protective Armor for Directors’ Responsibilities: A Comparative Study Among Mauritius, United Kingdom, and United States

Abstract The business judgment rule (BJR) is a corporate law doctrine that protects a decision of directors from fairness review by a court of law unless there is sufficient evidence that the director has breached his fiduciary duties or that the decision-making process is tainted. Given that the BJR acts as a protective armor for directors as their personal liability is not engaged for a corporate decision irrespective of its outcomes provided that some requirements are satisfied, the purpose of this paper is to critically assess whether directors of Mauritian companies are sufficiently protected and encouraged in the exercise of their duties. To achieve the research objective, the black letter research method was adopted to collect secondary data and a comparative analysis with some other countries’ rules on business judgment was conducted. A desk-based approach and content analysis were used to collect this information. The countries selected for the comparison are the United Kingdom and United States. Mauritian law does not afford protection to directors for decisions taken based on business judgment whereas this research has demonstrated that the laws of other countries being the United Kingdom and the United States as well as their judicial instances both recognize and apply the BJR.

A just energy transition for Indigenous peoples: From ideal deliberation to fairness in Canada and Australia

In response to the global climate crisis, renewable energy projects are being developed on Indigenous territories. As renewable energy sources are not without social and ecological impacts, it is crucial to consider who benefits from these projects and who bears the consequences. In settler states such as Canada and Australia, the power dynamics are affecting the role of Indigenous peoples in the energy transitions. This paper proposes a theoretical framework for a just energy transition (JET) based on a comprehensive literature review of justice movements, energy transitions, and Indigenous experiences in Canada and Australia. The literature on environmental, climate, and energy justice examines three dimensions of justice: procedural, distributional, and recognition. While these dimensions offer important perspectives for our analysis, they are not sufficient on their own. This paper argues that promoting justice for Indigenous knowledge renewal is crucial for establishing a fair energy future. In addition to knowledge renewal, our framework comprises two more JET components: socioeconomic impact distribution, and procedural power. We argue that the JET components are deeply interrelated in an insidious loop effect. This means that all justice issues have the same roots, perpetuated by the historical consequences of colonisation. The question is how to simultaneously address them to transform a negative cycle into a positive one. Drawing on Indigenous rights in Canada and Australia this paper highlights the importance of improved bilateral communication strategies to break the insidious loop effect. Before discussing project strategies, it is crucial to consider the fairness of the decision-making process.

Alien plant species distribution in Romania: a nationwide survey following the implementation of the EU Regulation on Invasive Alien Species

Biological invasions pose an increasing risk to nature, social security and the economy, being ranked amongst the top five threats to biodiversity. Managing alien and invasive species is a priority for the European Union, as outlined in the EU Biodiversity Strategy for 2030 and the Kunming-Montreal Global Biodiversity Framework. Alien plant species are acknowledged to impact the economy and biodiversity; thus, analysing the distribution of such species provides valuable inputs for the management and decision-making processes. The database presented in the current study is the first consolidated checklist of alien plant species that are present in Romania, both of European Union concern and of national interest. This database complements a prior published distribution, based only on records from literature, bringing new information regarding the occurrence of alien plants in Romania, as revealed by a nationwide field survey. We consider this database a valuable instrument for managing biological invasions at both national and regional levels, as it can be utilised in further research studies and in drafting management and action plans, assisting stakeholders in making informed decisions and implementing management actions. We present the results of the first nationwide survey of alien plant species in Romania, conducted between 2019 and 2022, in the framework of a national project coordinated by the Ministry of Environment, Waters and Forests and the University of Bucharest. The present database complements and updates the database published by Sirbu et. al (2022), which included occurrence records published until 2019. The new database includes 98323 occurrence records for 396 alien plant species in 77 families, with most species belonging to the Asteraceae family. One alien plant species in our database, the black locust Robinia pseudoacacia L., had more than 10,000 occurrence records. The distribution database also includes information on newly-reported invasive alien plant species of European Union concern in Romania (i.e. the floating primrose-willow Ludwigia peploides (Kunth) P.H.Raven) and documents the presence of plants in 44 additional families compared to Sirbu et al. (2022). Each entry includes information on species taxonomy, location, year, person who recorded and identified the alien plant, geographical coordinates and taxon rank.

Fostering human learning in sequential decision-making: Understanding the role of evaluative feedback.

Cognitive rehabilitation, STEM (science, technology, engineering, and math) skill acquisition, and coaching games such as chess often require tutoring decision-making strategies. The advancement of AI-driven tutoring systems for facilitating human learning requires an understanding of the impact of evaluative feedback on human decision-making and skill development. To this end, we conduct human experiments using Amazon Mechanical Turk to study the influence of evaluative feedback on human decision-making in sequential tasks. In these experiments, participants solve the Tower of Hanoi puzzle and receive AI-generated feedback while solving it. We examine how this feedback affects their learning and skill transfer to related tasks. Additionally, treating humans as noisy optimal agents, we employ maximum entropy inverse reinforcement learning to analyze the effect of feedback on the implicit human reward structure that guides their decision making. Lastly, we explore various computational models to understand how people incorporate evaluative feedback into their decision-making processes. Our findings underscore that humans perceive evaluative feedback as indicative of their long-term strategic success, thus aiding in skill acquisition and transfer in sequential decision-making tasks. Moreover, we demonstrate that evaluative feedback fosters a more structured and organized learning experience compared to learning without feedback. Furthermore, our results indicate that providing intermediate goals alone does not significantly enhance human learning outcomes.

The Mathematics Performance and the Track Preference of Grade 10 Students

This research paper focused on investigating the mathematical performance of grade 10 students and its role in determining their Senior High School (SHS) track/strand. The aim of the study is to explore the attitudes, beliefs, and opinions of the students towards mathematics and how it influences their decision-making process in choosing their SHS track/strand. Additionally, the study aims to examine the factors that affect students' performance in mathematics in relation to their chosen SHS track/strand. The research design utilized in this study is a survey questionnaire, which was administered to grade 10 students at Narvacan National Central High School. The participants of the study are primarily grade 10 students, with a majority being 16 years old. There are more female respondents than male, and a significant percentage of the respondents' parents have blue-collar jobs. Additionally, most of the parents are college graduates. The findings of the study indicate that the level of mathematics performance among grade 10 students at Narvacan National Central High School is considered "Fair." Furthermore, the students' preferred track for SHS is Humanities and Social Sciences (HUMSS). The study also reveals a significant relationship between mathematics performance and the profile of the respondents. Additionally, there is a significant relationship between mathematics performance and STEM, HUMSS, and TVL track/strands. However, there is no relationship between mathematics performance and the preferred strand of students who chose ABM. The implications of this research highlight the importance of mathematics in the decision-making process of students when choosing their SHS track/strand.

Environmental and Economic Aspects of a Containership Engine Performance in Off-Design Conditions

A comprehensive thermodynamic model of the marine diesel engine in combination with the operating cost assessment is used in the decision-making process regarding the selection of the most favorable slow steaming speed. The influence of the number of cylinders and sailing speed on exhaust emissions, fuel consumption and operating costs is analyzed for the case of a containership sailing on a Trans-Pacific route. The engine simulation model was used for the calculation of engine fuel consumption, NOX and soot emissions. The operating costs and annual income were calculated through a fuel consumption correlation. The benefit of slow steaming is shown through the comparison of calculated data with the data calculated for the six-cylinder engine and the design speed of 23 knots. The highest reduction of 67.2% in CO2 and 93.3% in NOX emissions is achieved with the seven-cylinder engine at 15 knots, but the six-cylinder engine yields the highest increase in income per route of 6.2%. To comply with the proposed regulations for GHG emissions, the sailing speed should be reduced by at least 26%, which results in a decrease in the annual income by 24% compared to the design speed.

Combining Building Information Model and Life Cycle Assessment for Defining Circular Economy Strategies

Although the construction industry has the potential to implement circular economy (CE) principles, the sector suffers from a veritable lack of initiatives to develop circular and regenerative design principles. However, existing buildings intended to be demolished could be considered as material banks for future constructions, with the aim to exploit anthropogenic resources, extend material/product efficiency, and reduce the extraction of natural resources. This concept of buildings as material banks is being studied more and more in the scientific literature, but it still requires the existing building stock to be fully digitalized, thus making materials reusable in new buildings starting from the architectural design stage. Moreover, the decision process regarding CE strategies requires the consideration of the environmental impacts of the deconstruction and end-of-life processes, which is essential in CE implementation. This paper introduces a digital platform for generating materials inventory and supporting the definition of reuse strategies. Then, by using digital tools in combination with life cycle assessment (LCA) studies of the deconstruction process and output of materials and components inventories, a method is defined and tested on a pilot building in Luxembourg.