Methodological Decisions Research Articles

Validating the Influences of Methodological Decisions on Assessing the Spatiotemporal Stability of Speech Movement Sequences Using Children's Speech Data

Purpose: Prior research introduced quantifiable effects of three methodological parameters (number of repetitions, stimulus length, and parsing error) on the spatiotemporal index (STI) using simulated data. Critically, these parameters often vary across studies. In this study, we validate these effects, which were previously only demonstrated via simulation, using children's speech data. Method: Kinematic data were collected from 30 typically developing children and 15 children with developmental language disorder, all spanning the ages of 6–8 years. All children repeated the sentence “buy Bobby a puppy” multiple times. Using these data, experiments were designed to mirror the previous simulated experiments as closely as possible to assess the effects of analytic decisions on the STI. Experiment 1 manipulated number of repetitions, Experiment 2 manipulated stimulus length (or the number of movement units in the target phrase), and Experiment 3 manipulated precision of parsing of the articulatory trajectories. Results: The findings of all three experiments closely mirror those of the prior simulation. Experiment 1 showed consistent underestimation of STI values from smaller repetition counts consistent with the theoretical model for all three participant groups. Experiment 2 found speech segments containing fewer movements yield lower STI values than longer ones. Finally, Experiment 3 showed even small parsing errors are found to significantly increase measured STI values. Conclusions: The results of this study are consistent with the findings of prior simulations in showing that the number of repetitions, length of stimuli, and amount of parsing error can all strongly influence the STI independent of behavioral factors. These results further confirm the importance of closely considering the design of experiments, which employ the STI.

Integrated fuzzy decision-making methodology with intuitionistic fuzzy numbers: An application for disaster preparedness in clinical laboratories

Society is on constant alert due to the increasing frequency and severity of Seasonal Respiratory Diseases (SRDs), posing significant challenges from both a humanitarian and public health perspective. The recent COVID-19 pandemic has tested the capacity of clinical laboratories to address seasonal infections, epidemic outbreaks, and critical emergencies. This scenario has led to operational burdens, primarily from resource limitations, a lack of proactive planning, and the low adaptation to unforeseen circumstances. Coupling different data-driven approaches considering multi-criteria weighting, interdependence assessment, and outranking are critical for devising effective interventions upgrading the operability of clinical labs during SRDs. Nonetheless, a deep literature review revealed there are no studies using these hybridized approaches when addressing this problem. Consequently, this article proposes the application of an innovative hybrid Multicriteria Decision-Making (MCDM) methodology that integrates the Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP), Intuitionistic Fuzzy Decision Making Trial and Evaluation Laboratory (IF-DEMATEL), and Combined Compromise Solution (CoCoSo) to assess the disaster preparedness of clinical laboratories during SRDs. Initially, we applied IF-AHP to assign the relative weights to criteria and sub-criteria, considering the inherent hesitation and uncertainty in decision-making. Subsequently, IF-DEMATEL was utilized to analyze the interrelationships between criteria, providing insights into the interrelations among clinical lab disaster management drivers. Finally, the CoCoSo method was applied to estimate each lab’s Preparedness Index (PI) and detect response gaps when coping with SRDs. The suggested methodology was validated across nine clinical laboratories in Colombia during the most recent respiratory pandemic. This study contributes to the healthcare sector authorities by identifying key criteria and sub-criteria affecting the response of clinical labs, the elicitation of main response drivers in clinical labs when facing SRDs, and the calculation of a multidimensional indicator representing the preparedness of the labs. This work also enriches the literature by applying the IF-AHP, IF-DEMATEL, and CoCoSo approach to a challenging case study requiring a multi-method data-driven application. Furthermore, it suggests future directions to improve the proposed framework in other related contexts.

Arbitrary methodological decisions skew inter-brain synchronization estimates in hyperscanning-EEG studies

Abstract Over the past decade, hyperscanning has emerged as an important methodology to study neural processes underlying human interaction using fMRI, EEG, fNIRS, and MEG. However, many methodological decisions regarding preprocessing and analysis of hyperscanning data have not yet been standardized in the hyperscanning community, yet may affect inter-brain estimates. Here, we systematically investigate the effects common methodological choices can have on estimates of phase-based inter-brain synchronization (IBS) measures, using real and simulated hyperscanning (dual) EEG data. Notably, we introduce a new method to compute circular correlation coefficients in IBS studies, which performs more reliably in comparison to the standard approach, showing that the conventional circular correlation implementation leads to large fluctuations in IBS estimates due to fluctuations in circular mean directions. Furthermore, we demonstrate how short epoch durations (of 1 s or less) can lead to inflated IBS estimates in scenarios with no strong underlying interaction. Finally, we show how signal-to-noise ratios and temporal factors may confound IBS estimates, particularly when comparing, for example, resting states with conditions involving motor actions. For each of these investigated effects, we provide recommendations for future research employing hyperscanning-EEG techniques, aimed at increasing validity and replicability of inter-brain synchronization studies.

Theories of Arousal Predict a Link Between Heart Rate Variability and Reactive Aggression: Meta-Analytic Results Disagree.

Polyvagal theory posits that habitually aggressive individuals might have an impaired capacity to calm after arousal, which has led to the investigation of Arousal-based biological indicators - "biomarkers" - of aggression, to identify individuals at high risk. The most popular approach in research examining (specifically reactive) aggression is the use of wearable technologies that can non-invasively measure heart rate variability (HRV), a cardiovascular phenomenon impacted by activation of the parasympathetic ("rest and digest") nervous system. But there is a problem: no one has systematically analyzed the results of these studies to determine if HRV is an effective predictor of reactive aggression. We surveyed an initial 705 articles, producing 48 effect estimates amenable to meta-analysis. Counter to predictions derived from polyvagal theory, the results reveal no correlation between HRV and reactive aggression. We discuss the implications of this novel finding for theory and practice, considering both the complexity of identifying effective biomarkers and the practical limitations driving methodological decisions in aggression research. We conclude that there is no empirical evidence supporting HRV as a valid biomarker of aggression.

Multi-attribute-based procedure for seismic loss scenario in a historical area

AbstractSeismic events have shown to be exceedingly damaging to structures over time, with serious social and economic consequences. As a result, large-scale seismic risk assessments are essential for reducing the potential damage from future earthquakes. Therefore, the proposed study attempts to examine the vulnerability and risk of unreinforced masonry buildings (URM) placed in aggregate conditions in a historical area of the city centre of Lisbon. To this purpose, a comprehensive exposure model was developed combining satellite remote sensing, GIS software, and census data. Subsequently, seismic hazard was evaluated in the area, considering both Peak Ground Acceleration (PGA) and macro-seismic intensity for different return periods (i.e. 2-50-100-475-975-2500-5000 years). Vulnerability was assessed by introducing a novel approach to earthquake risk assessment using Multi-Criteria Decision-Making methodologies. Specifically, the method employs the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) computational methods to evaluate parameter weights and vulnerability index. Damage scenarios, fragility curves and mean damage ratios curves were provided to offer an overview of the vulnerability of the assets exposed to risk. Finally, the expected consequences were evaluated in terms of direct economic losses showing an economic loss of 595 M€ for a 475-years return period, while 1108 M€ for 5000-years. This study significantly enhances seismic analysis for urban areas by introducing a Multi-Criteria Approach. This method simplifies vulnerability assessment, ensuring ease of application and reproducibility. Its insights offer valuable support for disaster risk management decisions, facilitating the implementation of resilience and risk-reduction strategies.

Clinical recommendations for polygenic risk score-enhanced breast cancer screening: Can.Heal project

Abstract Background The application of polygenic risk score (PRS) in breast cancer (BC) screening presents promising opportunities. Developing recommendations for future use and research on this topic is a key focus of the EU4Health project: Building the EU cancer and public health genomics platform (Can.Heal). We aim to provide these recommendations based on the analysis of available evidence through a transparent and rigorous development process. Methods The recommendations adopt the GRADE evidence to decision methodology, leveraging an evidence review team and a multidisciplinary panel of nine experts. A systematic review is being conducted to evaluate the evidence for PRS in BC screening, in the domains of benefits and harms, acceptability, feasibility, equity and cost-effectiveness. Results Regarding benefits and harms of adding PRS to BC standard screening, we identified 63 relevant articles. Forty-five (71%) discussed benefits, while 14 (22%) addressed harms. Forty-two (67%) were observational studies, 18 (28%) modeling studies and 1 non-randomised control trial (2%) that examined the diagnostic accuracy of PRS-enhanced screening for relative BC risk prediction using measures such as net reclassification index and area under the curve. Lastly, two modeling studies (3%) assessed the clinical utility of PRS-enhanced screening in terms of life years gained, BC deaths averted. The other domains are under examination. The panel convened to redefine the research question and outcomes of interest and will reconvene to assess the certainty of evidence collected, and subsequently to draft recommendations. Conclusions The integration of PRS into BC screening shows potential benefits in improving risk prediction. Ongoing trials, such as Wisdom and MyPEBS, are studying the clinical utility of integrating PRS in BC screening. The approach taken by the Can.Heal aims to ensure that the recommendations are based on a thorough and balanced evaluation of the available evidence. Key messages • The Can.Heal project aims to formulate policy and research recommendations for breast cancer screening using PRS through a thorough evidence review. • Upcoming PRS recommendations rely on expert opinion and modeling studies.

Quasirung orthopair fuzzy linguistic sets and their application to multi criteria decision making

Linguistic term fuzzy sets provide an intuitive way to express preferences, enhancing understanding and communication among decision-makers. In this article, we introduce the novel concept of p,q-quasirung orthopair fuzzy linguistic sets (p,q-QOFLSs), which merge the principles of p,q-quasirung orthopair fuzzy sets (p,q-QOFSs) with linguistic fuzzy sets. This new framework offers a more robust approach to handle uncertain and imprecise information in decision-making processes, characterized by linguistic membership and non-membership degrees. We establish several fundamental operational laws, alongside score and accuracy functions, to facilitate the comparison of p,q-quasirung orthopair fuzzy linguistic numbers. Leveraging these operational laws, we propose a series of weighted averaging and geometric operators under p,q-QOFLSs. Furthermore, we formulate a multi-attribute decision-making methodology using these operators. The significance of the proposed method lies in its ability to model complex decision-making scenarios with enhanced precision. A numerical example validates the practicality and adaptability of the methodology, supported by sensitivity analyses and comparative evaluations, highlighting the innovation and efficiency of the approach.

Demerit of the information concerning decision-making methodology: A dynamic Cournot game with adaptive expectation

It is not uncommon for firms to gain access to the information concerning not only the previous decisions of others but also their decision-making methodologies. However, few literatures study the information concerning decision-making methodologies in dynamic Cournot games. This paper considers a dynamic Cournot game with adaptive expectation. It is assumed that each firm extra acquires the information regarding the other competitors' decision-making methodologies, and the information will be employed to estimate the outputs of other firms in the next period. The information leads to a decline in the stability of the original Cournot-Nash equilibrium, as demonstrated by both theoretical analysis and numerical simulation. It implies that boundedly rational firms need be vigilant for the information concerning decision-making methodologies in certain cases.

From persons to general principles: Methodological decisions for idiographic and nomothetic research

Research in psychology can have various foci, ranging from the psychological dynamics of single individuals to the generalization across individuals, often termed idiographics and nomothetics, respectively. However, terminological ambiguities have limited communication clarity about idiographics and nomothetics. Importantly, whether studies are suitable for idiographic and/or nomothetic inferences is not categorical but rather a matter of degree. Therefore, we propose a list of concrete, better-defined methodological aspects characterizing individual studies. Specifically, we highlight 16 different decisions researchers have to make pertaining to (1) research question, (2) research design, and (3) analyses and interpretation. These decisions are introduced with a focus on personality psychology but apply to psychological research broadly. We discuss the different decisions with an emphasis on their relation to idiographic and nomothetic inferential goals. Rather than providing specific recommendations, we emphasize the importance of aligning the methodology of a given study with the research question and theory. Overall, this overview seeks to help researchers make relevant decisions more intentionally to help them tailor their studies to their inferential goals.

Multi-Criteria Decision-Making Approach for Optimal Energy Storage System Selection and Applications in Oman

This research aims to support the goals of Oman Vision 2040 by reducing the dependency on non-renewable energy resources and increasing the utilization of the national natural renewable energy resources. Selecting appropriate energy storage systems (ESSs) will play a key role in achieving this vision by enabling a greater integration of solar and other renewable energy. ESSs allow for solar power generated during daylight hours to be stored for use during peak demand periods. Additionally, the proposed framework provides guidance for large-scale ESS infrastructure planning and investments to support Oman’s renewable energy goals. As the global renewable energy market grows rapidly and Oman implements economic reforms, the ESS market is expected to flourish in Oman. In the near future, ESS is expected to contribute to lower electricity costs and enhance stability compared to traditional energy systems. While ESS technologies have been studied broadly, there is a lack of comprehensive analysis for optimal ESS selection tailored to Oman’s unique geographical, technical, and policy context. The main objective of this study is to provide a comprehensive evaluation of ESS options and identify the type(s) most suitable for integration with Oman’s national grid using a multi-criteria decision-making (MCDM) methodology. This study addresses this gap by applying the Hesitate Fuzzy Analytic Hierarchy Process (HF-AHP) and Hesitate Fuzzy VIKOR methods to assess alternative ESS technologies based on technical, economic, environmental, and social criteria specifically for Oman’s context. The analysis reveals pumped hydro energy storage (PHES) and compressed air energy storage (CAES) as the most appropriate solutions. The tailored selection framework aims to guide policy and infrastructure planning to determine investments for large-scale ESSs and provide a model for comprehensive ESS assessment in energy transition planning for countries with similar challenges.

Understanding Ourselves in Qualitative Research Through Collaborative Sense-making

In the fall of 2023, the iSchool at Syracuse University offered a new PhD course entitled IST 800 Information Studies Seminar: Qualitative Research Methods. This course was intended to teach students about various qualitative methods. A key part of the curriculum was centered on a discussion of how researchers are implicated by their epistemological and ontological positionality and how this awareness should inform their methodological decisions and analytical interpretations. As part of the course, students were asked to keep a weekly sense-making journal, which was intended as a routine medium through which students could reflectively engage with the various author claims, arguments, and philosophical perspectives that were raised in the course readings and shared class discussions. After the course ended, several of the students from the course embarked on a collaborative autoethnographic project to examine how their respective learnings about qualitative methodologies had shaped their individual research ontologies and epistemologies. This poster provides an overview of this collective analysis. We argue that the reflexive nature of the course acted as a generative springboard for us (i.e., the students) to explore and question our values, sensitivities, and, ultimately, our worldviews; in tandem, the assigned sense-making memos gave us a chance to proactively develop our researcher positionalities. It is our hope that sharing the insights from this autoethnographic project will support other early career researchers in understanding how to critically engage their own positionality, and will provide information science educators inspiration for their own research methodology pedagogy.

Methodology of Multiple-Criteria Decision Making for Selecting a Refrigerant to Be Used in Commercial Refrigeration Equipment

This paper presents the application of the multiple-criteria decision-making (SAW) method for selecting the optimum refrigerant for the refrigeration systems of commercial cooling equipment used in gastronomy furniture, which is paramount in storing food under optimal conditions. The analysis focused on comparing different refrigerants, including natural refrigerants such as R744 (carbon dioxide) and R290 (propane) and synthetic refrigerants such as R455A, R449A, and R452A. As a result of the analysis using the SAW method, the refrigerant R455A was found to be the best solution. This choice resulted from the consideration of various decision criteria, such as energy efficiency, environmental impact, operating costs, and technology availability. R455A stands out as a synthetic refrigerant that provides high energy efficiency with minimal environmental impact. Its use supports sustainability goals by reducing greenhouse gas emissions and electricity consumption, which is crucial given the modern regulatory requirements and environmental standards. This study offers a practical decision-making tool for commercial refrigeration equipment designers and manufacturers, supporting them while selecting the optimal technological solutions. The choice of refrigerant R455A addresses the need to integrate energy efficiency, environmental protection, and cost-effectiveness in the process of designing modern refrigeration systems for catering furniture.

Organizing planning knowledge for automated vehicles and intelligent transportation systems

AbstractIntelligent Transportation Systems (ITS) are crucial for developing fully automated vehicles. While significant progress has been made with advanced driver assistance systems and automation technology, challenges remain, such as improving traffic information, enhancing planning and control systems, and developing better decision‐making capabilities. Despite these hurdles, the potential benefits of ITS are so many that its challenges have attracted substantial industrial investment and research groups interested in the automated driving field. In this work, a methodology based on state space search for planning knowledge integration is proposed. The main goal of the proposal is to provide a planning system with the necessary information to perform properly any task related to lateral and longitudinal control, path following, trajectory generation, arbitration and behavior execution by localizing the vehicle with respect to a high‐level road plan. To this end, this research compares cutting‐edge methods for rapidly finding the K nearest neighbor in relatively high dimensional road plans constructed from the traffic information stored in a high definition map. During the experimentation phase, promising real‐time results have been obtained for fast KNN algorithms, leading to a robust tree index‐based methodology for decision making in self‐driving vehicles combining path planning, trajectory tracking, trajectory creation, knowledge aggregation and precise vehicle control.

Optimizing marine vehicles industry: a hybrid analytical hierarchy process and additive ratio assessment approach for evaluating and selecting IoT-based marine vehicles

Rapid developments in the Internet of Things (IoT) have opened the door for game-changing applications in numerous sectors, especially the vehicle industry. There is a rising demand for efficient assessment and decision-making methodologies to pinpoint the most promising choices for the vehicle sector with the introduction of IoT-based maritime vehicles. To overcome this issue, the integrated multi-criteria decision-making analysis (MCDA) paradigm proposed in this research combines the additive ratio assessment (ARAS) and analytic hierarchy process (AHP) approaches to evaluate and choose IoT-based maritime vehicles based on their performance- and authenticity-related criteria in the vehicle sector. The selection issue is hierarchically organized, and the assessment criteria are prioritized using the AHP approach. There are seven performance and authentication related criteria are selected that might aid in the selection procedure. Using the AHP, we are assigned these criteria proportionate weights that reflect their respective significance and interrelationships. AHP, however, falls short of offering a thorough analysis of the alternatives that exist. To overcome these restrictions, this research presents the integration of AHP with the ARAS approach for the ranking of alternatives according to how well they perform against the set criteria. By using the ARAS technique, it is possible to get over the restrictions of AHP and achieve a more thorough assessment of maritime IoT-based vehicles. The efficiency of the framework is proven using empirical data and professional judgment. The findings show that the hybrid method successfully encapsulates the intricate relationships between the factors being evaluated and objectively appraises the potential of IoT-based maritime vehicles for the automotive sector. This study extends to the area by providing an organized and thorough method for assessing and choosing IoT-based maritime vehicles. Considering several factors and their mutual dependence, the hybrid AHP and ARAS technique gives decision-makers a powerful tool for evaluating the potential of IoT-based maritime vehicles in the automotive sector. Smart decisions on the deployment of IoT-based marine vehicles and maximizing the potential they present may be made by beneficiaries in the automotive sector using the study’s results.

Infection detection revolution: Harnessing AI-powered image analysis to combat infectious diseases.

Infectious diseases wield significant influence on global mortality rates, largely due to the challenge of gauging their severity owing to diverse symptomatology. Each nation grapples with its unique obstacles in combatting these diseases. This study delves into three distinct decision-making methodologies for medical diagnostics employing Neutrosophic Hypersoft Set (NHSS) and Plithogenic Hypersoft Set (PHSS), extensions of the Hypersoft set. It introduces state-of-the-art AI-driven techniques to enhance the precision of medical diagnostics through the analysis of medical imagery. By transforming these images into the aforementioned sets, the analysis becomes more refined, facilitating more accurate diagnoses. The study advocates various courses of action, including isolation, home or specialized center quarantine, or hospitalization for further treatment. The novelty in this study utilizes cutting-edge AI methods to enhance medical imaging, transforming them into accurate diagnostic tools, marking a significant change in how infectious diseases are addressed. By combining machine learning and pattern recognition, it offers the potential to overhaul healthcare worldwide, facilitating accurate diagnoses and customized treatment plans, ultimately reducing the global burden of infectious diseases on mortality rates.

Smart supply chain collaboration maturity evaluation model based on a q-Rung orthopair fuzzy decision making methodology

In today’s IT-intensive business context, evaluating supply chain (SC) collaboration maturity (CM) is crucial for guiding enterprises toward integrated processes that collaborate across enterprise boundaries. This study develops an SC CM evaluation model by integrating a conceptual maturity model with a q-Rung Orthopair Fuzzy Set (q-ROFs)-based decision-making methodology, including a new scoring method. The major aim of this study is to facilitate a qualitative multi-criteria evaluation of SC CM. The conceptual model develops criteria sets based on smart technologies (such as sensors, robotics, cloud, ERP) and business processes. The proposed methodology is applied to 10 generic case companies, demonstrating its ability to provide multi-criteria scorecards and comparative rankings within each maturity level. The results show that the suggested approach can be used to guide the future efforts of individual enterprises, serving for benchmarking purposes across multiple companies by providing an overall objective evaluation for managers and policymakers. The proposed methodology is original by: (a) conceptual model development, (b) developing a multi-criteria quantitative decision-making approach with a new scoring method and integrating it with unique process-based and IT-based criteria sets of the conceptual maturity model, and (c) facilitating managerial scorecards and evaluations.

Enhancing readiness degree for Industrial Internet of Things adoption in manufacturing enterprises: An integrated Pythagorean fuzzy approach

This study proposes a hybrid multi-criteria decision-making (MCDM) methodology designed to enhance the readiness for adopting the Industrial Internet of Things (IIoT) in manufacturing enterprises. The Pythagorean fuzzy approach is employed to address uncertainty and imprecision throughout decision-making processes. The development framework in this study incorporates TOE (Technology-Organization-Environment) and HOT fit (Human-Organization-Technology) to pinpoint barriers to IIoT adoption. Additionally, a triple helix model (THM) emphasizing on the synergy among university-industry-government is utilized to formulate pragmatic strategies. The agro-food processing industry in Thailand is used as a case study. In this study, even barriers are identified and validated through the Delphi method. The SWARA (Step-wise Weight Assessment Ratio Analysis) method determines the importance weights of these barriers, revealing “lack of digital culture”, “lack of knowledge and expertise,” and “job displacement concerns” as the three most critical barriers. The COBRA (COmprehensive Distance Based Ranking) method is employed to prioritize pragmatic strategies under THM, indicating that the role of the university in enhancing human capital capabilities is the most important, followed by the government's roles in enabling national ICT infrastructures and offering investment incentives as the second and third pragmatic strategies, respectively. A sensitivity analysis validates the proposed framework's reliability and robustness. The study's findings emphasize the potential of this integrated framework to guide future research endeavors among scholars and academicians across diverse industries beyond agri-food processing.

Sustainable urban water management index for developing countries. A case study in Puebla City, Mexico

Several indicator and index frameworks have been reported to assess management efficiency and promote transparency, facilitating decision-making and innovation. However, they cannot be applied in cities with limited information, making them impractical. This study proposes a Sustainable Urban Water Management Index (SUWMI) for such cities, selecting six AquaRating indicators plus two indexes recommended in the Sustainable Development Goal 6, yielding a simplified and practical version for diagnosing the management performance rate of the Water Operating Systems (WOS) in cities. Conceptual and mathematical models were defined for SUWMI, and the eight selected terms were hierarchized based on a decision tree with the participation of an expert panel and further multicriteria decision-making methodology by the Analytic Hierarchy Process (AHP) to calculate the weighting parameters. The selected indicators represent five categories on a percentage scale. The highly populated Puebla City Metropolitan Zone in Mexico was the case study. Information was gathered from five Water Operation Systems (WOS) from 2002 to 2020. The tap water area's coverage and physical efficiency improved despite the population growth due to an adequate political strategy after 2014. The SUWMI values of each WOS diagnoses its performance, highlighting their differences and providing a valuable tool for guiding urban water public policy programs. The paper proposes a simple, fast, and easy-to-calculate index that can be helpful for a broader cluster of cities and urban settlements with deficient available information, which is common in developing countries.

Stirring digital innovation in wood waste upcycling: system dynamics as a circular design decision-making methodology

Abstract The urgency to reduce climate-changing emissions and natural resources exploitation is pushing architecture and industrial design to explore viable options to innovate current practices on material use. In forest-based industries, in particular, wood upcycling is emerging as a pivotal investigation field, with a specific research agenda aimed at reducing the impacts generated by the rising demand of timber products and the actual incineration and landfilling rates. Indeed, the development of new technological solutions needs to account for energy and material flows, as well as for the environmental impacts and social and economic dynamics. Such a new complexity pushes towards a design approach that links digital technologies to circularity goals for a more comprehensive understanding of the forest-based resource management and the evalutation of alternative scenarios. In this paper, we propose System Dynamics (SD) as a methodology to provide a qualitative and quantitative framework to promote digital circular innovation in the wood industry. SD is a dynamic, multi-scale, multi-dimension and site-specific decision-making support tool wich helps designers, researchers and industrial stakeholders to understand and simulate the dynamic behavior and performances of complex systems within specific time and space parameters. The proposed SD for wood waste upcycling allows to model “stock and flow” scenarios, to simulate and compare the consequences of technological options on the system and to target their development in time - and space-based projections. This study outlines the first advances for the SD application to the Italian wood industry, and examines the consequences of the application of different circular strategies on a “business-as-usual” scenario.

A multi-objective optimization decision-making methodology for fostering synergies in the water-energy-food nexus

Current decision-making methods within the water-energy-food nexus (WEFN) encounter challenges in practicality, portability, scalability, and accuracy. Optimization methods, integrating site-specific data, offer promise for achieving desired outcomes and enhancing practical decision implementation within WEFN systems. However, these methods still struggle with solving multi-objective and multi-constraint problems, poor performance, and decision-making difficulties. This study developed an optimization method for WEFN systems, which integrates an evolutionary algorithm, swarm intelligence algorithm, multistage evolutionary algorithm, and post-optimization theories to address these issues. Performance results demonstrated that the proposed algorithm outperformed traditional metaheuristic optimization algorithms. Specifically, it excelled in tackling high-dimensional constrained problems, surpassing the classical NSGA series algorithms with a 1.34-fold improvement in the comprehensive performance metric HV. Subsequently, this algorithm combined with knee point post-optimization theory was applied to a typical arid region known for food and energy production. Compared with the business-as-usual scenario, the optimized schemes enhance agricultural benefits, saving 13.3 billion m3 of water over the entire planning period. Meanwhile, sustainable energy implementation would yield potential carbon emission reduction benefits of around 2.8 billion CNY. In summary, the proposed method would successfully provide a paradigm for the synergetic decision-making of WEFN systems.