Four-phase Methodology Research Articles

Integrating machine learning and parametric design for energy-efficient building cladding systems in arid climates: Sport hall in Kerman

Climate change, driven by fossil fuel dependence, presents a significant challenge for the construction industry, particularly in energy-intensive regions like arid climates. This research investigates the potential of integrating machine learning and parametric optimization to enhance the energy efficiency of spatial structure domes in such environments. Focusing on a sports pavilion in Kerman, Iran, the study examines the crucial role of cladding systems in building energy performance. Employing a rigorous four-phase methodology, the research optimizes dome cladding materials for hot, dry climates using a dual objective function: energy cost and material cost. The process involves a comprehensive literature review, data-driven material selection, advanced energy simulations, and optimization analysis. Parametric modeling tools (Rhino, Grasshopper, Honeybee) facilitate the comparative analysis of various cladding systems. Multivariate Polynomial Regression (MPR) enables predictive modeling of energy consumption and material costs, streamlining the design process for architects. The optimized solution is a hybrid cladding model composed of 10 % polycarbonate and 90 % aluminum. Analysis reveals that the hybrid system offers superior energy optimization compared to pure aluminum (4.58 %) and polycarbonate (5.70 %). While polycarbonate has a lower initial material cost, the hybrid system achieves a balance between material expenditure and long-term energy efficiency. This highlights the importance of considering life-cycle costs when evaluating building envelope materials. This research advances a framework that leverages machine learning and parametric design for building envelope optimization. This framework empowers architects and engineers to create energy-efficient structures within arid environments, promoting a more sustainable built environment.

Development of the 2023 ACR/EULAR Antiphospholipid Syndrome Classification Criteria, Phase III-D Report: Multicriteria Decision Analysis.

The 2023 American College of Rheumatology/EULAR antiphospholipid syndrome (APS) classification criteria development, which aimed to identify patients with high likelihood of APS for research, employed a four-phase methodology. Phase I and II resulted in 27 proposed candidate criteria, which are organized into laboratory and clinical domains. Here, we summarize the last stage of phase III efforts, employing a consensus-based multicriteria decision analysis (MCDA) to weigh candidate criteria and identify an APS classification threshold score. We evaluated 192 unique, international real-world patients referred for "suspected APS" with a wide range of APS manifestations. Using proposed candidate criteria, subcommittee members rank ordered 20 representative patients from highly unlikely to highly likely to have APS. During an in-person meeting, the subcommittee refined definitions and participated in an MCDA exercise to identify relative weights of candidate criteria. Using consensus decisions and pairwise criteria comparisons, 1000Minds software assigned criteria weights, and we rank ordered 192 patients by their additive scores. A consensus-based threshold score for APS classification was set. Premeeting evaluation of 20 representative patients demonstrated variability in APS assessment. MCDA resolved 81 pairwise decisions; relative weights identified domain item hierarchy. After assessing 192 patients by weights and additive scores, the Steering Committee reached consensus that APS classification should require separate clinical and laboratory scores, rather than a single-aggregate score, to ensure high specificity. Using MCDA, candidate criteria preliminary weights were determined. Unlike other disease classification systems using a single-aggregate threshold score, separate clinical and laboratory domain thresholds were incorporated into the new APS classification criteria.

An Architectural Solution to a Biological Problem: A Systematic Review of Lighting Designs in Healthcare Environments

Neuroscience and biological evidence emphasizes the profound influence of natural light on human health, offering benefits such as reducing fatigue, heightened alertness in healthcare providers, and improving patient outcomes. The objective of this review is to identify scientific studies and research to evaluate and report evidence of indoor lighting conditions’ influence on health outcomes, which can be used to develop lighting designs that align circadian rhythms in healthcare settings. A comprehensive search was conducted to identify rigorous empirical studies focused on the link between interior lighting conditions with health outcomes in the healthcare environment. For a comprehensive review of the existing literature, a four-phased methodology was employed including literature search, screening, and selection. Literature appraisals were conducted to determine the relevance and quality of evidence for each study identified. In addition, using a thematic analysis, patterns were identified, analyzed, and interpreted within the literature review. Accordingly, the results were organized into two main groups interventional human subjects and simulation-based studies. Despite evidence that natural light influences human health and happiness, a synthesis of reviewed studies suggests that the evidence for the benefits of artificial lighting in healthcare settings is less conclusive, with potential factors including variations in lighting design, inconsistent implementation of lighting interventions, and differing sample populations across studies. We conclude with an executive summary suggesting that future research should use standardized metrics and methods to focus on bridging the gap between theoretical understanding and practical application in lighting design for healthcare environments. Collaboration among architects, designers, lighting experts, and healthcare professionals can address these factors contributing to building a stronger evidence-based design for the benefits of artificial lighting in healthcare settings.

An integrated approach for understanding global earthquake patterns and enhancing seismic risk assessment

Earthquakes, as intricate natural phenomena, profoundly impact lives, infrastructure, and the environment. While previous research has explored earthquake patterns through data analysis methods, there has been a gap in examining the time intervals between consecutive earthquakes across various magnitude categories. Given the complexity and vastness of seismic data, this study aims to provide comprehensive insights into global seismic activity by employing sophisticated data analysis methodologies on a century-long dataset of seismic events. The four-phase methodology encompasses exploratory data analysis (EDA), temporal dynamics exploration, spatial pattern analysis, and cluster analysis. The EDA serves as the foundational step, providing fundamental insights into the dataset's attributes and laying the groundwork for subsequent analyses. Temporal dynamics exploration focuses on discerning variations in earthquake occurrences over time. Spatial analysis identifies geographic regions with heightened earthquake activity and uncovers patterns of seismic clustering. K-means clustering is employed to delineate distinct earthquake occurrence clusters or hotspots based on geographical coordinates. The study's findings reveal a notable increase in recorded earthquakes since the 1960s, peaking in 2018. Distinct patterns in seismic activity are linked to factors such as time, human activities, and plate boundaries. The integrated approach enriches understanding of global earthquake trends and patterns, contributing to improved seismic hazard assessments, early warning systems, and risk mitigation efforts.

A Framework for Guiding the Design of Interactive Systems for Communication through Activities in Higher Learning Institutions

Human-computer interaction is paramount in the process of designing interactive-computer systems with high capabilities of guaranteeing users’ satisfaction. However, lack of suitable frameworks complicates their effective design. This study investigated suitable frameworks capable of guiding appropriate design of interactive systems for communication through activities (ISCA) in higher learning institutions in Tanzania. These systems are necessary for augmenting communication thereby overcoming persistent challenges related to their reliance on face-to-face, phone-based, and social networking websites communications. This study used a thorough user centred design approach where a framework for human activity design-centred (HADC) was employed. Authors qualitatively analysed data on communication and interaction issues from participants involved. Design science research was then combined with activity theory to develop a four-phased methodology which was used to design an intended human activity interactive communication (HAIC) framework. The findings confirmed that interactions design techniques based on human-activities undertaken are fundamental in designing communication frameworks capable of guiding suitable design of interactive systems in a particular setting. In the context of this study, and with ISCA need, the obtained HAIC framework was found to be appropriate. This study found out that the current user-centred design approach does not explain precisely how designers can employ activity-based interaction design techniques into the design process. Thus, the study contributes to the literature on users’ involvement in interactive systems’ design through the HADC framework provided

Evaluation of Open Geotechnical Knowledge in Urban Environments for 3D Modelling of the City of Seville (Spain)

The need for sustainable urban growth management and preventive conservation of built elements constitute the key factors in today’s increasing demand for the better understanding of subsoil. This information, mainly available from geotechnical surveys, can be integrated into spatial databases to produce operational models. Aiming to generate strategies that enable the visualisation of underground properties in highly anthropised environments, the following four-phase methodology has been proposed: (a) Gathering of geotechnical data; (b) Spatial and statistical analysis; (c) Database design; (d) Generation of 2D and 3D models. Following the aforementioned criteria and using open sources, a spatial dataset of 650 points located within the historical centre of Seville (Spain) has been developed. This urban area is characterised by the heterogeneous distribution of its soil layers and their geotechnical properties. The results show that the application of this method enables a prompt and efficient display of the distribution of geotechnical layers in urban and metropolitan environments, by considering the variations in their mechanical properties. This simplified approach therefore establishes a new starting point for the development of predictive strategies based on approaches of a more complex nature that facilitate the analysis of the interactions between subsoil, buildings, and infrastructures.

International Trauma-Informed Practice Principles for Schools (ITIPPS): expert consensus of best-practice principles

Recognition that schools should be responsive to children who are impacted by adversity and trauma is burgeoning internationally. However, consensus regarding the necessary components of a trauma-informed school is lacking. This research developed expert-informed and internationally relevant best-practice trauma-informed principles for schools. A four-phase methodology included (i) identification of school-relevant trauma-informed practice programs, (ii) inductive thematic analysis of the main concepts underlying programs, (iii) phrasing of draft Principles and (iv) Principle revision and finalisation via a two-round Delphi survey with international experts. Excellent agreement by experts on the importance of all Principles was achieved (round 1 ≥ 86.4%, 2 ≥ 92.3%). The final ‘International Trauma-Informed Practice Principles for Schools’ (ITIPPS) include four Overarching (A–D) and 10 Practice Principles (1–10). Summarised, these include that the school: (A) is student focussed; (B) models compassion and generosity; (C) is understanding and responsive; (D) incorporates recognition of their First Nations peoples in the school’s ethos: (1) prioritises safety and wellbeing; (2) models positive relationships; (3) provides a positive culture and connects; (4) consults and collaborates; (5) supports vulnerable students; (6) teaches social and emotional learning; (7) provides trauma-informed practice training; (8) is predictable yet flexible; (9) identifies and nurtures strengths and (10) reflects, changes and grows. The ITIPPS provide clear guidance for education sectors, schools and other settings about appropriate learning environments for children and young people impacted by trauma. Research is now underway in Western Australian schools to pilot test the feasibility and impact of using the ITIPPS within a framework (thoughtfulschools.org.au) to establish trauma-informed schools.

Investigation of barriers and mitigation strategies to blockchain technology implementation in construction industry: an interpretive structural modeling approach.

Blockchain technology has emerged as a promising solution for enhancing transparency, efficiency, and security in various industries, including construction. However, implementing blockchain in the construction industry faces several barriers that hinder its widespread adoption. To fill this gap, this study is aimed at identifying and discussing the key barriers to implementing blockchain in the construction industry. The study employs a four-phase methodology. In the first phase, barriers are identified through a comprehensive literature review and supplemented with semi-structured interviews with different stakeholders, identifying critical barriers. The second phase involves data collection from ten construction experts, while the third phase utilizes the interpretive structural modeling (ISM) technique for data analysis. In the fourth phase, ten effective strategies are formulated to tackle the identified barriers and their interdependencies. The results reveal the overall structure of barriers and classify them into four groups based on driving and dependence power using the cross-impact matrix multiplication applied to the classification (MICMAC) technique. The identified barriers include a lack of collaboration in consortium formation, insufficient capacity and performance, and regulatory uncertainty, which hinders blockchain implementation in the construction industry. The research findings provide valuable information for policymakers on the overall structure with barriers and implementation in India and other countries with similar situations. Additionally, the management implications of the results were analyzed to facilitate the effective implementation of measures to overcome the obstacles and increase the level of blockchain adoption in the construction industry.

Towards Trustworthy Myopia Detection: Integration Methodology of Deep Learning Approach, XAI Visualization, and User Interface System

Myopia, a prevalent vision disorder with potential complications if untreated, requires early and accurate detection for effective treatment. However, traditional diagnostic methods often lack trustworthiness and explainability, leading to biases and mistrust. This study presents a four-phase methodology to develop a robust myopia detection system. In the initial phase, the dataset containing training and testing images is located, preprocessed, and balanced. Subsequently, two models are deployed: a pre-trained VGG16 model renowned for image classification tasks, and a sequential CNN with convolution layers. Performance evaluation metrics such as accuracy, recall, F1-Score, sensitivity, and logloss are utilized to assess the models' effectiveness. The third phase integrates explainability, trustworthiness, and transparency through the application of Explainable Artificial Intelligence (XAI) techniques. Specifically, Local Interpretable Model-Agnostic Explanations (LIME) are employed to provide insights into the decision-making process of the deep learning model, offering explanations for the classification of images as myopic or normal. In the final phase, a user interface is implemented for the myopia detection and XAI model, bringing together the aforementioned phases. The outcomes of this study contribute to the advancement of objective and explainable diagnostic methods in the field of myopia detection. Notably, the VGG16 model achieves an impressive accuracy of 96%, highlighting its efficacy in diagnosing myopia. The LIME results provide valuable interpretations for myopia cases. The proposed methodology enhances transparency, interpretability, and trust in the myopia detection process.

Integrating Industry 4.0 and Total Productive Maintenance for global sustainability

PurposeThe integration of Total Productive Maintenance (TPM) and Industry 4.0 (I4.0) is an emerging model, and the global pressure of various stakeholders raises scepticism of any emerging model towards providing sustainability. Therefore, this research aims to identify and rank the potential significant drivers of an integrated model of I4.0 and TPM to guide manufacturing enterprises towards sustainability.Design/methodology/approachThis research follows a four-phase methodology including literature review and expert opinion to select the sustainability indicators and I4.0-integrated TPM key drivers, followed by employing the analytic hierarchy process approach for weight determination of sustainability indicators. The research then deploys the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to prioritise the I4.0-integrated TPM key drivers based on their effect on various sustainability indicators. Finally, a sensitivity analysis is conducted to check the robustness of the TOPSIS.FindingsThe findings establish the top five most influential key drivers of an I4.0-integrated TPM system, which include top management support, formal I4.0 adoption program, mid-management involvement and support, solid TPM baseline knowledge and high engagement of the production team. These top drives can lead manufacturing firms towards sustainability.Research limitations/implicationsThe digitalisation of shop floor practices, such as TPM, could be adapted by shop floor managers and policymakers of manufacturing companies to deliver sustainability-oriented outcomes. In addition, this research may aid decision-makers in the manufacturing sector in identifying the most important drivers of I4.0 and TPM, which will assist them in more effectively implementing an integrated system of I4.0 and TPM to practice sustainability. The scope of TPM applicability is wide, and the current research is limited to manufacturing companies. Therefore, there is a huge scope for developing and testing the integrated system of I4.0 and TPM in other industrial settings, such as the textile, food and aerospace industries.Originality/valueThis research makes a first-of-its-kind effort to examine how an I4.0-integrated TPM model affects manufacturing companies' sustainability and how such effects might be maximised.

Temporal impacts of road safety interventions: A structural-shifts-based method for road accident mortality analysis

Extensive prior research has statistically analyzed the impact of infrastructural, policy, and environmental factors on road accidents, injuries, and mortalities. Most of these studies assumed long-term temporal stability in road safety data. These studies were later criticized for ignoring structural shifts in data over time caused by varying systemic influences such as socioeconomic and environmental factors, as well as major changes to road safety rules and networks. In this work, we proposed a novel four-phase methodology that identifies structural shifts or breaks in the road safety data and subsequently evaluates the role of various factors (including road safety interventions) in causing these breaks. The method is generalized, allowing different modeling bases and assumptions on the underlying data distribution. To demonstrate the merits of this methodology, we used it to investigate road accident mortality patterns in the Eastern Province of Saudi Arabia and its subregions for the period 2010–2020, when a series of road safety interventions were introduced. The case study analysis revealed the varying impact of these interventions at both the provincial and governorate levels. These results can be used to evaluate the efficacy of road safety interventions. The lessons learned can help to develop more robust road safety management programs.

Asmaul Husna Mobile Application (AHMA): Foundation of the Prototype Design and Development

Asmaul Husna Mobile Application (AHMA) is a prototype mobile application based on an interaction model intended for use in heutagogy with undergraduates. This app was created and designed to increase students’ knowledge, perceived awareness, and perceived motivation for the learning material. When learners are assisted with signalling or cueing to focus their attention on the most appropriate resources, they demonstrate improved learning performance and significantly reduced cognitive load. The signalling principle’s effectiveness in enhancing learning outcomes by emphasising correspondences between text and images has been increasingly confirmed by empirical research. AHMA’s heutagogic design encourages students to explore, connect, and reflect through their self-learning process. By promoting aspects of the learning experience that are conducive to lecture or self-paced learning. This project involved a four-phase methodology which includes planning, design, development, and evaluation. According to the pre-and post-testing and heuristic evaluation results, AHMA significantly improved the learning experience and user interaction in a mobile learning environment.

Barriers to service recovery in B2B markets: a TISM approach in the context of IT-based services

PurposeTo address service failure issues, scholars have recognized several service recovery (SR) mechanisms. However, there exist many barriers that retard an effective SR process, and those often lead to negative consequences. Although this is a generic problem applicable across markets, it becomes critical in B2B markets, as they deal with high impact and corporate customers. The purpose of this paper is to identify these SR barriers in B2B markets and explore their linkages toward identifying the key driving barriers.Design/methodology/approachA four-phased methodology to address this call in the IT services industry was used. First, the identification phase led to the selection of 27 barriers based on literature. Second, in the prioritization phase, 12 salient barriers were prioritized with the help of an expert panel. Third, in the modelling phase, the total interpretive structural modelling (TISM) technique was used to examine the mutual influences among these barriers toward modelling their linkages using a digraph. Finally, in the classification phase, these barriers were graphically plotted and classified into four quadrants based on their dependence and driving powers by using Matriced Impacts Croisés Multiplication Appliquée á un Classement (MICMAC) analysis.FindingsTwo strategic barriers, namely, technology obsolescence and poor top management commitment, presented the strongest challenges against effective SR, as they were least dependent on other barriers and exerted strong influence over the operational barriers to executing SR.Practical implicationsBecause the strategic level barriers, exerting strong influence against effective SR, arise from the strategic choices of board and the C-suite, this paper helps them anticipate the recovery-related after effects of their choices, thus helping them in making better decisions.Originality/valueThis paper contributes to research as it is first of its kind to explore and link SR barriers in B2B markets.

Determining and evaluating socially sustainable supply chain criteria in agri-sector of developing countries: insights from West Africa cashew industry

Social sustainability issues such as labour rights concern in the agricultural sector receive significant attention from several stakeholders. The role of small and medium scale enterprises (SMEs) that dominate the sector’s supply chain in developing countries remains critical in implementing initiatives to address these issues. Through a four-phase methodology, this study proposed the criteria of a socially sustainable supply chain (SSSC) guided by ISO 26000 and based on empirical evidence from the cashew industry. Subsequently, based on the Best worst method and Grey relational analysis, the criteria are evaluated by cashew manufacturing SME managers to determine how SSSC initiatives can be implemented. The study shows that food safety, labour and work condition, traceability, and child and force/prison labour emerge in order of importance as a pathway for implementation of SSSC. The study also provides insight into achieving SSSC among various manufacturing SMEs and understanding their assessed SSSC performance. The study suggests that agricultural sector SMEs that implement SSSC practices through social compliance or collaborations are more aware of the implementation challenges. On the other hand, SMEs that generate SSSC practices may perceive their social sustainability performance in the supply chain much higher than adopters who meet customers’ sustainability requirements.

Análisis de factibilidad para el incremento de capacidad instalada en el almacén del centro de distribución de una empresa en Cancún

The project was conducted at the distribution center/warehouse of Sigma Alimentos Cancun, a frozen-food warehouse and distribution company. The company aimed at determining its actual storage capacity and presenting a proposal to increase its cold storage capacity to meet the demands of the coming years. A four-phase project planning and evaluation methodology was used: market study, technical study, financial and economic study, and results. During the project, these three studies and their results were analyzed. The results of this research suggest to expand the warehouse by increasing its installed capacity to 67,500 kilograms. This expansion will allow the correct operation of the distribution center/warehouse during a minimum of 5 years and even for 9 years.

Methodological framework for the implementation of circular economy in urban systems

Urban areas are hubs for innovation, economic activity and growth that hugely influence the development of our society, making those areas important drivers in the global transition towards circular economy. Although global policies are necessary to set the general ambition, local interventions are crucial to realize it. This paper presents a methodological framework aimed at facilitating the understanding and application of circular economy strategies in urban systems, that being a single city, or urban regions. The framework is conceived as a flexible structure that contains a network of potential decisions, describing different convergence and divergence points, and that is meant as a supporting tool for future urban circular economy implementation initiatives.After a literature review and an analysis of specific case studies on urban circular economy implementation, a four-phased methodology is proposed where the territory is explored in order to identify and select initiatives in the areas with greatest potential for circular economy. Actions needed to implement the selected initiatives are finally summarized in a roadmap. Each phase contains recommendations of different tasks to complete and available tools for achieving the expected results. Different approaches to adopt in the application of this methodology are discussed as well, such as production-based vs. consumption-based, and top-down vs. bottom-up. Special emphasis is put on the importance of involving local agents, in order to obtain specific and validated proposals that are adapted to the reality of the territory and the concerns of the stakeholders.Through these comprehensive guidelines, the ultimate goal of this methodology is to help urban systems to foster circular economy principles, and therefore reaffirm their role in addressing and managing global sustainability issues.

Grey-based Multi-Criteria Decision Analysis approach: Addressing uncertainty at complex decision problems

In complex systems, decision makers encounter uncertainty from various sources. In this paper, a new hybrid grey-based Multi-Criteria Decision Analysis (MCDA) approach is proposed to optimize the evaluation space in decision problems that are subject to subjective and objective uncertainty over different types of interrelated criteria. The four-phase methodology begins with the formulation of a decision problem through the analysis of the system of concern, its functionality, and substantial connections among evaluation criteria. The second phase involves the development of grey linguistic scales to handle the uncertainty of human judgements. The third phase integrates the grey linguistic scale, concepts of grey systems theory, and principles of Analytical Network Process to prioritize criteria. Finally, to evaluate and rank alternatives in such a complex setting, Preference Ranking Organization METHod for Enrichment Evaluation II is extended using a grey linguistic scale to articulate subjective uncertainty, grey numbers to account for objective uncertainty, grey operating rules to normalize evaluation measures, and the proposed approach of prioritizing evaluation criteria to establish relative preferences. To demonstrate the viability of the methodology, a case study is presented, in which a strategic decision is made within the context of innovation. To validate the methodology, a comparative analysis is provided.

A methodology to design and redesign services in smart cities based on the citizen experience

Smart cities are urban spaces where massive amounts of data are generated and shared creating an ecosystem of service providers. Translating these opportunities into appropriate citizen services requires diagnosis of citizen’s expectations and a projection of the value that these services can generate for them. This article offers a methodology that provides a systematic approach to understand the interaction between citizens and services aimed to improve the design of smart city services and presents a pilot test. The four-phased methodology results in a description of the service, a model to evaluate it and offers quantitative indicators to operate and to improve the design of the service.

Selection of supplier management policies using clustering and fuzzy-AHP in the retail sector

Supplier development contributes to supply chain integration and performance, thus playing an essential role in any industry. In the retail market, no formal procedures exist for the selection-assignment of supplier development programs, meaning that respective management is complex and unclear given actor and requirement heterogeneities. Multi-criteria techniques are currently used for selection and evaluation processes. However, no models exist that integrate clustering and multi-criteria techniques together to efficiently select-assign supplier development programs. This study proposes a four-phase methodology - diagnosis, program design, assignment, and proposal - that considers supplier clustering through K-means and multi-criteria fuzzification. Additionally, case-study analysis of 149 retail suppliers in Colombia clustered businesses into high (8), medium (32), and low (109) risk sets, each of which was assigned tailored development programs. The obtained results support use of the proposed methodology to improve supply chain performance for organisations with many suppliers requiring development programs.

Dilemma Between Applying Coherent Principle and Signaling Principles In Interactive Learning Media

Background: Studies on developing multimedia content for eLearning have widely been conducted. In previous studies, multimedia learning contents were developed without applying any principles, making them less efficient in supporting user’s mental effort. To this end, this study extends the current state by applying the coherence principle and signaling principle to make the multimedia learning content usable and interesting to use. Furthermore, the paper aims at describing the dilemma when applying both principles together. Materials and Methods: In this study, a four-phase methodology has been used. First, a prototype has been developed by applying the coherence principle and signaling principle. Following the development, five experts were consulted for identifying flaws in the developed prototype through a walkthrough session. Results: Finally, this paper showcases the flaws gathered by the experts for further improvement.