4D Framework Research Articles

From Concept to Community Impact: the ABCD Model's Trailblazing Role in Community Service Programs

Objective: This study aims to develop a unique model for the Community Service Program (CSP) at UIN Imam Bonjol Padang by emphasising the ABCD (Asset-Based Community Development) approach. Methodology: Involving CSP activities from 2016 to 2023, this study aims to enrich the CSP program by integrating innovative strategies in community empowerment. The research adopted the 4D Model (Define, Design, Develop, Disseminate) for model development, by organising the CSP framework. Using a qualitative approach, the research method involved appreciative inquiry, community mapping, field interviews, and surveys in 10 districts/cities in West Sumatra. Analyses of CSP activities reveal distinctive patterns-Thematic CSP, CSP-PPM, and different types of CSP-that impact community empowerment. Results: The findings emphasise the need for structured, sustainable, and tailored CSP programs, with an emphasis on continuity of planning, program stability, and sustained impact for lasting community benefit. This study presents an ABCD pattern of service-learning, emphasising collaboration, asset building, and problem-solving, contained within the 4D framework. Conclusion: The model highlights structured planning stages, debriefing rules, and comprehensive engagement strategies that are essential for the implementation of impactful service-learning.

Blockchain Applications in the Pharmaceutical Industry.

We utilized a 4D framework using ease of implementation, novelty, necessity, and fit of the overall industry to examine the adoption of blockchain technology in the pharmaceutical industry. Based on the 2D framework of difficulty and novelty as driving factors for the development of foundational technologies in the world of business by Iansiti and Lakhani, each application was ranked and scored for the best potential implementation. The potential applications proposed in this paper can be grouped into two main categories. The first category, management, includes best-use cases, such as health records, clinical trials, and inventory systems. The second category, monitoring, highlights cases, such as pharmaceutical products, preventing counterfeits, optimizing supply chains, and addressing prescription misuse and abuse. Each application was ranked by the four metrics in the framework, giving the greatest weight to necessity and ease of implementation. Using the highlighted methodology earlier, the applications for best implementation include Prescription Drug Misuse and Abuse Prevention, Prevention of Counterfeits, Clinical Trial Outcomes, and Smart Contracts. Blockchain technology offers a new and promising solution to the pharmaceutical industry's needs. To promote the most appropriate use, each application of blockchain technology must fit within the framework of necessity, ease of implementation, familiarity amongst stakeholders, and fit of the overall industry. By using the extended framework proposed by Iansiti and Lakhani, we show how blockchain, in all these domains, shows promise to improve pharmaceutical industry performance.

AN INNOVATIVE LEARNING ENVIRONMENT: G-MOOC 4D TO ENHANCE VISUAL IMPAIRMENTS LEARNING MOTIVATION

The proliferation of visual impairment among school-age children in Indonesia has prompted the need for specialized online learning solutions. The G-MOOC 4D platform, a novel Learning Management System (LMS), is designed to address this need by leveraging gamification and artificial intelligence to enhance accessibility for visually impaired users. This study reports on the development and testing of two AI models within the G-MOOC 4D framework: a facial recognition model for secure user authentication and a voice command model for interactive learning. User Acceptance Testing (UAT), conducted with expert users, namely teachers at a special needs school, showed high approval rates for the platform's features. The results show that all metrics, accuracy, precision, and recall reach their optimal values at a distance of 40 cm for face detection. The respective metric scores at that distance, precision: 100%, accuracy: 98%, and recall: 97%. Additionally, the voice command functionality tested achieved a 100% recognition rate, reflecting the platform’s potential to significantly ease the learning process for visually impaired students. The findings underscore the importance of integrating assistive technologies into educational platforms to ensure all students have equal access to learning opportunities.

AUGMENTED INTELLIGENCE: HUMAN-AI COLLABORATION IN THE ERA OF DIGITAL TRANSFORMATION

Augmented Intelligence (AI) combines human and artificial intelligence to enhance decision-making. This paper reviews AI concepts, applications, and collaboration models like human-in-the-loop AI and cognitive computing AI. It examines AI's role in improving human judgment, handling large datasets, and making routine decisions. The paper explores AI's impact on sectors like healthcare through use cases in blood glucose monitoring. It applies the McKinsey 4D framework to implement AI for glucose monitoring. The paper also discusses emerging models like Hybrid Augmented Intelligence (HAI) that integrate human cognition within AI systems for optimal performance. Overall, it underscores AI's potential in complementing human capabilities and driving innovation across industries with responsible design.

Addressing the politics of mission-oriented agricultural innovation systems

Mission-oriented agricultural innovation systems (MAIS) are becoming more prevalent in view of tackling the challenges of agri-food systems transformation. In this perspective, we argue that the politics of MAIS requires more comprehensive and considerable attention in the field, given the contested and deeply normative nature of the direction of innovations in agri-food systems transformation. Literature from development studies, policy sciences, and transition studies is reviewed to inform the perspective. We question the politics of MAIS structured around the dimensions of the 4D framework: directionality, diversity, distribution and democracy. Regarding directionality, MAIS should explicitly consider how power dynamics shape the direction of innovation and future agri-food systems, and to which extent these power dynamics hinder desirable directions. Considering diversity means that MAIS need to stimulate a diversity of transformation pathways; include a diversity of actors, communities and knowledge; and consider roles of both humans and non-humans in transformation. Questions regarding the distribution of resources and effects of innovations across ecosystems and communities imply that MAIS should actively advance just transitions across different scales and geographical contexts. Finally, democratization of MAIS in our view means that the ways in which knowledge and innovations are produced through MAIS should be more democratic and deliberative, though this may be challenging since missions imply strong steering. We stress that these 4D considerations also bring along important implications for the ways policies and research on and in MAIS, and agri-food system transformation more broadly, are considered. Confronting the politics of MAIS is not an easy endeavor, but critical to advance agri-food system transformation in directions that are not only sustainable and transformative, but also socially just and desirable. This requires for agricultural systems researchers to develop awareness on how their work feeds into the politics of MAIS, and conversely is influenced by it.

Authentic Integration of Ethics and AI through Sociotechnical, Problem-Based Learning

Growing awareness of both the demand for artificial intelligence (AI) expertise and of the societal impacts of AI systems has led to calls to integrate learning of ethics alongside learning of technical skills in AI courses and pathways. In this paper, we discuss our experiences developing and piloting the TechHive:AI curriculum for high school youth that integrates AI ethics and technical learning. The design of the curriculum was guided by the following pedagogical goals: (1) to respond to the capacity-building need for critical sociotechnical competencies in AI workforce pathways; and (2) to broaden participation in AI pathways through intentional instructional design to center equity in learning experiences. We provide an overview of the 30-hour learning sequence’s instructional design, and our “4D Framework,” which we use as a heuristic to help students conceptualize and inspect AI systems. We then provide a focused description of one of three 8-hour modules that make up the sequence. Finally, we present evidence of promise from an exploratory study of TechHive:AI with a small sample of students, and discuss insights from implementation, including from our use of established resources for AI learning within the learning sequence as well as those created by our team.

How Can Learnings From the Automotive Industry Prevent Deterioration of Maternal Health During a Pandemic like COVID-19

What does a shop floor of automobile companies, pregnant women, and public health have in common? Any senile person wouldn’t be able to draw a correlation. A creative genius may suspect a safety video of sorts explaining the safety hazards of the shop floor to pregnant employees, but that’s by a longshot. However, when our cross-functional team sat together over some tea and cake and pondered how to pandemic-proof expecting mothers, the link between the three became more evident. While the direct effects of COVID-19 on maternal health are documented, the literature on indirect effects is still non-substantial. Thus, we decided to look at the previous pandemics and endemics that have ravaged humanity, like Ebola, to identify the barriers to maternal health. Mistrust and fear, constraints in the health infrastructure and poor communication were identified as amongst the top three obstacles to maternal health1. What made it even more worrisome is that there was a sharp reduction in regular visits to health centers during the endemic in some countries like Guinea, and these numbers sadly stagnated post the endemic2. While one may argue that these events can’t be extrapolated to other nations, it is only wise to take the worst-case scenario and build an antifragile action plan. Popularly called the Eight Disciplines to problem-solving or 8D approach3, it started in the shop floors of Ford and extended to ship operations. Shown in Fig-1, it covers not only the immediate action but also the corrective and preventive action to any problem. We propose building an action plan inspired by this approach to ensure that the maternal health of pregnant women and their children are not impacted by COVID-19 and its different variants. Our Framework, as an ode to its inspiration, is called the 4D Framework and is as follows,

Validation of 4D Components for Measuring Quality of the Public Health Data Collection Process: Elicitation Study.

BackgroundIdentification of the essential components of the quality of the data collection process is the starting point for designing effective data quality management strategies for public health information systems. An inductive analysis of the global literature on the quality of the public health data collection process has led to the formation of a preliminary 4D component framework, that is, data collection management, data collection personnel, data collection system, and data collection environment. It is necessary to empirically validate the framework for its use in future research and practice.ObjectiveThis study aims to obtain empirical evidence to confirm the components of the framework and, if needed, to further develop this framework.MethodsExpert elicitation was used to evaluate the preliminary framework in the context of the Chinese National HIV/AIDS Comprehensive Response Information Management System. The research processes included the development of an interview guide and data collection form, data collection, and analysis. A total of 3 public health administrators, 15 public health workers, and 10 health care practitioners participated in the elicitation session. A framework qualitative data analysis approach and a quantitative comparative analysis were followed to elicit themes from the interview transcripts and to map them to the elements of the preliminary 4D framework.ResultsA total of 302 codes were extracted from interview transcripts. After iterative and recursive comparison, classification, and mapping, 46 new indicators emerged; 24.8% (37/149) of the original indicators were deleted because of a lack of evidence support and another 28.2% (42/149) were merged. The validated 4D component framework consists of 116 indicators (82 facilitators and 34 barriers). The first component, data collection management, includes data collection protocols and quality assurance. It was measured by 41 indicators, decreased from the original 49% (73/149) to 35.3% (41/116). The second component, data collection environment, was measured by 37 indicators, increased from the original 13.4% (20/149) to 31.9% (37/116). It comprised leadership, training, funding, organizational policy, high-level management support, and collaboration among parallel organizations. The third component, data collection personnel, includes the perception of data collection, skills and competence, communication, and staffing patterns. There was no change in the proportion for data collection personnel (19.5% vs 19.0%), although the number of its indicators was reduced from 29 to 22. The fourth component, the data collection system, was measured using 16 indicators, with a slight decrease in percentage points from 18.1% (27/149) to 13.8% (16/116). It comprised functions, system integration, technical support, and data collection devices.ConclusionsThis expert elicitation study validated and improved the 4D framework. The framework can be useful in developing a questionnaire survey instrument for measuring the quality of the public health data collection process after validation of psychometric properties and item reduction.

Development of animation-based learning media to increase student’s motivation in learning physics

The difficulty of students in understanding concepts in physics has a significant effect on students’ motivation. The purpose of this study is to develop animation media that make students easier to understand the abstract concept of Newton’s law and find out students’ responses to the use of instructional media. The 4D framework is used to develop animation-based learning media, which consists of four-phase, namely: define, design, develop, and disseminate. The instruments used are a questionnaire and interviews. The results showed that (1) Based on expert judgment Animation-based learning media was appropriate and feasible to be implemented in the physics class; (2) used of media animation in learning physics could facilitate students in understanding the abstract concept of Newton’s Law, especially in describing the direction of the force and gave opportunity to the student’s to increase their motivation to learning physics where average score of the motivation was 70% in the “High” category.

Application of a four-dimensional framework to evaluate the quality of the HIV/AIDS data collection process in China

ObjectiveTo qualitatively evaluate the quality of the data collection process used by the Chinese national HIV/AIDS data repository (CRIMS), using a four-dimensional (4D) framework. The process is vital for the acquisition of high-quality data for ending the HIV/AIDS epidemic in China. MethodsThe study was carried out in China from September 2014 to April 2015. Stratified convenient sampling was conducted to recruit 28 study participants including health administrators, public health professionals and clinicians. Data were collected through semi-structured interviews with the participants and from field observations in six hospitals. Content analysis was conducted following the 4D Framework. Results61 percent of the facilitators and 74 percent of the barriers of the 4D Framework were confirmed in the CRIMS data collection process. The CRIMS achieved better-quality data collection management. The perceived gaps primarily included: impractical data collection protocol and invalid quality assessment mechanism for data collection management; weak leadership and unsupportive organizational policy for data collection environment; poor communication and job fatigue for data collection personnel; and inflexibility and inaccessibility of data collection system. Areas for improvement included: engaging frontline staff in the design of data collection protocol, standardizing quality assurance procedures, strengthening leadership, recognizing data collector’s contributions, and meeting end-users’ needs for the CRIMS. ConclusionThe findings generated knowledge about the quality of the CRIMS data collection process. The 4D Framework has potential as an evaluation tool for decision-makers on the improvement of the public health data collection process.

The adoption of 4D BIM in the UK construction industry: an innovation diffusion approach

PurposeBritish construction industry KPI data collected over recent years shows a trend in projects exceeding their time schedules. In 2013, the UK Government set a target for projects timeframes to reduce by 50 per cent. Proposed interventions included more rapid project delivery processes, and consistent improvements to construction delivery predictions, deployed within the framework of 4D Building Information Modelling (BIM). The purpose of this paper is to use Rogers’ Innovation Diffusion theory as a basis to investigate how this adoption has taken place.Design/methodology/approachIn total, 97 construction planning practitioners were surveyed to measure 4D BIM innovation take-up over time. Classic innovation diffusion research methods were adopted.FindingsResults indicated an increasing rate of 4D BIM adoption and reveal a time lag between awareness and first use that is characteristic of this type of innovation.Research limitations/implicationsUse of a non-probability sampling strategy prevents the results being generalisable to the wider construction population. Future research directions and methods are suggested, including qualitative investigations into decision-making processes around 4D BIM, and case studies exploring the consequences of 4D BIM adoption.Practical implicationsRecommendations of how to facilitate the adoption of 4D BIM innovation are proposed, which identify the critical aspects of system compatibility and safe trialling of the innovation.Originality/valueThis paper reinforces 4D BIM as an innovation and records its actual UK industry adoption rate using an accepted diffusion research method. By focusing on UK industry-wide diffusion the work also stands apart from more typical research efforts that limit innovation diffusion exploration to individual organisations.

Geochemical characterisation of xenotime formation environments using U-Th

Xenotime (YPO4) is a trace component in many metasedimentary and some igneous rocks, altered rocks and many hydrothermal ore assemblages, where it forms in response to a range of different processes from igneous crystallisation to low-temperature early diagenesis. Due to its wide range of formation temperatures, its suitability and isotopic robustness for reliable U-Pb geochronology, dissolution/precipitation characteristics during overprint events, and widespread occurrence, it is one of the most valuable minerals for U-Pb geochronology leading to four-dimensional (4D) studies of many terranes. The formation environment of xenotime can be deduced from careful petrography and reliable U-Pb in-situ geochronology within a 4D framework. Further, xenotime is a physically robust mineral during sediment transport and may carry distinctive geochemical fingerprints, including age, to secondary environments.From published works, we review the U-Th-contents of well-characterised examples of xenotime formation to provide chemical fingerprints, which assist in identifying the xenotime formation environment. Although the U-Th characteristics of xenotime from all formation environments show considerable overlap, we make the following observations which may be distinctive of some ore formation environments: (i) hydrothermal xenotime formed from low salinity ore fluids (e.g. iron ore and orogenic gold deposits) trend to have the lowest U-contents (<100ppm U) and U/Th (<4), in contrast to xenotime formed from higher salinity ore fluids (Sn-W and base metal deposits); (ii) xenotime associated with unconformity-related U-ores have the highest U-contents at U/Th>10; (iii) diagenetic xenotime has the most variable and highest U/Th, whereas (iv) xenotime from Precambrian orogenic gold deposits has the least variable and lowest U/Th.Petrogenetic inferences from these observations for ore deposit research and exploration include: (i) support for the homogenising effect of the crustal scale of the fluid system in Precambrian orogenic gold deposits, and (ii) the potential for distinctive U-Th geochemical fingerprints for U ores and some Au ores. These characteristics may be reflected in detrital xenotime grains recovered from routine exploration sampling programs.

Four-Dimensional Graded Consciousness.

Both the multidimensional phenomenon and the polysemous notion of consciousness continue to prove resistant to consistent measurement and unambiguous definition. This is hardly surprising, given that there is no agreement even as regards the most fundamental issues they involve. One of the basic disagreements present in the continuing debate about consciousness pertains to its gradational nature. The general aim of this article is to show how consciousness might be graded and multidimensional at the same time. We therefore focus on the question of what it is, exactly, that is or could be graded in cases of consciousness, and how we can measure it. Ultimately, four different gradable aspects of consciousness will be described: quality, abstractness, complexity and usefulness, which belong to four different dimensions, these being understood, respectively, as phenomenal, semantic, physiological, and functional. Consequently, consciousness may be said to vary with respect to phenomenal quality, semantic abstraction, physiological complexity, and functional usefulness. It is hoped that such a four-dimensional approach will help to clarify and justify claims about the hierarchical nature of consciousness. The approach also proves explanatorily advantageous, as it enables us not only to draw attention to certain new and important differences in respect of subjective measures of awareness and to justify how a given creature may be ranked higher in one dimension of consciousness and lower in terms of another, but also allows for innovative explanations of a variety of well-known phenomena (amongst these, the interpretations of blindsight and locked-in syndrome will be briefly outlined here). Moreover, a 4D framework makes possible many predictions and hypotheses that may be experimentally tested (We point out a few such possibilities pertaining to interdimensional dependencies).

Whole-body direct 4D parametric PET imaging employing nested generalized Patlak expectation–maximization reconstruction

Whole-body (WB) dynamic PET has recently demonstrated its potential in translating the quantitative benefits of parametric imaging to the clinic. Post-reconstruction standard Patlak (sPatlak) WB graphical analysis utilizes multi-bed multi-pass PET acquisition to produce quantitative WB images of the tracer influx rate Ki as a complimentary metric to the semi-quantitative standardized uptake value (SUV). The resulting Ki images may suffer from high noise due to the need for short acquisition frames. Meanwhile, a generalized Patlak (gPatlak) WB post-reconstruction method had been suggested to limit Ki bias of sPatlak analysis at regions with non-negligible 18F-FDG uptake reversibility; however, gPatlak analysis is non-linear and thus can further amplify noise. In the present study, we implemented, within the open-source software for tomographic image reconstruction platform, a clinically adoptable 4D WB reconstruction framework enabling efficient estimation of sPatlak and gPatlak images directly from dynamic multi-bed PET raw data with substantial noise reduction. Furthermore, we employed the optimization transfer methodology to accelerate 4D expectation–maximization (EM) convergence by nesting the fast image-based estimation of Patlak parameters within each iteration cycle of the slower projection-based estimation of dynamic PET images. The novel gPatlak 4D method was initialized from an optimized set of sPatlak ML-EM iterations to facilitate EM convergence. Initially, realistic simulations were conducted utilizing published 18F-FDG kinetic parameters coupled with the XCAT phantom. Quantitative analyses illustrated enhanced Ki target-to-background ratio (TBR) and especially contrast-to-noise ratio (CNR) performance for the 4D versus the indirect methods and static SUV. Furthermore, considerable convergence acceleration was observed for the nested algorithms involving 10–20 sub-iterations. Moreover, systematic reduction in Ki % bias and improved TBR were observed for gPatlak versus sPatlak. Finally, validation on clinical WB dynamic data demonstrated the clinical feasibility and superior Ki CNR performance for the proposed 4D framework compared to indirect Patlak and SUV imaging.

Data-centric view of knowledge in information systems literature review

With its multidisciplinary nature, Information Systems (IS) offers more opportunities to its researchers. However, a review of prior relevant literature may not be easy for a new IS researcher to understand many disciplines that could be involved. The keyword 'knowledge' is a case in point in that fields related to IS research can range from Information Technology (IT) to Business and Economics. This paper aims to investigate how to understand the concept of knowledge from an IT data-centric point of view and this has not been done before to the best of our knowledge. It develops a new 4D framework to analyse the outcomes collected from relevant IS literature. It also demonstrates how the end-result from the 4D analysis can be extended to understand the concept of knowledge in Business and Economics in a multidisciplinary manner.

Data-Centric View of Knowledge in Information Systems Literature Review

With its multidisciplinary nature, Information Systems (IS) offers more opportunities to its researchers. However, a review of prior relevant literature may not be easy for a new IS researcher to understand many disciplines that could be involved. The keyword 'knowledge' is a case in point in that fields related to IS research can range from Information Technology (IT) to Business and Economics. This paper aims to investigate how to understand the concept of knowledge from an IT data-centric point of view and this has not been done before to the best of our knowledge. It develops a new 4D framework to analyse the outcomes collected from relevant IS literature. It also demonstrates how the end-result from the 4D analysis can be extended to understand the concept of knowledge in Business and Economics in a multidisciplinary manner.

Multi-natural inflation in supergravity

We show that the recently proposed multi-natural inflation can be realized within the framework of 4D ${\cal N}=1$ supergravity. The inflaton potential mainly consists of two sinusoidal potentials that are comparable in size, but have different periodicity with a possible non-zero relative phase. For a sub-Planckian decay constant, the multi-natural inflation model is reduced to axion hilltop inflation. We show that, taking into account the effect of the relative phase, the spectral index can be increased to give a better fit to the Planck results, with respect to the hilltop quartic inflation. We also consider a possible UV completion based on a string-inspired model. Interestingly, the Hubble parameter during inflation is necessarily smaller than the gravitino mass, avoiding possible moduli destabilization. Reheating processes as well as non-thermal leptogenesis are also discussed.

Turbulence accelerating cosmology from an inhomogeneous dark fluid

Specific dark energy models with a linear inhomogeneous time-dependent equation of state, within the framework of 4d Friedman-Robertson-Walker (FRW) cosmology, are investigated. It is demonstrated that such 4d inhomogeneous fluid models may lead to a turbulence FRW cosmology. Both one-component and two-component models from 4d inhomogeneous dark fluid models are considered. In the one-component model the universe may develop from a viscous era with, for instance, a constant bulk viscosity, into a turbulent era. In the two-component model the fluid can be decomposed into two components, one non-turbulent (ideal) and another turbulent part, obeying two different equations of state. Conditions for the appearance of the turbulent dark energy universe in terms of the parameters in the equation of state (EoS) without introducing the turbulence concept explicitly are are obtained. An equivalent description in terms of an inhomogeneous fluid for the viscous Little Rip (LR) cosmology is also developed.

Rip brane cosmology from 4d inhomogeneous dark fluid universe

Specific dark energy models with linear inhomogeneous time-dependent equation of state, within the framework of 4d Friedman-Robertson-Walker (FRW) cosmology, are investigated. It is demonstrated that the choice of such 4d inhomogeneous fluid models may lead to a brane FRW cosmology without any explicit account of higher dimensions at all. Effectively, we thus obtain a brane dark energy universe without introducing the brane concept explicitly. Several examples of brane Rip cosmology arising from 4d inhomogeneous dark fluid models are given.