Search results

Problem-solving, systems thinking and design thinking are disciplines practiced by all human beings and is also innate in other living objects with limited use of symbolic structures. They are necessary to achieve the goals and survive. Interpretation of problem-solving as a change in equilibrium makes it applicable throughout the inanimate world. The aim is to describe the proposed empirical systems theory that integrates problem-solving and systems thinking through design thinking.

A brief historical background describes why comprehensive empirical systems theory has not been attempted before except as a restricted version in engineering systems. The methodology of the general theory follows that of conventional science, but with systemic content. Interference by required mental or physical product/systems changes states which is subject to discussion, creativity, innovation and inspiration accomplished by iteration as necessary.

A problem-solving structure has been created, which is implemented in a methodical way to aid the innate ability of individuals and organisations, and is open to modifications and the use of quantitative methods. Processed natural language allows for implementation at the operational level.

The proposed systems theory is an empirical theory that uses the structural properties of parts of the world. The integration makes “systems” the driver of change of state and offers fundamental concepts. The implementation shown in Figure 2 is methodical and can be applied by individuals and organisations subject to peer review and development. The method clarifies the roles of product/artifact and systems designers.

The purpose of this paper is to analyse the measures that could be taken to combat the risk of economic crime in the aftermath of South African disasters.

This paper used secondary sources including, but not limited to, institutional reports, newspaper articles and peer-reviewed academic journal articles.

The COVID-19 pandemic was used as an example in this paper to discuss the susceptibility of post-disaster funding to the risk of economic crime and to assess how the South African government attempted to combat this risk during the pandemic. The Auditor-General of South Africa (AGSA) conducted a real-time audit of the government’s essential COVID-19 initiatives in collaboration with the newly established Fusion Centre. Through their collaborative efforts, they successfully identified mismanaged funds, facilitated the recovery thereof and prosecuted individuals and entities involved. This paper found that to proactively combat economic crime in future post-disaster events, the collaborative use of the AGSA and the Fusion Centre, in conjunction with existing bodies established under the Disaster Management Act, should be considered.

This paper contributes to the body of knowledge in disaster risk management and forensic accountancy. As the frequency of disasters is expected to increase in the future, so will the economic crime risk associated with post-disaster funding. This paper demonstrates that post-disaster funding is especially susceptible to the risk of economic crime and it is therefore important to research methods to combat this problem and prevent further losses.

Amid rapid technological progress, the construction industry is embracing Construction 4.0, redefining work practices through emerging technologies. However, the implications of Construction 4.0 technologies to enhancing well-being are still poorly understood. Particularly, the challenge lies in selecting technologies that critically contribute to well-being enhancement. Therefore, this study aims to evaluate the implications of Construction 4.0 technologies to enhancing well-being.

A list of Construction 4.0 technologies was identified from a national strategic plan on Construction 4.0, using Malaysia as a case study. Fourteen construction industry experts were selected to evaluate the implications of Construction 4.0 technologies on well-being using fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The expert judgment was measured using linguistic variables that were transformed into fuzzy values. Then, the collected data was analyzed using the following analyses: fuzzy TOPSIS, Pareto, normalization, sensitivity, ranking performance and correlation.

Six Construction 4.0 technologies are critical to enhancing well-being: cloud & real-time collaboration, big data & predictive analytics, Internet of Things, building information modeling, autonomous construction and augmented reality & virtualization. In addition, artificial intelligence and advanced building materials are recommended to be implemented simultaneously as a very strong correlation exists between them.

The novelty of this study lies in a comprehensive understanding of the implications of Construction 4.0 technologies to enhancing well-being. The findings can assist researchers, industry practitioners and policymakers in making well-informed decisions to select Construction 4.0 technologies when targeting the enhancement of the overall well-being of the local construction industry.

In the evolution process of building construction accidents, there are key nodes of risk change. This paper aims to quickly identify the key nodes and quantitatively assess the node risk. Furthermore, it is essential to propose risk accumulation assessment method of building construction.

Authors analyzed 419 accidents investigation reports on building construction. In total, 39 risk factors were identified by accidents analysis. These risk factors were combined with 245 risk evolution chains. Based on those, Gephi software was used to draw the risk evolution network model for building construction. Topological parameters were applied to interpret the risk evolution network characteristic.

Combining complex network with risk matrix, the standard of quantitative classification of node risk level is formulated. After quantitative analysis of node risk, 7 items of medium-risk node, 3 items of high-risk node and 2 items of higher-risk nodes are determined. The application results show that the system risk of the project is 44.67%, which is the high risk level. It can reflect the actual safety conditions of the project in a more comprehensive way.

This paper determined the level of node risk only using the node degree and risk matrix. In future research, more node topological parameters that could be applied to node risk, such as clustering coefficients, mesoscopic numbers, centrality, PageRank, etc.

This article can quantitatively assess the risk accumulation of building construction. It would help safety managers could clarify the system risk status. Moreover, it also contributes to reveal the correspondence between risk accumulation and accident evolution.

This study comprehensively considers the likelihood, consequences and correlation to assess node risk. Based on this, single-node risk and system risk assessment methods of building construction systems were proposed. It provided a promising method and idea for the risk accumulation assessment method of building construction. Moreover, evolution process of node risk is explained from the perspective of risk accumulation.

Conventional approaches such as Data Envelopment Analysis (DEA) and Fuzzy Data Envelopment Analysis (FDEA) cannot effectively account for uncertainty, which can lead to imprecise decision-making. Furthermore, these methods frequently rely on precise numbers, ignoring the inherent uncertainty of real-world data. To address this gap, the research question arises: How can we develop a methodology that combines Z-number theory and FDEA to provide a comprehensive assessment of residency preferences in European countries while accounting for uncertainty in information reliability? The proposed methodology aims to fill this gap by incorporating Z-number theory and FDEA.

The proposed study assesses residency preferences across 39 European countries, focusing on key factors like environment, sustainability, technology, education, and development, which significantly influence individuals' residency choices. Unlike conventional DEA and FDEA approaches, the proposed method introduces a novel consideration: dependability. This inclusion aims to refine decision-making precision by accounting for uncertainties related to data reliability. The proposed methodology utilizes an interval approach, specifically employing the a-cut approach with interval values in the second step. Unlike using crisp values, this interval programming resolves formulations to determine the efficiencies of decision-making units (DMUs).

The comprehensive findings provide valuable insights into the distinctive factors of European nations, aiding informed decision-making for residency choices. Malta (75.6%-76.1%-75.8%), Austria (78.2%-78%-76.1%), and the United Kingdom (79.3%-78.4%-77%) stand out with distinct characteristics at levels of a = 0-a = 0.5-a = 1, assuming the independence of variables of the overall evaluation. Individual consideration of each factor reveals various countries as prominent contenders, except for the environmental factor, which remains consistent across countries.

Traditional DEA models encounter challenges when dealing with uncertainties and inaccuracies, particularly in the evaluation of large systems. To overcome these limitations, we propose integrating Z-numbers—a powerful mathematical tool for modeling uncertainty—into the conventional DEA process. Our methodology not only assesses the effectiveness of countries across various socio-economic and environmental metrics but also explicitly addresses the inherent uncertainties associated with the data. By doing so, it aims to enhance the precision of decision-making and provide valuable insights for policymakers and stakeholders.

The paper aims to propose an innovative two-stage decision model to address the sustainable-resilient supplier selection and order allocation (SSOA) problem in the single-valued neutrosophic (SVN) environment.

First, the sustainable and resilient performances of suppliers are evaluated by the proposed integrated SVN-base-criterion method (BCM)-an acronym in Portuguese of interactive and multi-criteria decision-making (TODIM) method, with consideration of the uncertainty in the decision-making process. Then, a novel multi-objective optimization model is formulated, and the best sustainable-resilient order allocation solution is found using the U-NSGA-III algorithm and TOPSIS method. Finally, based on a real-life case in the automotive manufacturing industry, experiments are conducted to demonstrate the application of the proposed two-stage decision model.

The paper provides an effective decision tool for the SSOA process in an uncertain environment. The proposed SVN-BCM-TODIM approach can effectively handle the uncertainties from the decision-maker’s confidence degree and incomplete decision information and evaluate suppliers’ performance in different dimensions while avoiding the compensatory effect between criteria. Moreover, the proposed order allocation model proposes an original way to improve sustainable-resilient procurement values.

The paper provides a supplier selection process that can effectively integrate sustainability and resilience evaluation in an uncertain environment and develops a sustainable-resilient procurement optimization model.

We identify and further aggregate the most commonly engaged risk factors in dry port projects into dimensions. Noting the importance of developing a multi-perspective view of risk, we further assess the priority, interdependency and heterogeneity of the identified risk dimensions.

We identified 44 risk factors from the literature, which were aggregated via exploratory factor analysis (EFA) into 8 major risk dimensions. We employ a fuzzy-based decision-making trial and evaluation laboratory (DEMATEL) relationship map to articulate various relationships among the risk dimensions.

“Cost” emerged as the most important risk influencing the success of the dry port project, followed by “location,” “accessibility,” “infrastructural” and “operational,” which were also ranked prominently.

This study offers significant insight into the management of risk in dry port projects. By aggregating key risk factors into distinct dimensions, we develop a structured framework for effective risk assessment and management. The insights gleaned from the study extend globally, as it serves as a concrete knowledge base to understand potential barriers to successful dry port projects.

With the advent of technology, a huge amount of data is being transmitted and received through the internet. Large bandwidth and storage are required for the exchange of data and storage, respectively. Hence, compression of the data which is to be transmitted over the channel is unavoidable. The main purpose of the proposed system is to use the bandwidth effectively. The videos are compressed at the transmitter’s end and reconstructed at the receiver’s end. Compression techniques even help for smaller storage requirements.

The paper proposes a novel compression technique for three-dimensional (3D) videos using a zig-zag 3D discrete cosine transform. The method operates a 3D discrete cosine transform on the videos, followed by a zig-zag scanning process. Finally, to convert the data into a single bit stream for transmission, a run-length encoding technique is used. The videos are reconstructed by using the inverse 3D discrete cosine transform, inverse zig-zag scanning (quantization) and inverse run length coding techniques. The proposed method is simple and reduces the complexity of the convolutional techniques.

Coding reduction, code word reduction, peak signal to noise ratio (PSNR), mean square error, compression percent and compression ratio values are calculated, and the dominance of the proposed method over the convolutional methods is seen.

With zig-zag quantization and run length encoding using 3D discrete cosine transform for 3D video compression, gives compression up to 90% with a PSNR of 41.98 dB. The proposed method can be used in multimedia applications where bandwidth, storage and data expenses are the major issues.

The adaptive performance of architects as a key professional in project delivery teams has become important for developing strategies, skills and cognitive behaviours for sustainability of working systems. However, the understanding and knowledge of adaptive performance of architects is lacking in the current literature. Thus, this study fills this gap by primarily assessing the adaptive performance of architects in project teams in project delivery.

By adopting the widely used eight-dimension attributes of adaptive performance, a questionnaire survey was conducted among team participants and stakeholders who directly or indirectly work on projects with architects in the public and private sectors project delivery supply chain in Ghana. A total of 42 responses were subsequently used in a fuzzy set theory analysis being facilitated by a set of linguistic terms.

From the assessment, the overall adaptive performance of architects from the eight-dimension attributes emerged to be fairly high. Additionally, the architects’ performance in the individual eight-dimensions showed varied results. High performance was registered in architects’ ability to handling work stress and cultural adaptability. Also, architects demonstrated a fairly high performance in dealing with uncertain or unpredictable work situations. However, in the cases of learning work tasks, technologies and procedures, interpersonal adaptability and handling crisis and emergency situations, architects were deemed to have low and fairly low adaptive performance among project teams.

Given the vagueness and complexities in understanding adaptability among teams and its assessment, through the use of fuzzy set theory based on a suitable set of linguistics terms, the study presents a novel understanding of the level of architects’ adaptive performance in project teams in project delivery. The findings are extremely useful in helping architects adapt and cope with changing competitive work environment by developing the right cognitive behaviours for task functions and organizational roles, disruptions and aiding their ability to self-regulate.

The aim of this study is to (1) construct a standard framework for assessing the capability of bioenergy enterprises' digital green innovation partners; (2) quantify the choice of partners for digital green innovation by bioenergy enterprises; (3) propose based on a dual combination empowerment niche digital green innovation field model.

Fuzzy set theory is combined into field theory to investigate resource complementarity. The successful application of the model to a real case illustrates how the model can be used to address the problem of digital green innovation partner selection. Finally, the standard framework and digital green innovation field model can be applied to the practical partner selection of bioenergy enterprises.

Digital green innovation technology of superposition of complementarity, mutual trust and resources makes the digital green innovation knowledge from partners to biofuels in the enterprise. The index rating system included eight target layers: digital technology innovation level, bioenergy technology innovation level, bioenergy green level, aggregated digital green innovation resource level, bioenergy technology market development ability, co-operation mutual trust and cooperation aggregation degree.

This study helps to (1) construct the evaluation standard framework of digital green innovation capability based on the dual combination empowerment theory; (2) develop a new digital green innovation domain model for bioenergy enterprises to select digital green innovation partners; (3) assist bioenergy enterprises in implementing digital green innovation practices.