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The challenges posed by unsustainable practices in today's economy underscore the urgent need for a transition toward a circular economy (CE) and a holistic supply chain (SC) perspective. Benchmarking plays a pivotal role in managing circular SCs, offering a metric to gauge progress. However, the lack of consensus on the optimal benchmarking approach hampers effective implementation of circular business practices. To address this gap, we conducted a systematic review of the literature, identifying 29 pertinent publications. The analysis revealed 30 unique attributes and sub-attributes for benchmarking circularity, which were clustered into five main attributes. The main attributes are goals, subjects, key performance indicators (KPIs), data sources, and evaluation methods, while the sub-attributes are organised as features of the main attributes and depicted as a feature model. Drawing from selected publications, we illustrated each feature with examples. Our model offers a comprehensive benchmarking reference for circularity and will be a valuable tool for managers in the transition toward circularity. Supply chains seeking to benchmark their transition to circularity can apply the reference model to ensure that their benchmarking strategy is consistent with state-of-the-art knowledge. By providing a generic circularity benchmarking approach that is valid for diverse economic sectors, our findings contribute to theoretical efforts to address the lack of generic frameworks for CE.

In the rapidly evolving landscape of cyber threats, effective defense strategies are crucial for safeguarding sensitive information and critical systems. Deep learning methods, notably the Transformer architecture, have shown immense potential in addressing cybersecurity challenges. However, customizing, and adapting Transformer architectures for cybersecurity applications presents a challenge, demanding the utilization of effective strategies to achieve optimal performance. This study presents a comprehensive analysis of design tactics employed in tailoring Transformer architectures specifically for cybersecurity problems. Design tactics, defined as strategic solutions to architectural challenges based on well-justified design decisions, are explored in-depth within the context of cybersecurity. By examining the modifications and adaptations made to the original Transformer architecture, this study unveils the design decisions and strategies crucial for successful implementation in diverse cybersecurity domains. The findings emphasize the significance of aligning design tactics with the unique business requirements and quality factors of each specific application domain. This study contributes valuable insights into the utilization of design tactics for customizing Transformer architectures in cybersecurity, paving the way for enhanced defense strategies against the dynamic and evolving nature of cyber threats.

The notion of the smart city involves embedding Industry 4.0 technologies to improve the lives of inhabitants in urban environments. Within this context, smart city data layers (SCDLs) concern the integration of extra tiers of information for the purposes of improving communication potential. Under the Industry 4.0 technology grouping, advanced communication technologies, such as virtual reality (VR), further the opportunities to model, recreate, evaluate and communicate scenarios that potentially improve citizens lives at multiple SCDL scales in a smart cities context. The use of added contextual information in SCDLs is of special interest for emergency planning situations at the building scale. In this research, a literature review to understand the current approaches for the use, development and evaluation of VR applications in the context of emergency planning was conducted. The results indicated four main categories of relevant challenges for these types of applications, for which recommendations and a roadmap for VR development are presented. In total, the study identified 10 commonly occurring challenges (e.g., optimization and discomfort) and 19 solution directions (e.g., model construction and spatial directions) in related articles when considering the development of VR for spatial planning and emergency situations.

The traditional data management systems prove inadequate to handle the volume, velocity, and variety of the data within farm business processes. Smart farming technologies offer advanced data management systems as a practical solution to these challenges. However, data is complex and originates from many sources; hence many aspects of data must be considered during the data management design of smart farming systems. This study proposes a reference architecture for data management in smart farming, developed through domain analysis and architecture modeling approaches. The domain analysis provides insights into the common and variant features and modules of the smart farming system, resulting in a blueprint representing family features across various smart farming domains. The effectiveness of the proposed reference architecture has been evaluated through two case studies, demonstrating its efficacy in designing data management systems for smart farming. The study found that the percentage of reused modules in the case studies, compared to the provided reference architecture, was 82.6%. The outcomes of this research will pave the way for further exploration in smart farming, particularly addressing data management issues within smart farming systems.

Microservice architecture (MSA) is an architectural style that is designed to support the modular development of software systems within a particular domain. It is characterized by the use of small, independently deployable services, which can be developed and deployed autonomously. The benefits of MSA include improved scalability, fault-tolerance, and ease of deployment and maintenance. However, developing MSA for a specific domain can be challenging and requires a thorough consideration of various concerns such as service boundaries, communication protocols, and security. To support the easy development and guidance of an MSA for practitioners, we present a reference architecture design for MSA. The reference architecture has been designed after a comprehensive domain analysis of MSA in the literature and the MSAs of key vendors. This analysis has been used to identify best practices and common patterns that can be applied to the development of MSA. Additionally, relevant architecture viewpoints have been selected to model the corresponding architecture views, providing a clear and comprehensive understanding of the architecture. To validate the proposed reference architecture, a multi-case study research approach has been used. In this approach, two industrial case studies have been used to demonstrate the practical applicability of the proposed reference architecture. The results of these case studies have shown that the reference architecture can be used to effectively guide the development of MSA in real-world scenarios.

Although failure has been described as a significant trigger for the organizational learning process, current literature studies have provided limited insight into the organizational conditions that foster learning from failure. Interpreting the organizational failure as a missed opportunity for a firm to anticipate, recognize, avoid, neutralize or adapt to external environment generating diverse misfits, our study investigates how firms respond to different misfits within and in their environment. Using interviews and document analysis, we identify four types of misfits-managerial, cultural, technical, and political-and examine how firms respond to these misfits over time. Our research reveals that firms exhibit distinct patterns of response and learning depending on the type of misfit encountered. We also identify specific strategies and resources that firms utilize to anticipate, prepare for, and respond to misfits, such as developing innovative solutions and cultivating external networks. Lastly, we discuss the managerial implications of our findings and propose avenues for future research.

Digital twins are a core industry 4.0 technology enabling the virtual replication of real-world objects, mimicking behaviours and states throughout their lifespan. While digital twins have shown significant benefits in industries such as manufacturing, transportation, and healthcare, their application in agriculture is still within its infancy. Their realisation also poses significant challenges, such as the creation of dynamic agricultural objects (e.g., plants). Existing literature on digital twins in agriculture identifies their limited ability to monitor physical objects without predictive capabilities and that there is a significant lack of 3D representations of plants with functional attributes. Yet, incorporating 3D representations of plants with underlying functionality in a digital twin can greatly improve growth, yield, and disease prediction accuracy. This enhancement enables various applications, such as assessing and developing pruning strategies, providing education to growers, guiding pruning robots, and optimizing spraying techniques. To that end, Functional Structural Plant Modelling presents a potential solution by representing the 3D architecture of plants and incorporating the functionality of different plant parts. By conducting a domain analysis of 3D plant phenotyping and FSPM, this study addresses the specific needs of digital twins in agriculture regarding FSPM. The investigation bridges the existing knowledge gap by identifying crucial concepts, including 3D plant modelling with underlying functionality and 3D plant phenotyping for digital twins. Specifically, a framework for 3D FSPM integration into agricultural digital twins is proposed. The framework not only acknowledges the associated requirements and challenges identified in existing literature but also lays foundation for the advancement of digital twins in the agricultural domain.

A safety-critical system is a system in which the software malfunctioning could result in death, injury, or damage to the environment. Addressing safety concerns early on at the architecture design level is critical to guide the subsequent life cycle activities to ensure that the eventual system is reliable. A fundamental approach to address safety at the design level is the adoption of architectural tactics. It is crucial for safety-critical systems to correctly implement the constraints as defined by the selected safety tactics. Given the limitations of traditional safety measures, particularly in conducting thorough analyses of safety tactics at the architectural design level, our motivation is to close this gap by integrating safety tactics and fault-based mutation testing. This article proposes a systematic approach for assessing the adequacy of test suites of safety-critical systems based on these architectural safety tactics. Our proposed approach involves the integration of safety tactics and fault-based mutation testing to comprehensively assess the adequacy of test suites in safety-critical systems. At the core of our methodology is the adoption of architectural safety tactics, addressing potential issues at the design level. We leverage fault-based testing, utilizing a dedicated metamodel and domain-specific language (DSL) to model safety views and their relation to the code. We use a case study to evaluate the effectiveness of our approach using fault-injection techniques. Our study shows that this systematic approach is feasible and effective for test suite assessment of safety-critical systems.