Smart Fruit Growing Through Digital Twin Paradigm: Systematic Review and Technology Gap Analysis

The application of digital twins and artificial intelligence to manufacturing has shown potential in improving system resilience, responsiveness, and productivity. Traditional digital twin approaches are generally applied to single, static systems to enhance a specific process. This paper proposes a framework that applies digital twins and artificial intelligence to manufacturing system reconfiguration, i.e., the layout, process parameters, and operation time of multiple assets, to enable system decision making based on varying demands from the customer or market. A digital twin environment has been developed to simulate the manufacturing process with multiple industrial robots performing various tasks. A data pipeline is built in the digital twin with an API (application programming interface) to enable the integration of artificial intelligence. Artificial intelligence methods are used to optimise the digital twin environment and improve system decision-making. Finally, a multi-agent program approach shows the communication and negotiation status between different agents to determine the optimal configuration for a manufacturing system to solve varying problems. Compared with previous research, this framework combines distributed intelligence, artificial intelligence for decision making, and production line optimisation that can be widely applied in modern reactive manufacturing applications.

The new economic context has brought new challenges to the supply chain and has increased the complexity of its processes. The digitalization; as one of these challenges, is a rapidly evolving paradigm that transforms supply chains by integrating data and communication technologies to optimize operations, enhance sustainability, and improve overall performance. Digital twin technology emerged as one of the most promising digital tools that offer an innovative approach to supply chain management. However, the adoption of digital twins in the supply chain is still in its early stages. Previous research papers presented limited overviews of the applications of digital twin technology in supply chain systems that need to be extended, as it is inevitably a work in progress. In this matter, we conducted a systematic literature review built upon 31 articles to determine the applications of supply chain digital twins (SCDT). This study is divided into three core themes; the first is a comprehensive review of the paradigm of digital supply chain with a focus on digital twin technology and its primary features. The second theme presents an analysis of the 31 papers where we explore the different purposes of SCDTs and their integration. in the third theme by using VOSviewer to conduct a network analysis. We aim; through this paper, to contribute significantly to the supply chain management field by summarizing and analyzing existing research and developments in the applications of digital twins in the different areas of supply chains.

Digital Twins (DT) have become valuable tools of industry 4.0 architectures, providing a cyber-physical link of the increasingly complex systems found in modern society. This link has proven useful in modern manufacturing and distribution methods, enabling instantaneous monitoring and optimization of processes. A key benefit of synchronous monitoring and management of systems is the resultant reduction of carbon emissions. It is expected that the gains attained in other industries through DT use can be applied to the Built Environment (BE), transforming the sector, and building on the foundations laid by Building Information Modelling (BIM). Facility Management (FM) is of interest for DT application due to the complexity of modern FM systems and increasing pressures for improved energy efficiency of assets. Current trends in research highlight the following challenges: Anomaly detection, limited scope, integration of artificial intelligence (AI), standardisation, and practical application. AI offers the possibility to automate the detection and management of anomalies within building systems, enabling automated decision-making. If DTs are to contribute to improved energy efficiency; the building blocks of the Twin must be targeted towards promoting Net Zero principles. This paper posits that DTs should be created towards specific objectives, and that if done correctly these will transfer from the cyber to physical system. This paper presents a conceptual framework for a Net Zero DT based automated decision-making system. The benefits and challenges of BE DTs and the value of specific needs generation were explored through bibliographic literature review, resulting in creation of a novel Net Zero predictive maintenance framework. Key findings include a novel framework, insights into Net Zero DT principles, and key challenges faced by these systems. These new findings contribute to knowledge of the field by providing a basis for efficient DT design, laying a path towards a more sustainable BE.

This study investigates the scientific development of Artificial Intelligence (AI) and Machine Learning (ML) applications in Human Resource Management (HRM) through bibliometric analysis. To this end, 522 academic publications indexed in the Scopus database between 2020 and 2024 were analyzed using the R-based bibliometrix package and VOSviewer software. Descriptive analysis, scientific productivity metrics, and content analysis techniques were employed. The findings revealed three main patterns. First, research on AI and ML applications in HRM has grown significantly—particularly between 2022 and 2024—driven by post-pandemic digital transformation. Second, India, China, and the USA lead in research output, while the UK and France demonstrate strong citation impact, indicating a globally expanding research ecosystem. Third, the thematic focus of research is shifting from technical infrastructure toward more human-centered and ethical dimensions. Additionally, keyword co-occurrence network analysis identified three major thematic clusters: HRM functions, AI applications, and machine learning analytics, highlighting the field’s interdisciplinary nature. Compared to the previous studies, this research provides a more comprehensive bibliometric analysis of AI and ML applications in HRM. It is the first extensive study to map the intellectual evolution of the field from a multidisciplinary perspective. Furthermore, it charts research trends and collaboration networks, revealing a shift from technical implementations to strategic integration. In conclusion, this analysis offers new insights to the literature by illustrating the technological evolution in HRM and highlighting the growing significance of cutting-edge approaches such as AI and ML, reaffirming the field as a timely and impactful area of research.

Geometric parameter updating in digital twin of built assets: A systematic literature review

Digital Twins can be considered as a new phase in smart and data-driven greenhouse horticulture. A Digital Twin is a digital equivalent to a real-life object of which it mirrors its behaviour and states over its lifetime in a virtual space. Research indicates that they can substantially enhance productivity and sustainability, and are able to deal with the increasing scarcity of green labour in greenhouse horticulture. This paper presents the results of a systematic literature review on Digital Twin applications in greenhouse horticulture. The review identifies 8 articles that explicitly address Digital Twins in greenhouse horticulture and 115 studies that implicitly apply the Digital Twin concept in smart IoT-based systems. Findings indicate that the concept of the Digital Twin is in a seminal phase in greenhouse horticulture, but there are existing applications that are not yet framed as Digital Twins. In the reviewed papers, there is a dominant focus on the cultivation process at the greenhouse level, among others for climate control, energy management and lighting. About 9% of the articles are virtualizing plants themselves, which indicates that the granularity level addressed is still rather limited. Only 7 % of the articles look beyond plants or single greenhouses. None of the reviewed articles consider the company level. Furthermore, most applications address monitoring and control of the state and behaviour of real-life objects. More advanced applications, including predictive and prescriptive capabilities across the complete lifecycle, are still in an early stage of development, although predictive Digital Twins are gaining prominence.

Digital Twins (DT) have massive scope for success in the field of sustainable agriculture. But the number of works done in this field is relatively less compared to other domains like Manufacturing, Healthcare, Autonomous Vehicles, and Aviation. Due to frequent natural calamities like floods and epidemics, the need for sustainable and self-sufficient agriculture from the primary level is essential. Soil-less agriculture is gaining more popularity over soil-born methods due to issues of soil-based agriculture like soil erosion, intensive labor, water reusability, and overall productivity. This work deals with one of the popular soil-less methods, Hydroponics. The various ways in which DTs can contribute to the various phases of hydroponics like designing, operation, monitoring, optimization, and maintenance are discussed. This paper also presents a review of research works done in the application of DT in smart farming.

G protein-coupled receptor digital twins for precision and personalized medicine

This paper explores the potential for implementing digital twin technology, focusing on the internal structure of the research object and the remote characteristics of its surrounding environment. Specifically, it examines and demonstrates the practical application of local digital twins, which replicate the object’s structural parameters using data from sensors and measurement devices positioned at key nodes within the research object. Another category of digital twins leverages data collected from instruments measuring external environmental conditions and falls under the classification of remote digital twins. When combined, these local and remote digital twins create a comprehensive framework for predictive decision-making, assessing both the current status of the object and potential outcomes in emergency situations. This study seeks to explore the feasibility of integrating digital twins across various hierarchical levels of the research object. The findings presented in this paper represent the authors’ practical innovations, which demonstrate effective outcomes and offer a foundation for advancing research objectives in this area.

Digital twin technology has been increasingly applied in healthcare and patient well-being in recent years. This paper provides an overview of the current methods and applications of digital twins in the healthcare field. One such application is digital twins in precision healthcare, where digital twins are used to create patient-specific models to assist in diagnosis and treatment planning. Digital twins are also used in hospital/clinic management, where they help to optimize resource allocation and workflow processes. In response to the COVID-19 pandemic, digital twins have been utilized to detect outbreaks and predict disease spread. In addition, digital twins have been applied in bio-manufacturing and pharmaceutical industry to improve manufacturing processes. Another application area is machine learning and modeling, where digital twins are used in machine learning, data generation, and system modeling for applications in healthcare and disease prediction. Security and ethical issues related to digital twins are also discussed in this paper, as privacy concerns and data protection remain important considerations in the application of digital twin technology in healthcare. Finally, the paper concludes by discussing the future challenges and directions of future work in this field. These include the need to develop more accurate and sophisticated digital twin models, addressing interoperability and integration issues, and further exploring the potential of digital twin technology in emerging areas such as telemedicine and personalized medicine.

The digital twin concept has been widely known for asset monitoring in the industry for a long time. A clear example is the automotive industry. Recently, there has also been significant interest in the application of digital twins in healthcare, especially in genomics in what is known as precision medicine. This work focuses on another medical speciality where digital twins can be applied, sleep medicine. However, there is still great controversy about the fundamentals that constitute digital twins, such as what this concept is based on and how it can be included in healthcare effectively and sustainably. This article reviews digital twins and their role so far in what is known as personalized medicine. In addition, a series of steps will be exposed for a possible implementation of a digital twin for a patient suffering from sleep disorders. For this, artificial intelligence techniques, clinical data management, and possible solutions for explaining the results derived from artificial intelligence models will be addressed.KeywordsDigital twinPersonalized medicinePrecision medicine

When, in 1956, Artificial Intelligence (AI) was officially declared a research field, no one would have ever predicted the huge influence and impact its description, prediction, and prescription capabilities were going to have on our daily lives. In parallel to continuous advances in AI, the past decade has seen the spread of broadband and ubiquitous connectivity, (embedded) sensors collecting descriptive high dimensional data, and improvements in big data processing techniques and cloud computing. The joint usage of such technologies has led to the creation of digital twins, artificial intelligent virtual replicas of physical systems. Digital Twin (DT) technology is nowadays being developed and commercialized to optimize several manufacturing and aviation processes, while in the healthcare and medicine fields this technology is still at its early development stage. This paper presents the results of a study focused on the analysis of the state-of-the-art definitions of DT, the investigation of the main characteristics that a DT should possess, and the exploration of the domains in which DT applications are currently being developed. The design implications derived from the study are then presented: they focus on socio-technical design aspects and DT lifecycle. Open issues and challenges that require to be addressed in the future are finally discussed.

<b><sc>Abstract.</sc></b> Cooling tomatoes after harvesting and keeping them refrigerated during their transportation is a key step toward preserving the quality attributes of tomatoes. This is a challenging task to accomplish in Nigeria, mainly because shipments of tomatoes from the tomato-producing states in northern Nigeria to the non-producing states in southern Nigeria were predominately not transported in a refrigerated ambient condition. To minimize postharvest losses of tomatoes during transportation, quantification of changes in quality would be extremely valuable. For this purpose, a digital tomato twin was developed based on mechanistic physics-based modeling. This digital twin simulated the actual thermal profile of tomatoes during transportation, based on sensed ambient environmental conditions. The impacts of the adoption of a low-cost, simple plastic packaging container compared with the traditional container (raffia baskets) on tomato's quality evolution during transportation were analyzed in-silico using the developed digital twin. Generally, the values of colour parameter (a*), firmness (N), and lycopene content (LC, mg/100ml) predicted by the digital twin model for tomatoes transported in raffia baskets were all higher than prediction for plastic container. For example, in shipment #2 the predicted colour parameter (a*) was 46.9 % more than the value predicted for the tomatoes in the plastic container at the end of the transportation. Similarly, lycopene content (LC) increased from 1.14 mg/100ml to 2.58 mg/100ml and 1.80 mg/100ml for raffia baskets and plastic container and firmness (N) decreased by 89% and 58 % in the raffia basket and plastic container respectively.

Statement of problem: Artificial intelligence (AI) applications are increasingly used in restorative process. But existing dentistry restoration effectiveness as well as growth and IA applications have not yet been systematically analyzed or documented. Purpose: The goal of such comprehensive evaluation is to discover &amp; assess the abilities associated with artificial intelligence models in restorative dentistry for the analysis of caries as well as vertical tooth fracture, evaluate margins in preparing tooth, and analyze reconstructive failures. Methods: A systematic electronic review of 5 databases was carried out: PubMed/ MEDLINE, World of Science, EMBASE, Scopus and Cochrane. The investigation was carried out manually as well. Research using AI models was chosen on the basis of 4 criterion: dental caries diagnostics, diagnostics, vertical tooth fracture, tooth preparation recognition, &amp; cause of failure of restoration. Both researchers rated the quality of the study for Critical Appraisal Checklist for Quasi-Experimental Studies (nonrandomized experimental studies). The 3rd author was asked for resolving the dispute. Results: 34 researches were made the part of this analysis: from which 29 contains artificial intelligence techniques including the diagnostics or treatment related to caries and its causes according to sensitivity models, 2 to diagnose vertical tooth fractures, 1 to prepare the teeth. Among the studied analysis, the accuracy of caries diagnosis in the AI models was tested from 76-88.3%, sensitivity from 73-90%, as well as specificity from 61.5-93%. In the study, the accuracy of predicted caries ranges from 83.6-97.1%. The performed research showed the accuracy of the analysis of a vertical tooth fracture from 88.3-95.7%. The study, which uses AI models to find a destination, had details with the range of 90.6-97.4%. Conclusions: AI models are a powerful tool for diagnosing caries as well as vertical tooth fractures, recognizing preparation margins and predicting restoration failures. But, the dental use of AI models continues to evolve. More research is needed to evaluate the clinical effectiveness of artificial intelligence models in restorative dentistry. Keyword: Artificial intelligence, restorative dentistry, vertical tooth fractures.

Climate change is increasingly disrupting businesses and ecosystems, creating urgent demand for data-driven adaptation strategies. This study examines how artificial intelligence (AI) can strengthen climate resilience across diverse industries, with particular attention to the innovative business strategies that help organizations respond to global environmental challenges. The objective is to address gaps in existing AI frameworks, focusing on developing countries with resource and technical limitations. The study highlights the significance of AI in fostering sustainable practices, particularly in climate change mitigation. A systematic review of 42 high-quality studies, published between 2010 and 2025 in the Scopus database, was carried out using the PRISMA framework. The analysis identifies key AI applications, technologies, and challenges. Data were organized according to industry applications, technological contributions, and obstacles. Key findings indicate that AI enhances climate risk assessment through predictive modelling, supports adaptive decision-making via scenario analysis, and optimizes resource allocation for sustainability. Applications in renewable energy, precision agriculture, and disaster management are also noted. However, significant barriers persist, including ethical concerns such as algorithmic bias and data privacy, technical complexities, and high financial costs. The review underscores the necessity of collaborative approaches, such as public-private partnerships, and the importance of conducive policy frameworks. The research's originality lies in its comprehensive synthesis of AI applications for climate resilience, offering actionable insights for scholars, practitioners, and policymakers. The findings highlight AI's potential to drive sustainable business models while calling for interdisciplinary research to address scalability and ethical implications in resource-constrained environments.