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Although most digital twin (DT) applications for health care have emerged in precision medicine, DTs can potentially support the overall health care process. DTs (twinned systems, processes, and products) can be used to optimize flows, improve performance, improve health outcomes, and improve the experiences of patients, doctors, and other stakeholders with minimal risk. This paper aims to review applications of DT systems, products, and processes as well as analyze the potential of these applications for improving health care management and the challenges associated with this emerging technology. We performed a rapid review of the literature and reported available studies on DTs and their applications in health care management. We searched 5 databases for studies published between January 2002 and January 2022 and included peer-reviewed studies written in English. We excluded studies reporting DT usage to support health care practice (organ transplant, precision medicine, etc). Studies were analyzed based on their contribution toward DT technology to improve user experience in health care from human factors and systems engineering perspectives, accounting for the type of impact (product, process, or performance/system level). Challenges related to the adoption of DTs were also summarized. The DT-related studies aimed at managing health care systems have been growing over time from 0 studies in 2002 to 17 in 2022, with 7 published in 2021 (N=17 studies). The findings reported on applications categorized by DT type (system: n=8; process: n=5; product: n=4) and their contributions or functions. We identified 4 main functions of DTs in health care management including safety management (n=3), information management (n=2), health management and well-being promotion (n=3), and operational control (n=9). DTs used in health care systems management have the potential to avoid unintended or unexpected harm to people during the provision of health care processes. They also can help identify crisis-related threats to a system and control the impacts. In addition, DTs ensure privacy, security, and real-time information access to all stakeholders. Furthermore, they are beneficial in empowering self-care abilities by enabling health management practices and providing high system efficiency levels by ensuring that health care facilities run smoothly and offer high-quality care to every patient. The use of DTs for health care systems management is an emerging topic. This can be seen in the limited literature supporting this technology. However, DTs are increasingly being used to ensure patient safety and well-being in an organized system. Thus, further studies aiming to address the challenges of health care systems challenges and improve their performance should investigate the potential of DT technology. In addition, such technologies should embed human factors and ergonomics principles to ensure better design and more successful impact on patient and doctor experiences.

Using data from embedded participant-observers and a field experiment at the second largest mobile phone factory in the world, located in China, I theorize and test the implications of transparent organizational design on workers' productivity and organizational performance. Drawing from theory and research on learning and control, I introduce the notion of a transparency paradox, whereby maintaining observability of workers may counterintuitively reduce their performance by inducing those being observed to conceal their activities through codes and other costly means; conversely, creating zones of privacy may, under certain conditions, increase performance. Empirical evidence from the field shows that even a modest increase in group-level privacy sustainably and significantly improves line performance, while qualitative evidence suggests that privacy is important in supporting productive deviance, localized experimentation, distraction avoidance, and continuous improvement. I discuss implications of these results for theory on learning and control and suggest directions for future research.

Understanding how cell organelles and compartments communicate with each other has always been an important field of knowledge widely explored by many researchers. However, despite years of investigations, one point—and perhaps the only point that many agree on—is that our knowledge about cellular-signaling pathways still requires expanding. Chloroplasts and mitochondria (because of their primary functions in energy conversion) are important cellular sensors of environmental fluctuations and feedback they provide back to the nucleus is important for acclimatory responses. Under stressful conditions, it is important to manage cellular resources more efficiently in order to maintain a proper balance between development, growth and stress responses. For example, it can be achieved through regulation of nuclear and organellar gene expression. If plants are unable to adapt to stressful conditions, they will be unable to efficiently produce energy for growth and development—and ultimately die. In this review, we show the importance of retrograde signaling in stress responses, including the induction of cell death and in organelle biogenesis. The complexity of these pathways demonstrates how challenging it is to expand the existing knowledge. However, understanding this sophisticated communication may be important to develop new strategies of how to improve adaptability of plants in rapidly changing environments.

This study develops reliable and robust machine learning (ML) models to predict the outlet air temperature and humidity and thermal efficiency of a solar air heater (SAH). Also, the application of predictive models for optimal control of the SAH operation is proposed. For this, the work contains three main parts: (a) a vertically-mounted symmetrical SAH was installed outside of a building room and operated throughout the winter of 2022. (b) By conducting experiments for five air mass flow rates, a large dataset with more than 62,500 sample points was collected. (c) Six input features containing time, environmental-related attributes, and SAH variables were applied to develop several state-of-the-art ML algorithms. To figure out the most accurate models for predicting output variables, the dataset was partitioned into three parts. Also, various modeling performance evaluation criteria were calculated and compared on the validation and test sets. Among these models, the gradient boosting machine algorithm based on LightGBM implementation achieved the best degree of generalization in modeling the target variables. The results demonstrated that the developed models obtained the lowest R-squared and the highest mean absolute percentage error of 0.9827 and 2.95%, respectively, on the test set. Moreover, the offline analysis of SAH operation based on the proposed control scheme demonstrated that 350 kWh of thermal energy can be generated during the application in the one-year winter season, 24% more than SAH operation without a model-based control strategy.

Building energy systems (BESs) are essential for modern infrastructure but face significant challenges in equipment diagnosis, energy consumption prediction, and operational control. The complexity of BESs, coupled with the increasing integration of renewable energy sources, presents difficulties in fault detection, accurate energy forecasting, and dynamic system optimisation. Traditional control strategies struggle with low efficiency, slow response times, and limited adaptability, making it difficult to ensure reliable operation and optimal energy management. To address these issues, researchers have increasingly turned to machine learning (ML) techniques, which offer promising solutions for improving fault diagnosis, energy scheduling, and real-time control in BESs. This review provides a comprehensive analysis of ML techniques applied to fault diagnosis, energy consumption prediction, energy scheduling, and operational control. According to the results of analysis and literature review, supervised learning methods, such as support vector machines and random forest, demonstrate high classification accuracy for fault detection but require extensive labelled datasets. Unsupervised learning approaches, including principal component analysis and clustering algorithms, offer robust fault identification capabilities without labelled data but may struggle with complex nonlinear patterns. Deep learning techniques, particularly convolutional neural networks and long short-term memory models, exhibit superior accuracy in energy consumption forecasting and real-time system optimisation. Reinforcement learning further enhances energy management by dynamically adjusting system parameters to maximise efficiency and cost savings. Despite these advancements, challenges remain in terms of data availability, computational costs, and model interpretability. Future research should focus on improving hybrid ML models, integrating explainable AI techniques, and enhancing real-time adaptability to evolving energy demands. This review also highlights the transformative potential of ML in BESs and outlines future directions for sustainable and intelligent building energy management.

Climate change is viewed as the major threat to the security of water supplies in most parts of the world in the coming decades, and the water resources literature continues to be dominated by impact and risk assessments based on the latest climate projections from General Circulation Models (GCMs). However, the evidence for anthropogenic changes in precipitation and streamflow records continues to be elusive which, together with the known high uncertainty in GCM ensemble projections, has led to the development of risk assessment methods which are not driven exclusively by GCMs. It is argued that a baseline risk assessment should retain the assumption of climatic stationarity, and be based on the modelling of observed interannual variability as a dominant process in determining water resource system reliability, augmented where justifiable by reliable information from GCMs. However, irrespective of what the climate does in the future, globalization and socio-economic changes are the major drivers for increases in water demand and threats to water security, as exemplified by the burgeoning economies of the BRIC and MINT countries, and the large population increases and economic growth seen in many developing countries. It is suggested that more attention needs to be paid to adaptation to socio-economic change which is arguably more predictable than climatic change, based on what is already known about population and economic growth, lifestyle changes and human choices. More focus is needed on economic analyses that can inform the major investments in water use efficiency measures which can deliver the water savings needed to avert widespread water scarcity. The effectiveness of water use efficiency measures is largely determined by (a) the potential of modern information technology to achieve more efficient water resources management and water use and (b) human responses and choices in the uptake of measures. To assess the potential efficiency gains, it is argued that water resource systems modelling needs to evolve to incorporate the human dimension more explicitly, through Coupled Human and Natural Systems (CHANS) modelling. A CHANS modelling framework is outlined which incorporates agent-based modelling to represent individual choices within the human system, and prospects for assessing the effectiveness of efficiency measures involving individual human responses are discussed.