Advanced Resources International (United States)

Background: Asymptomatic left ventricular systolic dysfunction (LVSD) often precedes overt heart failure but frequently goes undetected in the general population. With the rise of artificial intelligence-enabled electrocardiogram (AI-ECG) now offer a scalable approach for early detection. Hypothesis: We hypothesize that AI-ECG is capable of identifying LVSD in apparently healthy individuals undergoing routine check-ups. Methods: This retrospective single-center study evaluated the performance of an AI-ECG model (AiTiALVSD) among 40,713 asymptomatic adults who underwent 60,711 paired ECG and echocardiography exams as part of self-referred health screenings from 2011 to 2023. LVSD was defined as left ventricular ejection fraction ≤40%. ( Figure 1 ) Model performance was compared against two conventional heart failure risk scores. Results: The AiTiALVSD model demonstrated excellent diagnostic accuracy with an area under the receiver operating characteristic curve of 0.973 and a precision-recall area of 0.328. ( Figure 2 ) At the prespecified threshold, the model achieved 90.6% sensitivity, 99.4% specificity, 7.7% positive predictive value, and 100% negative predictive value. In comparison, conventional models performed worse (area under the curve of 0.696 for MESA and 0.672 for PCP-HF). ( Figure 3 ) Simulation suggested that 1,841 ECGs and 13 echocardiograms would be required to identify one LVSD case. Notably, most false positives exhibited other echocardiographic abnormalities. Conclusions: The AI-ECG model showed high diagnostic performance in a real-world low-prevalence setting, supporting its potential as a cost-effective, noninvasive screening tool for early detection of LVSD. These findings warrant prospective validation and suggest a promising role for AI in community-based heart failure prevention strategies.

In preceding chapters of this comprehensive exploration into smart cyber-physical power systems, we have delved into the structural complexities, operational challenges, and the innovative solutions and tools that characterize the current landscape of smart power systems. From the foundational concepts to the intricate challenges and cutting-edge solutions within the cyber-physical framework, this volume has traversed a wide spectrum of topics, setting the stage for an in-depth discussion on the future of the field. In this final chapter, we pivot toward the horizon, exploring the next era of smart cyber-physical power systems and cyber-physical social power systems, where emerging technologies such as artificial intelligence, generative AI, machine learning, blockchain, digital twins, and the revolutionary realms of quantum computing, information theory, and the metaverse along with advanced control mechanisms promise to redefine the boundaries of what is possible. This chapter not only encapsulates the visionary trajectory of cyber-physical power systems but also emphasizes the critical role of ongoing research, development, and collaboration across disciplines. It underscores the necessity of embracing these emerging technologies to foster a future where power systems are not only smart but also integral to a sustainable, interconnected global community.

Abstract Peatlands are crucial yet vulnerable carbon stores. Here, we investigated carbon biogeochemical processes in tropical peatlands converted to plantations. We measured carbon dioxide (CO 2 ) and methane (CH 4 ) concentrations, stable isotope ratios and radiocarbon content in an experimental Acacia crassicarpa plantation in Sumatra, Indonesia. We found exceptionally high levels of dissolved organic carbon (DOC), CO 2 , and CH 4 in porewater and drainage networks, indicating that Acacia plantations are carbon hotspots due to their high productivity and exposed carbon‐dense substrates. Stable isotope models revealed that while CO 2 and CH 4 are produced belowground, CH 4 contribution was lower than in natural undrained peatlands. Radiocarbon analysis suggested that remobilized carbon contributed to the carbon pool, with a median age of ∼470 years before present. These findings constrain the links between land‐use, water table levels, and carbon dynamics, with implications for carbon management in plantation peatlands.

Abstract Nearly 25 years ago, publicly funded research into the suitability of permanently storing CO2 in the subsurface was initiated in many parts of the globe to mitigate CO2 emissions. Over time, this work developed capacity assessments and conducted pilot tests to demonstrate storage efficacy. Results of these assessments suggested that large volumes of CO2 can be stored in many global basins. In parallel with this research, regulatory authorities developed frameworks to govern the injection and storage processes to assure permanency and to minimize environmental impacts. However, these R&D and regulatory developments, by themselves, were not enough to kick-start an industry. A "third leg" of the stool was required, which is a viable commercial model. In a limited number of sites around the world, commercial storage projects were developed to mitigate carbon emission taxes. However, momentum has not been maintained and the portfolio of large projects has not grown. In the United States, however, the advent of tax incentives has spurred intensive commercial interest in carbon storage. This confluence of incentivization, decades long research, and an established regulatory framework has led to significant growth in carbon storage activity in projects great and small.

Abstract This paper seeks to demonstrate the application of learnings from the oil and gas industry, specifically in the field of geoscience, as they relate to applications within the growing industry of Carbon Capture, Utilization and Storage (CCUS). Additionally, this paper will highlight how current educational programs are already geared to produce a talent pipeline for young professionals beginning their careers in the CCUS field or for later career professionals who are looking to transition into CCUS. To demonstrate the stated objectives, the fundamental theories and knowledge required to operate as a successful geoscientist in the oil and gas industry will be presented. This will include a discussion of basic skills and working knowledge as well as an introduction to various software utilized extensively within the oil and gas industry. This provides context illustrating where the geoscience community has been and the prior focus of many geoscience education pipelines. Then, a comparison of the oil and gas workflows to CCUS workflows will be made to demonstrate that the skills and techniques are aligned. Example workflows from our experience developing CCUS projects will be presented with a focus on the geologic characterization, such as generating subsurface geologic and structural models and developing reservoir models to predict plume migration. It will be made clear that the fundamentals of these workflows and the skills necessary to implement them are essentially the same. Both oil and gas and CCUS workflows utilize the same basic knowledge and geologic understanding (e.g., reservoir porosity, permeability, volumetrics), the same data (e.g., geophysical well logs, seismic reflection surveys, geologic core analysis) and even the same software. This demonstrates the direct application of knowledge and workflows that were previously gatekept by the oil and gas community and highlights their significance in ability to be applied to new opportunities. The discussion presented in this paper clearly identifies the overlap of experience gained throughout the detailed history of oil and gas exploration and its direct application to the burgeoning world of CCUS from the perspective of geologic characterization. The examples provided herein also demonstrate that the pipeline for creating new talent already exists with emphasis on learning the fundamentals of geoscience, and the specific application of those skills can be enacted seamlessly within the CCUS community.

Study on development potential of

The pulp and paper industry in Indonesia is the tenth largest producer in the world, with Acacia and Eucalyptus as the main genera used for production; however, limited publications exist related to Eucalyptus growth models in Indonesia compared with other regions. Time-based models have been developed in which height, stand density, and basal area are predicted based on initial conditions and age. In contrast, a state–space approach utilizes the rate of change of these three state variables. Previous direct comparisons of these two approaches are generally limited. Consequently, the objective of this study was to compare two stand-level growth modeling approaches for Eucalyptus hybrid species on Sumatera (Sumatra) Island using both time-based and state–space methods. Our results indicate that dynamic models using either time-based or state–space approaches are adequate for predicting stand parameters to rotation age. A modified Bazukis matrix indicated that the behavior of both methods produced reliable predictions that were biologically reasonable in terms of stand development; however, the time-based approach provided better performance than the state–space approach on a variety of equivalence tests and goodness-of-fit statistics. Overall, the analysis highlights the advantages and disadvantages of these two commonly used, yet highly contrasting, stand-level growth modeling approaches, which need further consideration and evaluation.