Integrated AI Framework for Room-Temperature Atom Manipulation in Scanning Probe Microscopy.

Carotid plaque is a biomarker of generalized atherosclerosis, and may predict ischemic stroke. Carotid intima-media thickness (C-IMT) measurement with ultrasonography imaging could capture the condition of carotid plaque. However, manual measurement of C-IMT is observer- dependent, resulting in observer bias and low reproducibility. In this study, we develop artificial intelligence (AI) framework that could automatically measure the C-IMT, and compared it with C-IMT measured by board of expert. This is a retrospective study done in Dr. Moewardi General Hospital, Surakarta, Indonesia. Carotid B-mode ultrasonography images were measured by panel of expert and by AI. After annotation process on Neurabot platform, AI could detect region of interest (ROI), and would do segmentation on the area to measure C-IMT autonomously. Dependent T-test was used to evaluate validity, and Cronbach’s alpha was used to find the reliability of C-IMT measured by panel of expert and AI. There was strong correlation (r=0.874; p=0.014) on dependent t-test for C-IMT measured by AI with C-IMT measured by board of expert. The internal consistency reliability coefficients (Cronbach’s alpha) were 0.938 and 0.909, for pretest and posttest, respectively. We also analyzed the test-retest reliability by comparing pretest and posttest score with dependent t-test, and we observed strong correlation with r=0.871 (p=0.000). AI developed on Neurabot platform are valid and reliable to measure C-IMT.

We determined the probabilities of finding diffusing Si atoms in faulted and unfaulted half-unit cells on the Si(111)-(7 × 7) surface. An adsorbed Si atom on the surface at room temperature moved in the half-unit cells. The atom was adsorbed via atom manipulation and located via under-sampled scanning tunneling microscopy. Images of the half-unit cells with the diffusing Si atom and images of the clean surface were superimposed via image processing to calculate differences used to determine the Si atom positions at room temperature. There were different probabilities for Si atom diffusion in faulted and unfaulted half-unit cells.

Large earthquakes (EQs) occur at surprising loci and timing, and their descriptions remain a long-standing enigma. Finding answers by traditional approaches or recently emerging machine learning (ML)-driven approaches is formidably difficult due to data scarcity, interwoven multiple physics, and absent first principles. This paper develops a novel artificial intelligence (AI) framework that can transform raw observational EQ data into ML-friendly new features via basic physics and mathematics and that can self-evolve in a direction to better reproduce short-term large EQs. An advanced reinforcement learning (RL) architecture is placed at the highest level to achieve self-evolution. It incorporates transparent ML models to reproduce magnitude and spatial location of large EQs (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$M_w \\ge $$\\end{document} 6.5) weeks before of the failure. Verifications with 40-year EQs in the western U.S. and comparisons against a popular EQ forecasting method are promising. This work will add a new dimension of AI technologies to large EQ research. The developed AI framework will help establish a new database of all EQs in terms of ML-friendly new features and continue to self-evolve in a direction of better reproducing large EQs.

This study aimed to develop and evaluate an artificial intelligence (AI) framework for detecting dental restorations and prosthesis devices on panoramic radiographs (PRs). Detecting these elements is essential for enhancing automated reporting, improving the accuracy of dental assessments, and reducing manual examination time. A fast region-based convolutional neural network (Fast R-CNN) was trained using 186 PRs for the training set and 42 for validation. The model's performance was assessed on an external test dataset of 1133 PRs. Seven dental restorations and prosthesis devices were targeted: appliance, bridge, endodontic filling, crown filling, implant, retainer, and single crown. Precision, recall, and F1-score were calculated for each element to measure detection accuracy. The AI framework achieved high performance across all categories, with precision, recall, and F1-scores as follows: appliance (0.79, 0.96, 0.87), bridge (0.91, 0.86, 0.89), endodontic filling (0.98, 0.98, 0.98), crown filling (0.95, 0.95, 0.95), implant (0.99, 0.97, 0.98), retainer (0.98, 0.98, 0.98), and single crown (0.94, 0.96, 0.95). The system processes one panoramic image in under 30 seconds. The AI framework demonstrated high recall and efficiency in detecting dental prosthesis and other dental restorations on PRs. Its application could significantly streamline dental diagnostics and automated reporting, enhancing both the speed and accuracy of dental assessments. This study highlights the potential of AI in automating the detection of multiple dental restorations and prosthesis on PRs, offering a valuable tool for dental professionals to improve diagnostic workflows.

A hybrid Artificial Intelligence (AI) framework centered on metamodeling, integrating simulation data with hybrid data-driven techniques, was implemented to enhance the predictive accuracy and optimization of thermal load projections in three distinct climates in Morocco. Initially, 13 machine learning (ML) models were assessed to predict heating and cooling loads. The best-performing models from this stage were then selected for the subsequent phase to find out the optimal combinations of inputs to predict thermal loads. In this phase, an Integral Feature Selection (IFS) method was employed in conjunction with the best ML models. An extensive evaluation using advanced statistical measures was performed during the evaluation stage. The results reveal that, for each climate, numerous high-accuracy prediction pathways were identified for thermal load prediction, surpassing the confidence level of 99% for R2. The results found here outperformed those reported by other researchers in thermal load predictions for Low-Energy Buildings (LEBs).

A Framework for Collaborative Artificial Intelligence in Marketing

Topological Defects Induced High-Spin Quartet State in Truxene-Based Molecular Graphenoids

Aim: The majority of machine learning (ML) models in healthcare are built on retrospective data, much of which is collected without explicit patient consent for use in artificial intelligence (AI) and ML applications. The primary aim of this study was to evaluate whether clinicians and scientific researchers themselves consent to provide their own data for the training of ML models. Materials and Methods: This survey was conducted through an anonymous online survey, utilizing platforms such as Telegram, LinkedIn, and Viber. The target audience comprised specific international groups, primarily Russian, German, and English-speaking, of clinicians and scientific researchers. These participants ranged in their levels of expertise and experience, from beginners to veterans. The survey centered on a singular, pivotal question: “Do You Consent to the Use of Your Biological and Private Data for Training Machine Learning and AI Models?” Respondents had the option to choose from three responses: “Yes” and “No”. Results: The survey was conducted in January 2024. A total of 119 unique and verified individuals participated in the survey. The results revealed that only 50% of respondents (63 persons) expressed consent to provide their own data for the training of ML and AI models. Conclusion: In the development of ML and AI models, particularly open-source ones, it is crucial to ascertain whether participants are willing to provide their private data. While ML algorithms can transform the nature of data, it is important to remember that the primary owner of this data is the individual. Our findings show that in 50% of the cases, even participants from scientific research and clinical backgrounds – individuals typically accountable for ensuring data quality in AI and ML model development – do not consent to the use of their data in AI and ML settings. This highlights the need for more stringent consent processes and ethical considerations in the utilization of personal data in AI and ML research.

The integration of Artificial Intelligence (AI) in the pharmaceutical sector marks a transformative era, where technology reshapes traditional drug development processes. This paper delves into the pivotal role AI plays in enhancing efficiency and precision in drug discovery, testing, and approval. Existing methods in drug development often suffer from high costs, extended timelines, and high attrition rates in clinical trials, posing significant barriers to timely patient care. To address these issues, we propose the Drug Development based on Artificial Intelligence (DD-AI) framework, leveraging AI-driven algorithms and machine learning models. The DD-AI framework enhances the identification and optimization of drug candidates, predicts clinical trial outcomes, and personalizes patient treatment plans. AI algorithms analyze vast datasets to discover potential drug molecules, simulate their interactions, and streamline the clinical trial process by identifying the most promising candidates. Implementing the DD-AI framework has demonstrated substantial improvements in reducing drug development costs and timelines while increasing the accuracy of trial outcomes. AI's predictive capabilities also personalize treatments, optimizing efficacy and minimizing adverse effects for patients. The DD-AI framework offers a robust solution to existing challenges in drug development, fostering a more efficient, cost-effective, and patient-centered approach. The findings underscore AI's transformative potential in revolutionizing pharmaceutical practices, ultimately benefiting patients with faster and more precise medical solutions.

Robotics and artificial intelligence (AI) are being increasingly integrated in spine surgery. One emerging application of AI is in hand motion detection to assess surgical skill. However, no standardized framework currently exists for evaluating trainee proficiency in spine surgery. This proof-of-concept study applied AI-based motion analysis and the machine learning (ML) pipeline to evaluate hand movements during lumbar pedicle screw placement, aiming to generate objective metrics for skill assessment. AI-based motion tracking was used to analyze hand movements during pedicle screw placement on a lumbar spine sawbone model. Video recordings of hand movements during freehand (FH) and robot-assisted (RB) pedicle screw placement were analyzed to extract metrics including distance, displacement, speed, velocity, acceleration, jerk, and normalized jerk index. Due to the limited number of participants, data augmentation techniques were used to generate synthetic data to expand the dataset. Extracted and derived kinematic metrics were then evaluated for their ability to predict training level and surgical technique. In general, procedure time and movement distance appeared to decrease with increasing trainee experience, with more pronounced improvements in FH procedures. Kinematic analysis trended toward a reduction in speed, displacement, and jerk variability across training years. RB procedures were associated with reduced movement variability as extremes in velocity, acceleration, and jerk were limited. ML models were able to classify augmented data by training level and procedure type with acceptable accuracy. This proof-of-concept study presents a data processing pipeline capable of analyzing metrics to quantify surgical proficiency during spinal procedures. The methods described demonstrate the feasibility of using AI-driven video analysis to assess hand motion. It also highlights specific motion-based metrics that can distinguish between FH and RB techniques and correlate with surgical training level. These findings lay the groundwork for developing a standardized, objective framework for proficiency assessment in spine surgery.

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 223867, “Hybrid Optimization Framework for Well Placement Using Gradient‑Free Algorithm and Physics‑Informed Artificial Intelligence,” by Kheireddine Redouane, SPE, and Ashkan Jahanbani Ghahfarokhi, SPE, Norwegian University of Science and Technology. The paper has not been peer-reviewed. _ Accurate well placement plays an essential role in increasing field recovery and storage while reducing operational costs. This task is complex, requiring robust solutions that can handle optimization problems efficiently. Despite numerous existing solutions, a need remains for a fast, highly accurate, computer-aided optimization tool. In this paper, an auto-adaptive workflow is described that leverages a complex interplay between machine learning (ML), physics of fluid flow, and a gradient-free algorithm to enhance solving well-placement problems. Introduction The study’s main objective is to develop a novel surrogate-based hybrid intelligent system to handle real well-placement problems in realistic field conditions while overcoming the drawbacks of conventional approaches. The authors’ contribution is the development of a self-adaptive optimization routine (SAOR), a physics-informed artificial intelligence (AI) framework designed to enhance the accuracy and efficiency of the optimization process. This is achieved by directing computational resources to regions of interest identified through surrogate interaction with physics-based simulations by means of the proposed adaptive approach. This approach integrates technically constrained design of experiments (DoE) and a genetic algorithm (GA) optimizer to iteratively enrich the database in the regions where optimization is most critical, ensuring that the surrogate models are continuously refined, leading to faster convergence and higher accuracy. The SAOR in this paper is configured with different types of DoE, two adaptive sampling methods, and two ML models—Gaussian process (GP) and an adaptive neuro-fuzzy inference system (ANFIS). This enables the evaluation of multiple physics-informed AI optimization paradigms that allow the SAOR to explore the “no free lunch” theorem in the context of well placement, demonstrating that the effectiveness of an optimization framework depends on its ability to adapt to the specific characteristics of the problem. To the best of the authors’ knowledge, this is the first study presenting a comparison of direct GA with self-adaptive GP and ANFIS for a well-placement optimization problem. The SAOR demonstrated its capability to design efficient hybrid optimizers tailored to the specific challenges of well placement, achieving high accuracy within reasonable central-processing-unit (CPU) time. Moreover, SAOR ensures that solutions not only are optimal but also adhere to the underlying physics and technical constraints of the reservoir system. Methodology To aid in solving complex well-placement challenges for improved reservoir management, the SAOR has been designed to simultaneously consider the following factors: - Reduction of optimization time by minimizing the number of required simulations, particularly by using DoE and adaptive enrichment techniques - Approximation of the expensive numerical model with a surrogate model that offers faster response times - Dynamic exploration of irregular search spaces, avoiding local optimal results that might lead to suboptimal decisions, and refining the global optimum once it is found

With the change of the fourth generation of information technology represented by artificial intelligence, higher education is gradually stepping into the stage of intelligent development, and leisure sports majors as one of the majors that are booming, which puts forward higher requirements on the teaching power of teachers of leisure sports majors. Therefore, studying the current situation of talent cultivation of leisure sports majors under artificial intelligence plays a good role in promoting the overall improvement of the teaching quality of leisure sports majors. In this paper, the current situation of talent training and classroom teaching of leisure sports majors is studied based on a convolutional neural network in the artificial intelligence perspective, and the framework of artificial intelligence technology for leisure sports education is constructed. It can be seen that the attitude of leisure sports students towards theory class is better; 40.93% think theory class is very important, 38.13% think theory class is more important, and 19.27% think the importance of theory class is average. Students’ attitudes toward practical classes were better than those toward theoretical classes, with 63.71% considering practical classes very important and 26.85% considering them more important. From the analysis results, we can see that there are still problems of weak basic theory research, confusion of cultivation goals, lack of science in curriculum setting, and lack of teachers in cultivating talents in leisure sports majors. In summary, this paper argues that colleges and universities and college teachers should take the initiative to apply the technical framework of artificial intelligence in leisure sports education, strengthen theoretical research, make up for the shortcomings of teaching materials, as well as integrate training objectives, improve curriculum settings, enhance faculty strength, investigate the talent specification needs of the market in-depth, proactively meet the arrival of the era of artificial intelligence, and contribute to the cultivation of talents in leisure sports majors.

The authors present a new Framework of Artificial Intelligence which analyzes the key elements of transformational AI in industry. The State of the Art of Artificial Intelligence is gleaned from an examination of what has been done in the past, presently in the last decade and what is predicted for future decades. The paper will highlight the biggest changes in AI, important influencers to adoption/diffusion and give examples of how these technologies have and will be applied in three key industrial sectors, including agriculture, education and healthcare. Next the research examines seven driving triggers of cost, speed, accuracy, diversity/inclusion, interdisciplinary research/collaboration and ethics/trustworthiness that are accelerating AI development and concludes with a discussion of what are the critical success factors for industry to be transformational in AI.

Rearrangements that change the connectivity of a carbon skeleton are often useful in synthesis, but it can be difficult to follow their mechanisms. Scanning probe microscopy can be used to manipulate a skeletal rearrangement at the single-molecule level, while monitoring the geometry of reactants, intermediates and final products with atomic resolution. We studied the reductive rearrangement of 1,1-dibromo alkenes to polyynes on a NaCl surface at 5 K, a reaction that resembles the Fritsch–Buttenberg–Wiechell (FBW) rearrangement. Voltage pulses were used to cleave one C–Br bond, forming a radical, then to cleave the remaining C•–Br bond triggering the rearrangement. These experiments provide structural insight into the bromo-vinyl radical intermediates, showing that the C=C•–Br unit is nonlinear. Long polyynes, up to the octayne Ph–(C≡C)8–Ph, have been prepared in this way. The control of skeletal rearrangements opens a new window on carbon-rich materials and extends the toolbox for molecular synthesis by atom manipulation.

Summary A meticulous interpretation of steady-state or unsteady-state relative permeability (Kr) experimental data is required to determine a complete set of Kr curves. In this work, different machine learning (ML) models were developed to assist in a faster estimation of these curves from steady-state drainage coreflooding experimental runs. These ML algorithms include gradient boosting (GB), random forest (RF), extreme gradient boosting (XGB), and deep neural network (DNN) with a main focus on and comparison of the two latter algorithms (XGB and DNN). Based on existing mathematical models, a leading-edge framework was developed where a large database of Kr and capillary pressure (Pc) curves were generated. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from these simulation runs, mainly pressure drop along with other conventional core analysis data, were used to estimate analytical Kr curves based on Darcy’s law. These analytically estimated Kr curves along with the previously generated Pc curves were fed as features into the ML model. The entire data set was split into 80% for training and 20% for testing. The k-fold cross-validation technique was applied to increase the model’s accuracy by splitting 80% of the training data into 10 folds. In this manner, for each of the 10 experiments, nine folds were used for training and the remaining fold was used for model validation. Once the model was trained and validated, it was subjected to blind testing on the remaining 20% of the data set. The ML model learns to capture fluid flow behavior inside the core from the training data set. In terms of applicability of these ML models, two sets of experimental data were needed as input; the first was the analytically estimated Kr curves from the steady-state drainage coreflooding experiments, while the other was the Pc curves estimated from centrifuge or mercury injection capillary pressure (MICP) measurements. The trained/tested model was then able to estimate Kr curves based on the experimental results fed as input. Furthermore, to test the performance of the ML model when only one set of experimental data is available to an end user, a recurrent neural network (RNN) algorithm was trained/tested to predict Kr curves in the absence of Pc curves as an input. The performance of the three developed models (XGB, DNN, and RNN) was assessed using the values of the coefficient of determination (R2) along with the loss calculated during training/validation of the model. The respective crossplots along with comparisons of ground truth vs. artificial intelligence (AI)-predicted curves indicated that the model is capable of making accurate predictions with an error percentage between 0.2% and 0.6% on history-matching experimental data for all three tested ML techniques. This implies that the AI-based model exhibits better efficiency and reliability in determining Kr curves when compared to conventional methods. The developed ML models by no means replace the need to conduct drainage coreflooding or centrifuge experiments but act as an alternative to existing commercial platforms that are used to interpret experimental data to predict Kr curves. The two main advantages of the developed ML models are their capability of predicting Kr curves within a matter of a few minutes as well as with limited intervention from the end user. The results also include a comparison between classical ML approaches, shallow neural networks, and DNNs in terms of accuracy in predicting the final Kr curves. The research presented here is an extension of the state-of-the-art framework proposed by Mathew et al. (2021). However, the two main aspects of the current study are the application of deep learning for the prediction of Kr curves and the application of feature engineering. The latter not only reduces the training/testing time for the ML models but also enables the end user to obtain the final predictions with the least set of experimental data. The various models discussed in this research work currently focus on the prediction of Kr curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.