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Traffic accidents have become severe risks as they are one of the causes of enormous deaths worldwide. Reducing the number of incidents is critical to saving lives and achieving sustainable cities and communities. Machine learning and data analysis techniques interpret the reasons for car accidents and propose solutions to minimize them. However, this needs to take the benefits of big data solutions as the size and velocity of traffic accident data are increasingly large and rapid. This paper explores road car accident data patterns and proposes a predictive model by investigating meaningful data features, such as accident severity, the number of casualties, and the number of vehicles. Therefore, a pre-processing model is designed to convert raw data using missing and meaningless feature removal, data attribute generalization, and outlier removal using interquartile. Four classification methods, including decision trees, random forest, multinomial logistic regression, and naïve Bayes, are used and evaluated to study the performance of road accident prediction. The results address acceptable levels of accuracy for car accident prediction except for naïve Bayes. The findings are discussed through a data-driven approach to understand the factors influencing road car accidents and highlight the key ones to propose accident prevention solutions. Finally, some strategies are provided to achieve healthy and community-friendly cities.

The availability of an ever‐expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi‐dimensional parameter space and massive data sets involved is emblematic of complex, resource‐intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data‐driven approach and subset of artificial intelligence, is a potential game‐changer, enabling a cheaper – yet more efficient – alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine‐assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area. The family of 2D materials is an unprecedented platform for materials by design, thanks to their ever‐expanding material portfolio with rich internal degrees of freedom. The study provides a comprehensive overview of the recent progress, challenges and emerging opportunities in a frontier research area that exploits machine learning—a very powerful data‐driven approach and subset of artificial intelligence—for 2D materials.

Accurate prediction of relative permeability is essential for continuum‐scale simulations of multiphase flow in porous media. This study presents a workflow that couples a thermodynamic model with a data‐driven approach to estimate relative permeability directly from continuum‐scale wettability. By leveraging a pre‐trained neural network, the method bypasses the need for repetitive pore‐scale simulations and rapidly predicts permeability based on fluid configurations. Validation using μ ${\\upmu }$CT images of a glass bead pack confirms the accuracy and physical consistency of the predictions. Integration with the MATLAB Reservoir Simulation Toolbox framework demonstrates the workflow's scalability and ease of application. This approach offers an innovative and efficient solution for modeling complex multiphase flow, advancing the computational tools available for large‐scale porous media simulations.

To decrease human and economic damage owing to earthquakes, it is necessary to discover signals preceding earthquakes. We focus on the concept of “early warning signals” developed in bifurcation analysis, in which an increase in the variances of variables precedes its transition. If we can treat earthquakes as one of the transition phenomena that moves from one state to the other state, this concept is useful for detecting earthquakes before they start. We develop a covariance matrix from multi-channel time series data observed by an observatory on the seafloor and calculate the first eigenvalue and corresponding eigenstate of the matrix. By comparing the time dependence of the eigenstate to some past earthquakes, it is shown that the contribution from specific observational channels to the eigenstate increases before earthquakes, and there is a case in which the eigenvalue increases as predicted in early warning signals. This result suggests the first eigenvalue and eigenstate of multi-channel data are useful to identify signals preceding earthquakes.

Making computers automatically extract latent scientific knowledge from literature is highly desired for future materials and chemical research in the artificial intelligence era. Herein, the natural language processing (NLP)‐based machine learning technique to build language models and automatically extract hidden information regarding perovskite solar cell (PSC) materials from 29 060 publications is employed. The concept that there are light‐absorbing materials, electron‐transporting materials, and hole‐transporting materials in PSCs is successfully learned by the NLP‐based machine learning model without a time‐consuming human expert training process. The NLP model highlights a hole‐transporting material that receives insufficient attention in the literature, which is then elaborated via density functional theory calculations to provide an atomistic view of the perovskite/hole‐transporting layer heterostructures and their optoelectronic properties. Finally, the above results are confirmed by device experiments. The present study demonstrates the viability of NLP as a universal machine learning tool to extract useful information from existing publications. The natural language processing‐based machine learning technique can quickly explore literature and is employed to predict some novel materials for hole transport in perovskite solar cells. Furthermore, experimental validation in the device is performed.

Aim Prediction of future psychosis in individuals with obsessive and compulsive (OC) symptoms is crucial for treatment choice, but only a few predictors have been revealed. Although OC symptoms and psychotic experiences (PEs) are common in adolescence, no studies have revealed the predictors of subsequent PEs in adolescents with OC symptoms. We aimed to explore the predictors for subsequent PEs among adolescents with OC symptoms, using a data‐driven machine‐learning approach on an adolescent cohort. Methods We used data from a cohort study on the general population of adolescents in Tokyo (n = 3171 at age 10). Data were collected at age 10, 12, 14, and 16. We focused on a subgroup of participants who had OC symptoms at age 12. Participants who had PEs at age 10 were excluded. A machine learning method was utilized to explore over 600 potential predictors at baseline, distinguishing between those who had an onset of PEs after age 14 (n = 45) and those who never had PEs (n = 99). Results The predicting model demonstrated a good performance (test area under the curve = 0.80 ± 0.05). Other than known risk factors for PEs, novel predictors of subsequent PEs among adolescents with OC symptoms included: lack of interaction with people of different ages, desire to be like their father in the future, and nonworking of primary caregiver when they were 5 years old. Not sharing their belongings readily with other children was a strong predictor of having no PEs. Conclusion Close‐knit family bonds and limited social connections outside the family predict the later PEs among adolescents with OC symptoms.

ABSTRACT Machine learning (ML) enables computers to learn from experience by identifying patterns and trends. Despite ML's advancements in extracting valuable data, there are instances necessitating the removal or deletion of certain data, as ML models can inadvertently memorize training data. In many cases, ML models may memorize sensitive or personal data, raising concerns about data privacy and security. Machine unlearning (MU) techniques offer a solution to these concerns by selectively removing sensitive data from trained models without significantly compromising their performance. Similarly, we can analyze and evaluate whether MU can successfully achieve the “right to be forgotten.” In this paper, we investigate various MU approaches regarding their accuracy and potential applications. Experiments have shown that the data‐driven approach emerged as the most efficient method in terms of both time and accuracy, achieving a high level of precision with a minimal number of training epochs. When fine‐tuning, the full test error rises somewhat to 14.57% from the baseline model's 14.28%. One approach shows a high forget error of 99.90% with a full test error of 20.68%, while retraining yields a 100% forget error and a test error of 21.37%. While error‐minimizing noise preserves performance, the SCRUB technique results in a 21.08% test error and an 81.05% forget error, in contrast to the degradation brought on by error‐maximizing noise. On the other hand, the agnostic approach displayed sluggishness and generated less accurate results compared to the data‐driven approach. Furthermore, the choice of approach may depend on the unique requirements of the task and the available training resources. This study investigates MU approaches regarding their accuracy and potential applications.

Wildfires drive deforestation that causes various losses. Although many studies have used spatial approaches, a multi-dimensional analysis is required to determine priority areas for mitigation. This study identified priority areas for wildfire mitigation in Indonesia using a multi-dimensional approach including disaster, environmental, historical, and administrative parameters by integrating 20 types of multi-source spatial data. Spatial data were combined to produce susceptibility, carbon stock, and carbon emission models that form the basis for prioritization modelling. The developed priority model was compared with historical deforestation data. Legal aspects were evaluated for oil-palm plantations and mining with respect to their impact on wildfire mitigation. Results showed that 379,516 km2 of forests in Indonesia belong to the high-priority category and most of these are located in Sumatra, Kalimantan, and North Maluku. Historical data suggest that 19.50% of priority areas for wildfire mitigation have experienced deforestation caused by wildfires over the last ten years. Based on legal aspects of land use, 5.2% and 3.9% of high-priority areas for wildfire mitigation are in oil palm and mining areas, respectively. These results can be used to support the determination of high-priority areas for the REDD+ program and the evaluation of land use policies.

The design of complex molecular machines and devices represents one of the most ambitious frontiers in nanotechnology, synthetic chemistry, and molecular engineering. These intricate systems, inspired by biological machines, require precise control over atomic and electronic interactions to achieve desired functionalities. Theoretical modeling plays a crucial role in this process, offering predictive insights into molecular behavior, guiding experimental design, and optimizing performance. Methods such as density functional theory, quantum theory of atoms in molecules coupled with widely adopted and distinctive visualization methods, molecular dynamics simulations, and quantum mechanical/molecular mechanical hybrid approaches provide analytical information into the stability in terms of mutual chemical interactions and conformational shaping of flexible supramolecular aggregates for nanotechnological applications. Theoretical approaches also facilitate interdisciplinary integration, bridging chemistry, physics, and materials science to create conceptually hybrid devices with enhanced performance. Machine learning and artificial intelligence are now being incorporated into theoretical modeling, accelerating the discovery and refinement of novel molecular architectures. This fusion of data‐driven approaches with traditional computational chemistry algorithms is expected to revolutionize the design paradigm of soft molecular machines and devices. Theoretical approaches facilitate interdisciplinary integration, bridging chemistry, physics, and materials science to create conceptually hybrid devices with enhanced performance. Machine learning and artificial intelligence are now being incorporated into theoretical modeling, accelerating the discovery of novel molecular architectures. This fusion is expected to revolutionize the design paradigm of soft molecular machines and devices.

The present application note summarizes an advanced methodology that allows for deriving potential‐dependent volcano curves for energy storage and conversion processes. The conventional approach relies on the combination of density functional theory calculations and scaling relations for a single mechanistic pathway as well as a discussion of electrocatalytic activity by means of the potential‐determining step, determined at the equilibrium potential of the reaction. Herein, it is illustrated how several reaction mechanisms can be factored into the volcano curve and how the rate‐determining step based on the descriptor Gmax(U) can be derived by a rigorous thermodynamic analysis of adsorption free energies fed by a data‐inspired methodology.