This research paper explores the importance of optimizing the performance of electric vehicle (EV) batteries to align with the rapid growth in EV usage. It uses predictive machine learning (ML) techniques to achieve t...
详细信息
This research paper explores the importance of optimizing the performance of electric vehicle (EV) batteries to align with the rapid growth in EV usage. It uses predictive machine learning (ML) techniques to achieve this optimization. The paper covers various ML methods like supervised, unsupervised, and deep learning (DL) and ways to measure their effectiveness. Significant battery performance factors, such as state of charge (SoC), state of health (SoH), state of function (SoF), and remaining useful life (RUL), are discussed, along with methods to collect and prepare data for accurate predictions. The paper introduces an operation research model for optimizing the performance of EV Batteries. It also looks at challenges unique to battery systems and ways to overcome them. The study showcases ML models' ability to predict battery behavior for real-time monitoring, efficient energy use, and proactive maintenance. The paper categorizes different applications and case studies, providing valuable insights and forward-looking perspectives for researchers, practitioners, and policymakers involved in improving EV battery performance through predictive ML.
The expansion of marine protected areas (MPAs) is a core focus of global conservation efforts, with the "30x30" initiative to protect 30% of the ocean by 2030 serving as a prominent example of this trend. We...
详细信息
The expansion of marine protected areas (MPAs) is a core focus of global conservation efforts, with the "30x30" initiative to protect 30% of the ocean by 2030 serving as a prominent example of this trend. We consider a series of proposed MPA network expansions of various sizes, and we forecast the impact this increase in protection would have on global patterns of fishing effort. We do so by building a predictive machine learning model trained on a global dataset of satellite-based fishing vessel monitoring data, current MPA locations, and spatiotemporal environmental, geographic, political, and economic features. We then use this model to predict future fishing effort under various MPA expansion scenarios compared to a business-as-usual counterfactual scenario that includes no new MPAs. The difference between these scenarios represents the predicted change in fishing effort associated with MPA expansion. We find that regardless of the MPA network objectives or size, fishing effort would decrease inside the MPAs, though by much less than 100%. Moreover, we find that the reduction in fishing effort inside MPAs does not simply redistribute outside-rather, fishing effort outside MPAs would also decline. The overall magnitude of the predicted decrease in global fishing effort principally depends on where networks are placed in relation to existing fishing effort. MPA expansion will lead to a global redistribution of fishing effort that should be accounted for in network design, implementation, and impact evaluation.
Biomolecular condensates are increasingly recognized as important drivers of cellular function;their dysregulation leads to pathology and disease. We discuss three questions in terms of the impending utility of data-d...
详细信息
Biomolecular condensates are increasingly recognized as important drivers of cellular function;their dysregulation leads to pathology and disease. We discuss three questions in terms of the impending utility of data-driven techniques to predict condensate-driven biological outcomes, i.e., the impact of cellular state changes on condensates, the effect of condensates on biochemical processes within, and condensate properties that result in cellular dysregulation and disease.
The triad of Environmental, Social, and Governance (ESG) determinants in the contemporary global business environment has become integral to corporate longevity and ethical performance. With China's significant ec...
详细信息
The triad of Environmental, Social, and Governance (ESG) determinants in the contemporary global business environment has become integral to corporate longevity and ethical performance. With China's significant economic influence and unique socio-cultural tapestry, understanding its ESG dynamics is crucial for domestic and international stakeholders. This comprehensive research introduces an innovative multidimensional ESG scoring paradigm designed for the Chinese market. Built on a robust data foundation from Bloomberg, this model encompasses an 11-year trajectory of 1,496 companies, making it one of the most exhaustive studies in this domain. Integrating advanced mathematical and machinelearning models and techniques augments the model's predictive accuracy and ensures its resilience to rigorous validation processes. By delving deep into the complexities of China's ESG landscape, this study elucidates the intricate interdependencies of these factors, thereby offering a roadmap for sustainable business practices in China.
This study assesses the impact of One-Hot and Target Encoding techniques on the accuracy of predicting bachelor's degree final marks in economics and management. Employing regression models across six machine lear...
详细信息
ISBN:
(纸本)9783031686597;9783031686603
This study assesses the impact of One-Hot and Target Encoding techniques on the accuracy of predicting bachelor's degree final marks in economics and management. Employing regression models across six machinelearning algorithms -Linear Regression, Support Vector machine, Decision Tree, Random Forest, XGBoost, and Neural Network- we analyze a comprehensive dataset from Hassan II University of Casablanca. This dataset includes both demographic and academic performance data of students. We focus on the application of encoding methods to categorical variables and evaluate model performances based onMean Squared Error (MSE) and Mean Absolute Error (MAE). Our findings highlight the differential effectiveness of encoding techniques in enhancing the precision of predictive models for academic outcomes.
Nanoparticles (NPs) are a wide class of materials currently used in several industrial and biomedical applications. Due to their small size (1-100 nm), NPs can easily enter the human body, inducing tissue damage. NP t...
详细信息
Nanoparticles (NPs) are a wide class of materials currently used in several industrial and biomedical applications. Due to their small size (1-100 nm), NPs can easily enter the human body, inducing tissue damage. NP toxicity depends on physical and chemical NP properties (e.g., size, charge and surface area) in ways and magnitudes that are still unknown. We assess the average as well as the individual importance of NP atomic descriptors, along with chemical properties and experimental conditions, in determining cytotoxicity endpoints for several nanomaterials. We employ a multicenter cytotoxicity nanomaterial database (12 different materials with first and second dimensions ranging between 2.70 and 81.2 nm and between 4.10 and 4048 nm, respectively). We develop a regressor model based on extreme gradient boosting with hyperparameter optimization. We employ Shapley additive explanations to obtain good cytotoxicity prediction performance. Model performances are quantified as statistically significant Spearman correlations between the true and predicted values, ranging from 0.5 to 0.7. Our results show that i) size in situ and surface areas larger than 200 nm and 50 m2/g, respectively, ii) primary particles smaller than 20 nm;iii) irregular (i.e., not spherical) shapes and iv) positive Z-potentials contribute the most to the prediction of NP cytotoxicity, especially if lactate dehydrogenase (LDH) assays are employed for short experimental times. These results were moderately stable across toxicity endpoints, although some degree of variability emerged across dose quantification methods, confirming the complexity of nano-bio interactions and the need for large, systematic experimental characterization to reach a safer-by-design approach.
Wearable Internet of Medical Things (IoMT) technology, designed for non-invasive respiratory monitoring, has demonstrated considerable promise in the early detection of severe diseases. This paper introduces the appli...
详细信息
Wearable Internet of Medical Things (IoMT) technology, designed for non-invasive respiratory monitoring, has demonstrated considerable promise in the early detection of severe diseases. This paper introduces the application of supervised machinelearning techniques to predict respiratory abnormalities through frequency data analysis. The principal aim is to identify respiratory-related health risks in older adults using data collected from non-invasive wearable devices. This article presents the development, assessment, and comparison of three machinelearning models, underscoring their potential for accurately predicting respiratory-related health issues in older adults. The convergence of wearable IoMT technology and machinelearning holds immense potential for proactive and personalized healthcare among older adults, ultimately enhancing their quality of life.
The incidence of most diseases varies greatly with seasons, and global climate change is expected to increase its risk. predictive models that automatically capture trends between climate and diseases are likely to be...
详细信息
ISBN:
(纸本)9781728183923
The incidence of most diseases varies greatly with seasons, and global climate change is expected to increase its risk. predictive models that automatically capture trends between climate and diseases are likely to be beneficial in minimizing disease outbreaks. machinelearning (ML) predictive analytic tools have been popularized across many health-care applications, however the optimal task performance of such ML tools largely depends on manual parameter tuning and calibration. Such manual tuning significantly limits the full potential of ML methods, especially for high-dimensional and complex task domains, as typified by real-world health-care application data-sets. Additionally, the inaccessibility of many health-care data-sets compounds innate problems of method comparison, predictive accuracy and the overall advancement of ML based health-care applications. In this study we investigate the impact of Relevance Estimation and Value Calibration, an evolutionary parameter optimization method applied to automate parameter tuning for comparative ML methods (Deep learning and Support Vector machines) applied to predict daily diarrhoea cases across various geographic regions. Data-augmentation is also used to complement real-world noisy, sparse and incomplete data-sets with synthetic data-sets for training, validation and testing. Results support the efficacy of evolutionary parameter optimization and data synthesis to boost predictive accuracy in the given task, indicating a significant prediction accuracy boost for the deep-learning models across all data-sets.
暂无评论