We demonstrate the application of free-to-use and easy-to-implement Bayesian optimization (BO) software to streamline atomic layer deposition (ALD) process optimization. By employing machine learning-based Bayesian optimization algorithms, we enhanced the silicon surface passivation quality of titanium dioxide layers deposited using titanium tetraisopropoxide (TTIP). Unlike classical designs of experimental methods, such as Box-Behnken or Plackett-Burman designs, which require a predefined set of experiments and can become resource intensive, BO offers several advantages. It dynamically updates the search strategy based on previous outcomes, allowing for efficient exploration of parameter spaces with fewer experimental runs. This adaptive approach is particularly advantageous in small-scale experiments or laboratories where time, resources, and materials are limited. In a single-objective optimization experiment, we identified constrained search spaces that limited further optimization, underscoring the importance of properly defined parameter bounds prior to the optimization process. Our findings highlight that Bayesian optimization can not only reduce time and resource costs associated with ALD process optimization but also support faster discovery of more optimal ALD process parameters, even with minimal prior knowledge of the deposition process or precursor chemistry.
Department of Mechanical Engineering, Institute of Advanced Machines and Design, Institute of Engineering Research, Seoul National University, Seoul, Republic of Korea.
Soft sensors integrated or attached to robots or human bodies enable rapid and accurate estimation of the physical states of the target systems, including position, orientation, and force. While the use of a number of sensors enhances precision and reliability in estimation, it may constrain the movement of the target system or make the entire system complex and bulky. This article proposes a rapid, efficient framework for determining where to place the sensors on the system given the limited number of available sensors.
Purpose: To evaluate the efficacy of prominent machine learning algorithms in predicting normal tissue complication probability using clinical data obtained from 2 distinct disease sites and to create a software tool that facilitates the automatic determination of the optimal algorithm to model any given labeled data set.
Methods And Materials: We obtained 3 sets of radiation toxicity data (478 patients) from our clinic: gastrointestinal toxicity, radiation pneumonitis, and radiation esophagitis. These data comprised clinicopathological and dosimetric information for patients diagnosed with non-small cell lung cancer and anal squamous cell carcinoma.
INSERM UMR 1138, Team 22, Information Science to Support Personalized Medicine, Centre de Recherche des Cordeliers, Université de Paris, 15 rue de l'école de médecine 75006 Paris, France; Radiation Oncology, Hôpital Européen Georges Pompidou, 20 rue Leblanc 75015 Paris, France.
Introduction: Patients with a head and neck (HN) cancer undergoing radiotherapy risk critical weight loss and oral intake reduction leading to enteral nutrition. We developed a predictive model for the need for enteral nutrition during radiotherapy in this setting. Its performances were reported on a real-world multicentric cohort.
Purpose: Novel therapies targeting specific genomic alterations are a promising treatment approach for relapsed/refractory cancer. Patients with specific alterations may be more likely to respond. Trial designs should maximize opportunities for such patients to enroll on these trials.
Department of Colorectal Surgery, The Affiliated Xuzhou Clinical College of Xuzhou Medical University, Xuzhou Central Hospital, Xuzhou, Jiangsu, China.
Background: The optimal second-line systemic treatment for metastatic colorectal cancer (mCRC) is inconclusive.
Methods: We searched PubMed, Web of Science, EMBASE, and Cochrane Library for RCTs comparing second-line systemic treatments for mCRC from the inception of each database up to February 3, 2024. Markov Chain Monte Carlo (MCMC) technique was used in this network meta-analysis (NMA) to generate the direct and indirect comparison results among multiple treatments in progression-free survival (PFS), overall response rate (ORR), overall survival (OS), complete response (CR), partial response (PR), grade 3 and above adverse events (Grade ≥ 3AE), and any adverse events (Any AE).
