Machine-Learning-Enabled Ligand Screening for Cs/Sr Crystallizing Separation.

The reprocessing of spent nuclear fuel is critical for the sustainability of the nuclear energy industry. However, several key separation processes present challenges in this regard, calling for continuous research into next-generation separation materials. Herein, we propose a high-throughput screening framework to improve efficiency in identifying potential ligands that selectively coordinate metal cations of interest in liquid wastes that considers multiple key chemical characteristics, including aqueous solubility, pKa, and coordination bond length. Machine-learning models were designed for the fast and accurate prediction of these characteristics by using graph convolution and transfer-learning techniques. Suitable ligands for Cs/Sr crystallizing separation were identified through the "computational funnel", and several top-ranking, nontoxic, low-cost ligands were selected for experimental verification.

Potential of vibrational spectroscopy coupled with machine learning as a non-invasive diagnostic method for COVID-19.

BACKGROUND AND OBJECTIVE: Efforts to alleviate the ongoing coronavirus disease 2019 (COVID-19) crisis showed that rapid, sensitive, and large-scale screening is critical for controlling the current infection and that of ongoing pandemics. METHODS: Here, we explored the potential of vibrational spectroscopy coupled with machine learning to screen COVID-19 patients in its initial stage. Herein presented is a hybrid classification model called grey wolf optimized support vector machine (GWO-SVM). The proposed model was tested and comprehensively compared with other machine learning models via vibrational spectroscopic fingerprinting including saliva FTIR spectra dataset and serum Raman scattering spectra dataset. RESULTS: For the unknown vibrational spectra, the presented GWO-SVM model provided an accuracy, specificity and F1_score value of 0.9825, 0.9714 and 0.9778 for saliva FTIR spectra dataset, respectively, while an overall accuracy, specificity and F1_score value of 0.9085, 0.9552 and 0.9036 for serum Raman scattering spectra dataset, respectively, which showed superiority than those of state-of-the-art models, thereby suggesting the suitability of the GWO-SVM model to be adopted in a clinical setting for initial screening of COVID-19 patients. CONCLUSIONS: Prospectively, the presented vibrational spectroscopy based GWO-SVM model can facilitate in screening of COVID-19 patients and alleviate the medical service burden. Therefore, herein proof-of-concept results showed the chance of vibrational spectroscopy coupled with GWO-SVM model to help COVID-19 diagnosis and have the potential be further used for early screening of other infectious diseases.

Machine Learning-Enabled Framework for High-Throughput Screening of MOFs: Application in Radon/Indoor Air Separation.

Radon and its progeny may cause severe health hazards, especially for people working in underground spaces. Therefore, in this study, a hybrid artificial intelligence machine learning-enabled framework is proposed for high-throughput screening of metal-organic frameworks (MOFs) as adsorbents for radon separation from indoor air. MOFs from a specific database were initially screened using a pore-limiting diameter filter. Subsequently, random forest classification and grand canonical Monte Carlo simulations were implemented to identify MOFs with a high adsorbent performance score (APS) and high regenerability (R %). Interpretability and trustworthiness were determined by variable importance analysis , and adsorption mechanisms were elucidated by calculating the adsorption sites using Materials Studio. Notably, two MOF candidates were discovered with higher APS values in both the radon/N2 and radon/O2 systems compared with that of ZrSQU which is the best-performing MOF thus far, with R % values exceeding 85%. Furthermore, the proposed framework can be flexibly applied to multiple data sets due to good performance in model transfer. Therefore, the proposed framework has the potential to provide guidelines for the strategic design of MOFs for radon separation.

Machine-Learning-Guided Identification of Coordination Polymer Ligands for Crystallizing Separation of Cs/Sr.

Separation of Cs/Sr is one of many coordination-chemistry-centered processes in the grand scheme of spent nuclear fuel reprocessing, a critical link for a sustainable nuclear energy industry. To deploy a crystallizing Cs/Sr separation technology, we planned to systematically screen and identify candidate ligands that can efficiently and selectively bind to Sr2+ and form coordination polymers. Therefore, we mined the Cambridge Structural Database for characteristic structural information and developed a machine-learning-guided methodology for ligand evaluation. The optimized machine-learning model, correlating the molecular structures of the ligands with the predicted coordinative properties, generated a ranking list of potential compounds for Cs/Sr selective crystallization. The Sr2+ sequestration capability and selectivity over Cs+ of the promising ligands identified (squaric acid and chloranilic acid) were subsequently confirmed experimentally, with commendable performances, corroborating the artificial-intelligence-guided strategy.

Factors Influencing Private Hospitals' Participation in the Innovation of Biomedical Engineering Industry: A Perspective of Evolutionary Game Theory.

The innovation of the biomedical engineering (BME) industry is inseparable from its cooperation with medical institutions. China has considerable medical institutions. Although private hospitals account for more than half of Chinese medical institutions, they rarely participate in biomedical engineering industry innovation. This paper analyzed the collaborative relationship among biomedical engineering enterprises, universities, research institutes, public hospitals and private hospitals through evolutionary game theory and discussed the influence of different factors on the collaborative innovation among them. A tripartite evolutionary game model is established which regards private hospitals as a stakeholder. The results show that (1) the good credit of private hospitals has a positive effect on their participation in collaborative innovation; (2) it is helpful for BME collaborative innovation to enhance the collaborative innovation ability of partners; (3) the novelty of innovation projects has an impact on BME collaborative innovation. The specific impacts depend on the revenue, cost and risk allocation ratio of innovation partners; (4) the higher the practicability of innovation projects, the more conducive to collaborative innovation.

Integrated Modeling of Transfer Learning and Intelligent Heuristic Optimization for Steam Cracking Process.

The construction and expansion of steam cracking plants and feedstock diversification have resulted in a significant demand for the numerical simulation and optimization of models to achieve molecular refining and intelligent manufacturing. However, the existing models cannot be widely applied in industrial practice because of the high computational expense, time-consumption, and data size requirements. In this paper, a high-performance optimization process, which integrates transfer learning and a heuristic algorithm, is proposed for the optimization of furnaces for various feedstocks. An effective transfer learning structure, based on motif feature of the reaction network, is designed and subsequent product distribution prediction program is compiled. Then a hybrid genetic algorithm and particle swarm optimization method is applied for the coil outlet temperature (COT) curve optimization using the derived prediction model, and the results are obtained for different pricing policies of products. The results are determined based on the weight coefficients of prices for different products, and could be further explained by the yield distribution pattern and reaction mechanism.

Reducing carbon emissions from collaborative distribution: a case study of urban express in China.

The booming development of e-commerce has brought about rapid growth in the express delivery industry in China. However, urban express distribution is increasingly difficult and seriously affecting the traffic, safety, and environmental conditions of cities due to small, scattered end points, unreasonable allocation of resources, and seriously repeated resource waste. Therefore, there is an urgent need to solve the problems associated with the unreasonable resource allocation of express distribution. In the context of green logistics, a new mode of collaborative distribution based on intelligent end service station (IESS) is proposed. Following the measurement models of carbon emissions, before and after collaborative distributions are provided to prove the environmental benefits of the new mode. The influencing factors considered in the models are the average daily distribution volume, number of distribution sections, vehicle ownership of various types, and their capacity, use, fuel, and power consumption. To verify the models' validity, we conduct an empirical study of five express enterprises in China and make a comparative analysis on the results, which show that the implementation of collaborative distribution can extremely reduce carbon emissions and improve the overall load rate of vehicles. Specially, the use of new energy vehicles can contribute significantly to energy conservation and emissions reduction.

Construction of a Bacillus amyloliquefaciens strain for high purity levan production.

Bacillus amyloliquefaciens NK-1 has the potential to produce levan and poly-gamma-glutamic acid (γ-PGA) simultaneously. However, it is not possible to purify each single product from the same strain because the extraction process is identical. We deleted the pgs cluster (for γ-PGA synthesis) from the NK-1 strain and constructed a γ-PGA-deficient NK-ΔLP strain. Nuclear magnetic results showed that the NK-ΔLP strain could produce high purity levan product. However, its levan titer was only 1.96 g L(-1) in the basal medium. Single-factor experimental and response surface methodology was used to optimize the culture condition, leading to levan titer of 13.9 and 22.6 g L(-1) in flask culture and in a 5-L bioreactor, respectively. The levan purity can reach to 92.7% after 48 h cultivation. Furthermore, the relationship between levanase (LevB) and levan molecular weight was studied. The results showed that LevB resulted in the production of low molecular weight levan and its expression level determined the ratio of high and low molecular weight levan. We also deleted the sac cluster (for levan synthesis) from the NK-1 strain and constructed a levan-deficient NK-L strain. The NK-L strain exhibited increased purity of γ-PGA product from 79.5 to 91.2%.