Fundamental electronic structure and multiatomic bonding in 13 biocompatible high-entropy alloys

High-entropy alloys(HEAs)have attracted great attention due to their many unique properties and potential applications.The nature of interatomic interactions in this unique class of complex multicomponent alloys is not fully developed or understood.We report a theoretical modeling technique to enable in-depth analysis of their electronic structures and interatomic bonding,and predict HEA properties based on the use of the quantum mechanical metrics,the total bond order density(TBOD)and the partial bond order density(PBOD).Application to 13 biocompatible multicomponent HEAs yields many new and insightful results,including the inadequacy of using the valence electron count,quantification of large lattice distortion,validation of mechanical properties with experiment data,modeling porosity to reduce Young’s modulus.This work outlines a road map for the rational design of HEAs for biomedical applications.

Serrated flow in NaI:Tl scintillator crystals

The serrated-flow behavior is an important phenomenon that unveils material-deformation mechanisms,as reported for various kinds of materials.NaI doped with Tl(NaI:Tl)is unique among scintillation ma-terials in that the structure contains glide planes that are linked to serration behavior.In the present work,single crystals of NaI:Tl were subjected to room-temperature compression experiments at different strain rates.The serrated flow was observed,and complexity and multifractal analyses were performed to analyze the serration behavior.The findings revealed that the strain rate had a pronounced effect on the complexity and multifractality of the serrated flow,similar to what has been found in other alloy systems.The results also indicate that there may be a strong link between the complexity of the serrated flow behavior and the heterogeneity of the underlying dynamics.It is expected that the present work could be a step toward a better understanding of the deformation behavior and forgeability of NaI:Tl single crystals.

Explainable artificial intelligence in breast cancer detection and risk prediction:A systematic scoping review

With the advances in artificial intelligence(AI),data‐driven algorithms are becoming increasingly popular in the medical domain.However,due to the nonlinear and complex behavior of many of these algorithms,decision‐making by such algorithms is not trustworthy for clinicians and is considered a blackbox process.Hence,the scientific community has introduced explainable artificial intelligence(XAI)to remedy the problem.This systematic scoping review investigates the application of XAI in breast cancer detection and risk prediction.We conducted a comprehensive search on Scopus,IEEE Explore,PubMed,and Google Scholar(first 50 citations)using a systematic search strategy.The search spanned from January 2017 to July 2023,focusing on peer‐reviewed studies implementing XAI methods in breast cancer datasets.Thirty studies met our inclusion criteria and were included in the analysis.The results revealed that SHapley Additive exPlanations(SHAP)is the top model‐agnostic XAI technique in breast cancer research in terms of usage,explaining the model prediction results,diagnosis and classification of biomarkers,and prognosis and survival analysis.Additionally,the SHAP model primarily explained tree‐based ensemble machine learning models.The most common reason is that SHAP is model agnostic,which makes it both popular and useful for explaining any model prediction.Additionally,it is relatively easy to implement effectively and completely suits performant models,such as tree‐based models.Explainable AI improves the transparency,interpretability,fairness,and trustworthiness of AI‐enabled health systems and medical devices and,ultimately,the quality of care and outcomes.

Quasi-compensatory effect in emerging anode-free lithium batteries

As electric vehicle(EV)sales grew approximately 50%year-over-year,surpassing 3.2 million units in 2020,the“roaring era”of EV is around the corner.To meet the increasing demand for low cost and high energy density batteries,anode-free configuration,with no heavy and voluminous host material on the current collector,has been proposed and further investigated.Nevertheless,it always suffers from several non-negligible“bottlenecks”,such as fragile solid electrolyte interface,deteriorated cycling reversibility,and uncontrolled dendrite formation.Inspired by the“compensatory effect”of some disabled people with other specific functions strengthened to make up for their inconvenience,corresponding quasi-compensatory measures after anode removal,involving dimensional compensation,SEI robustness compensation,lithio-philicity compensation,and lithium source compensation,have been carried out and achieved significant battery performance enhancement.In this review,the chemistry,challenges,and rationally designed“quasi-compensatory effect”associated with anode-free lithium-ion battery are systematically discussed with several possible R&D directions that may aid,direct,or facilitate future research on lithium storage in anode-free configuration essentially emphasized.

Electricity consumption patterns within cities:application of a data-driven settlement characterization method

Urban areas presently consume around 75%of global primary energy supply,which is expected to significantly increase in the future due to urban growth.Having sustainable,universal energy access is a pressing challenge for most parts of the globe.Understanding urban energy consumption patterns may help to address the challenges to urban sustainability and energy security.However,urban energy analyses are severely limited by the lack of urban energy data.Such datasets are virtually non-existent for the developing countries.As per current projections,most of the new urban growth is bound to occur in these data-starved regions.Hence,there is an urgent need of research methods for monitoring and quantifying urban energy utilization patterns.Here,we apply a data-driven approach to characterize urban settlements based on their formality,which is then used to assess intraurban urban energy consumption in Johannesburg,South Africa;Sana’a,Yemen;and Ndola,Zambia.Electricity is the fastest growing energy fuel.By analyzing the relationship between the settlement types and the corresponding nighttime light emission,a proxy of electricity consumption,we assess the differential electricity consumption patterns.Our study presents a simple and scalable solution to fill the present data void to understand intra-city electricity consumption patterns.

Estimating geographic variation of infection fatality ratios during epidemics

Objectives We aim to estimate geographic variability in total numbers of infections and infection fatality ratios(IFR;the number of deaths caused by an infection per 1,000 infected people)when the availability and quality of data on disease burden are limited during an epidemic.Methods We develop a noncentral hypergeometric framework that accounts for differential probabilities of positive tests and reflects the fact that symptomatic people are more likely to seek testing.We demonstrate the robustness,accuracy,and precision of this framework,and apply it to the United States(U.S.)COVID-19 pandemic to estimate county-level SARS-CoV-2 IFRs.Results The estimators for the numbers of infections and IFRs showed high accuracy and precision;for instance,when applied to simulated validation data sets,across counties,Pearson correlation coefficients between estimator means and true values were 0.996 and 0.928,respectively,and they showed strong robustness to model misspecification.Applying the county-level estimators to the real,unsimulated COVID-19 data spanning April 1,2020 to September 30,2020 from across the U.S.,we found that IFRs varied from 0 to 44.69,with a standard deviation of 3.55 and a median of 2.14.Conclusions The proposed estimation framework can be used to identify geographic variation in IFRs across settings.