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0.7Bi(1−x)NdxFeO3–0.3BaTiO3(BNFO–BTO, x=0.005, 0.010, 0.020 and 0.050) ceramics were fabricated using the high-temperature solid-state reaction method. The X-ray diffraction (XRD) patterns revealed that the primary phase in these ceramics was pseudocubic. The Scanning Electron Microscopy (SEM) micrographs exhibited dense microstructures throughout all BNFO–BTO ceramics. Furthermore, the temperature dependence of dielectric behaviors and ferroelectric hysteresis loop shapes suggested the occurrence of a relaxor ferroelectric-type phase transition in BNFO–BTO ceramics. Additionally, the frequency dispersion characteristics and remnant polarization were enhanced with increasing substitution of Bi3+by Nd3+. Conducted impedance analysis on 0.7Bi(1−x)NdxFeO3–0.3BaTiO3ceramics and two dielectric responses of the grain at high frequency and grain boundary at low frequency were illustrated, respectively. The combination of imaginary impedance (Z″)and imaginary modulus (M″)versus logfplots revealed that the charge carriers’ motion was not entirely long-range, short-range migration also exists. Through calculations based on Curie–Weiss Law, the relaxation activation energy (Ea)and conductance activation energy (Ec)were investigated, confirming that doping Nd3+effectively mitigates the concentration of oxygen vacancies (OVs) and prevents the formation of oxygen vacancies clusters, ultimately suppressing conductivity in BNFO–BTO ceramics.

Dear Editor, This letter deals with the joint slot scheduling and power allocation in clustered underwater acoustic sensor networks (UASNs), based on the known clustering and routing information, to maximize the network's energy efficiency (EE). Based on the block coordinated decent (BCD) method, the formulated mixed-integer non-convex problem is alternatively optimized by leveraging the Kuhn-Munkres algorithm, the Dinkelbach's method and the successive convex approximation (SCA) technique. Numerical results show that the proposed scheme has a better performance in maximizing EE compared to the separate optimization methods.

Artificial intelligence (AI) is poised to transform therapeutic science. Therapeutics Data Commons is an initiative to access and evaluate AI capability across therapeutic modalities and stages of discovery, establishing a foundation for understanding which AI methods are most suitable and why.

We propose a new, to the best of our knowledge, compound technique to measure high-dynamic-range blood flow rate in a large-diameter vessel, which combines the dynamic scattering light (DLS) and the laser speckle contrast imaging (LSCI) methods, possessing the advantages of the high temporal resolution of DLS and the robust property of LSCI. By controlling the second-order spatial correlations of the laser speckle through two imaging systems, the speckle temporal intensity autocorrelation function g^2(t) and the decorrelation time τ_c are directly measured using a high-speed camera. It turns out the enhanced spatial second-order correlation helps to measure the blood flow with higher dynamic range and that the measured parameter β and the blood flow dynamics n were accurately determined. For further improvement the dynamic range, the modified LSCI method was adopted, and the decorrelation time as a function of blood flow rate was constructed. It reveals the feasibility of measuring the high flow rate in large-diameter vessels and provides significant guidance for the future biomedical study of the myocardial perfusion in coronary artery bypass grafting, ghost imaging, and ghost cytometry.

The development of a practical Hg(II) adsorbent is highly important for both environmental protection and public health. Herein, an adaptive metal–organic framework (MOF; FJI-H30) has been prepared from a highly flexible ligand [tris(pyridin-4-ylmethyl)amine] and Co(SCN)2with cheap Hg(II) adsorption sites (SCN–groups) that not only has excellent chemical stability but also can capture Hg(II) from aqueous solution with high adsorption capacity (705 mg g–1). Moreover, it also has good anti-interference ability and can be used repeatedly and large-scale prepared. Further researches demonstrate that the relatively high Hg(II) adsorption capacity originates from the adsorbed Hg(II)-induced deformation of FJI-H30, and such an adaptive deformation will reduce the potential repulsive forces between the adsorbed Hg(II) ions, enabling almost all Hg(II) absorption sites to adsorb Hg(II) ions. Finally, how to induce the deformation of FJI-H30by adsorbed Hg(II) also has been studied in detail. Our work not only provides a practical Hg(II) adsorbent for wastewater treatment but also offers a novel strategy for the design of novel MOFs for efficient heavy-metal-ion removal.

The underrepresentation of gender, racial, and ethnic minorities in clinical trials is a problem undermining the efficacy of treatments on minorities and preventing precise estimates of the effects within these subgroups. We propose FRAMM, a deep reinforcement learning framework for fair trial site selection to help address this problem. We focus on two real-world challenges: the data modalities used to guide selection are often incomplete for many potential trial sites, and the site selection needs to simultaneously optimize for both enrollment and diversity. To address the missing data challenge, FRAMMhas a modality encoder with a masked cross-attention mechanism for bypassing missing data. To make efficient trade-offs, FRAMMuses deep reinforcement learning with a reward function designed to simultaneously optimize for both enrollment and fairness. We evaluate FRAMMusing real-world historical clinical trials and show that it outperforms the leading baseline in enrollment-only settings while also greatly improving diversity.

AbstractMitigating globally emerging health problems such as obesity needs scalable solutions that can facilitate health management and promote healthier lifestyles outside of clinical settings. Such scalable solutions, while targeting general population, need to provide personalized behavior change plans that not only fit users’ own underlying physiologic dynamics but also suit their preferences and needs. There has been fast-growing development of mobile health devices and applications for monitoring of human behavior (such as physical activity and food intake) and health status such as BMI. However, there are challenges to translate these noisy and dynamic behavioral data into personalized longitudinal planning. To address such challenges, we develop an integrated framework that unifies dynamic modeling, sparse learning, dictionary learning and matrix completion to translate users’ behavioral data into personalized dynamic system models and use them as constraints for deriving deeply personalized longitudinal health plans. We evaluate the proposed framework on a real-world user behavioral dataset and demonstrate its promising utility and efficacy.

Emerging wearable and environmental sensor technologies provide health professionals with unprecedented capacity to continuously collect human behavioral data for health monitoring and management. This enables new solutions to mitigate globally emerging health problems such as obesity. With such outburst of dynamic sensor data, it is critical that appropriate mathematical models and computational methods are developed to translate the collected data into accurate characterization of the underlying health dynamics, enabling more reliable personalized monitoring, prediction, and intervention of health status changes. In addition to addressing common analytic challenges in analyzing sensor behavioral data, such as missing values and outliers, we focus on modeling heterogeneous dynamics to better capture health status changes under different conditions, which may lead to more effective state-dependent intervention strategies. We implement switching-state dynamic system models with different complexity levels on real-world daily behavioral data. Evaluation experiments of these models are conducted to demonstrate the importance of modeling the dynamic heterogeneity, as well as simultaneously conducting missing value imputation and outlier detection in achieving interpretable health dynamic models with better prediction of health status changes.

Recreating the natural anatomy in ligament reconstruction is crucial to fully restore the knee joint function and reduce impingement on iatrogenic injury to adjacent structures, yet is subject to the difficulties in locating ligament and other associated soft tissues insertion sites intraoperatively and the high interperson morphological variability cross patients. In this study, we present a new quantitative analysis method capable of achieving personalized identification of cruciate ligament and soft tissue insertions. We craft patient-specific features of tibia outline that can be accurately and reliably measured from CT images. In addition, we propose a supervised structure learning and prediction model with special interdimensional and response structure regularization terms to capture relationship between the spatial arrangement of soft tissue insertions and the patient-specific features extracted from the tibia outlines. In the experiment, the proposed model outperforms baseline models and provides an accurate and accessible approach that can be used as the first and the most critical step to achieve personalized surgical planning in cruciate ligament reconstruction.

Today, diagnosis of attention deficit hyperactivity disorder (ADHD) still primarily relies on a series of subjective evaluations that highly rely on a doctor’s experiences and intuitions from diagnostic interviews and observed behavior measures. An accurate and objective diagnosis of ADHD is still a challenge and leaves much to be desired. Many children and adults are inappropriately labeled with ADHD conditions, whereas many are left undiagnosed and untreated. Recent advances in neuroimaging studies have enabled us to search for both structural (e.g., cortical thickness, brain volume) and functional (functional connectivity) abnormalities that can potentially be used as new biomarkers of ADHD. However, structural and functional characteristics of neuroimaging data, especially magnetic resonance imaging (MRI), usually generate a large number of features. With a limited sample size, traditional machine learning techniques can be problematic to discover the true characteristic features of ADHD due to the significant issues of overfitting, computational burden, and interpretability of the model. There is an urgent need of efficient approaches to identify meaningful discriminative variables from a higher dimensional feature space when sample size is small compared with the number of features. To tackle this problem, this paper proposes a novel integrated feature ranking and selection framework that utilizes normalized brain cortical thickness features extracted from MRI data to discriminate ADHD subjects against healthy controls. The proposed framework combines information theoretic criteria and the least absolute shrinkage and selection operator (Lasso) method into a two-step feature selection process which is capable of selecting a sparse model while preserving the most informative features. The experimental results showed that the proposed framework generated the highest/comparable ADHD prediction accuracy compared with the state-of-the-art feature selection approaches with minimum number of features in the final model. The selected regions of interest in our model were consistent with recent brain–behavior studies of ADHD development, and thus confirmed the validity of the selected features by the proposed approach.

This paper studied the influence on corrosion resistant performance through the rust resistance performance tests and the resistance to chloride ion penetration tests. In the tests, four test specimens were prepared: (a) blank sample without corrosion inhibitors or composite admixture; (b) mix 20% composite admixture replacing the cement; (c) mix 2% organic inhibitor replacing the cement; (d) combine 20% composite admixture and 2% organic inhibitor replacing the cement. The results showed that both of the composite admixture and the organic inhibitors had the ability to prevent steel corrosion in concrete structures and had their own advantages respectively. Mixing mineral admixture and inhibitor together had synergy effect and had more obvious anticorrosion effect, which helped the specialized production of antirust compound-admixture to ensure homogeneity of concrete.

In order to simplify the traditional methods for simulating aircraft performance in ATC, a method is proposed in this paper by the flight aerodynamics theories, normal and maximal climb rate in steady climb motion and acceleration/deceleration of Straight-and-Level Flight of performance computation. This method can be used to simulate acceleration, deceleration, and climb/descent rate of usual civil aircraft types. It is convenient and useable compared with the traditional complex methods which need input many parameters manually in air traffic control (ATC) simulation. The experimental result listed in this paper coincides with the basic flying rules of aircraft types such as 737-300, A310-300, B747F, Y-7. The application of this method in the real ATC simulator for controller training proved its function and effect.

Due to the fluctuating and intermittent nature of solar irradiance, the power output of PV power station fluctuates. It brings great difficulties for power output forecasting of PV power station. Traditional PV power output forecasting technologies using physical/statistical model combined with NWP failed to provide accurate results. Based on the research of on-line analysis on ground-based cloud image captured by Total Sky Imager (TSI), through the technical phases of cloud recognition, cloud blocking prediction, distortion correction, projection transformation and integration with cloud forcing analysis, the roadmap of PV very short term solar irradiance forecasting based on ground-based cloud image analysis was presented in this paper. Theoretical analysis proves that the method tends to provide reasonable accurate forecasting results and can be widely used.

Clinical trials are crucial for drug development but often face uncertain outcomes due to safety, efficacy, or patient-recruitment problems. We propose the Hierarchical Interaction Network (HINT) to predict clinical trial outcomes. First, HINTencodes multi-modal data (drug molecule, target disease, trial eligibility criteria) into embeddings. Then, HINTtrains knowledge-embedding modules using drug pharmacokinetic and historical trial data. Finally, a hierarchical interaction graph connects all of the embeddings to capture their interactions and predict trial outcomes. HINTwas trained and validated on 1,160 phase I trials, 4,449 phase II trials, and 3,436 phase III trials. It obtained 0.665, 0.620, and 0.847 F1 scores on separate test sets of 627 phase I, 1,653 phase II, and 1,140 phase III trials, respectively. HINTsignificantly outperforms the best baseline method on most metrics. The benchmark dataset and codes are released at https://github.com/futianfan/clinical-trial-outcome-prediction.

Thanks to the increasing availability of genomics and other biomedical data, many machine learning algorithms have been proposed for a wide range of therapeutic discovery and development tasks. In this survey, we review the literature on machine learning applications for genomics through the lens of therapeutic development. We investigate the interplay among genomics, compounds, proteins, electronic health records, cellular images, and clinical texts. We identify 22 machine learning in genomics applications that span the whole therapeutics pipeline, from discovering novel targets, personalizing medicine, developing gene-editing tools, all the way to facilitating clinical trials and post-market studies. We also pinpoint seven key challenges in this field with potentials for expansion and impact. This survey examines recent research at the intersection of machine learning, genomics, and therapeutic development.