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Incomplete or inconsistent temporal neuroimaging records of patients over time pose a major challenge to accurately predict clinical scores for diagnosing Alzheimer’s Disease (AD). In this paper, we present an unsupervised method to learn enriched imaging biomarker representations that can simultaneously capture the information conveyed by all the baseline neuroimaging measures and the progressive variations of the available follow-up measurements of every participant. Our experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset show improved performance in predicting cognitive outcomes thereby demonstrating the effectiveness of our proposed method. 

IceCube alert events are neutrinos with a moderate-to-high probability of having astrophysical origin. In this study, we analyze 11 yr of IceCube data and investigate 122 alert events and a selection of high-energy tracks detected between 2009 and the end of 2021. This high-energy event selection (alert events + high-energy tracks) has an average probability of ≥0.5 of being of astrophysical origin. We search for additional continuous and transient neutrino emission within the high-energy events’ error regions. We find no evidence for significant continuous neutrino emission from any of the alert event directions. The only locally significant neutrino emission is the transient emission associated with the blazar TXS 0506+056, with a local significance of 3σ, which confirms previous IceCube studies. When correcting for 122 test positions, the globalp-value is 0.156 and compatible with the background hypothesis. We constrain the total continuous flux emitted from all 122 test positions at 100 TeV to be below 1.2 × 10⁻¹⁵(TeV cm²s)⁻¹at 90% confidence assuming anE⁻²spectrum. This corresponds to 4.5% of IceCube’s astrophysical diffuse flux. Overall, we find no indication that alert events in general are linked to lower-energetic continuous or transient neutrino emission.

 

Traditional accelerated life test plans are typically based on optimizing the C-optimality for minimizing the variance of an interested quantile of the lifetime distribution. These methods often rely on some specified planning values for the model parameters, which are usually unknown prior to the actual tests. The ambiguity of the specified parameters can lead to suboptimal designs for optimizing the reliability performance of interest. In this paper, we propose a sequential design strategy for life test plans based on considering dual objectives. In the early stage of the sequential experiment, we suggest allocating more design locations based on optimizing the D-optimality to quickly gain precision in the estimated model parameters. In the later stage of the experiment, we can allocate more observations based on optimizing the C-optimality to maximize the precision of the estimated quantile of the lifetime distribution. We compare the proposed sequential design strategy with existing test plans considering only a single criterion and illustrate the new method with an example on the fatigue testing of polymer composites. 

Unbiased photoelectrochemical hydrogen production with high efficiency and durability is highly desired for solar energy storage. Here, we report a microbial photoelectrochemical (MPEC) system that demonstrated superior performance when equipped with bioanodes and black silicon photocathode with a unique ‘‘Swiss-cheese’’ interface. The MPEC utilizes the chemical energy embedded in wastewater organics to boost solar H2 production, which overcomes barriers on anode H2O oxidation. Without any bias, the MPEC generates a record photocurrent (up to 23 mA cm2) and retains prolonged stability for over 90 hours with high Faradaic efficiency (96–99%). The calculated turnover number for MoSx catalyst during a 90 h period is 495 471 with an average frequency of 1.53 s1 . The system replaced pure water on the anode with actual wastewater and achieved waste organic removal up to 16 kg COD m2 photocathode per day. Cost credits from concurrent wastewater treatment and low-cost design make photoelectrochemical H2 production practical for the first time 

Current artificial photosynthesis (APS) systems are promising for the storage of solar energy via transportable and storable fuels, but the anodic half-reaction of water oxidation is an energy intensive process which in many cases poorly couples with the cathodic half-reaction. Here we demonstrate a self-sustaining microbial photoelectrosynthesis (MPES) system that pairs microbial electrochemical oxidation with photoelectrochemical water reduction for energy efficient H2 generation. MPES reduces the overall energy requirements thereby greatly expanding the range of semiconductors that can be utilized in APS. Due to the recovery of chemical energy from waste organics by the mild microbial process and utilization of cost-effective and stable catalyst/electrode materials, our MPES system produced a stable current of 0.4 mA/cm2 for 24 h without any external bias and ∼10 mA/cm2 with a modest bias under one sun illumination. This system also showed other merits, such as creating benefits of wastewater treatment and facile preparation and scalability.