Model Predictive Energy-Maximising Tracking Control for a Wavestar-Prototype Wave Energy Converter

To date, one of the main challenges in the wave energy field is to achieve energy-maximizing control in order to reduce the levelized cost of energy (LCOE). This paper presents a model predictive velocity tracking control method based on a hierarchical structure for a Wavestar-like deivce in the WEC-SIM benchmark. The first part of the system structure aims to estimate the wave excitation moment (WEM) by using a Kalman filter. Then, an extended Kalman filter (EKF) is chosen to obtain the amplitude and angular frequency of the WEM in order to compute the reference velocity. Following this, a low-level model predictive control (MPC) method is designed to ensure the wave energy converter (WEC) tracks the optimal reference velocity for maximum energy extraction from irregular waves. Two Gaussian Process (GP) models are considered to predict the future wave excitation moment and future reference velocity, which are needed in MPC design. The proposed strategy can give a new vision for energy-maximizing tracking control based on MPC

Doubly Robust Augmented Model Accuracy Transfer Inference with High Dimensional Features

Due to label scarcity and covariate shift happening frequently in real-world studies, transfer learning has become an essential technique to train models generalizable to some target populations using existing labeled source data. Most existing transfer learning research has been focused on model estimation, while there is a paucity of literature on transfer inference for model accuracy despite its importance. We propose a novel $\mathbf{D}$oubly $\mathbf{R}$obust $\mathbf{A}$ugmented $\mathbf{M}$odel $\mathbf{A}$ccuracy $\mathbf{T}$ransfer $\mathbf{I}$nferen$\mathbf{C}$e (DRAMATIC) method for point and interval estimation of commonly used classification performance measures in an unlabeled target population using labeled source data. Specifically, DRAMATIC derives and evaluates the risk model for a binary response $Y$ against some low dimensional predictors $\mathbf{A}$ on the target population, leveraging $Y$ from source data only and high dimensional adjustment features $\mathbf{X}$ from both the source and target data. The proposed estimators are doubly robust in the sense that they are $n^{1/2}$ consistent when at least one model is correctly specified and certain model sparsity assumptions hold. Simulation results demonstrate that the point estimation have negligible bias and the confidence intervals derived by DRAMATIC attain satisfactory empirical coverage levels. We further illustrate the utility of our method to transfer the genetic risk prediction model and its accuracy evaluation for type II diabetes across two patient cohorts in Mass General Brigham (MGB) collected using different sampling mechanisms and at different time points

The toxic effect of mobile phone radiation on rabbit organs

Whether electromagnetic radiation (EMR) emitted from mobile phones is hazardous to human health is largely unknown. We investigated the effects of mobile phone radiation on critical organs in a rabbit model by exposing the animals to mobile phone radiation with sub-thermal specific absorption rate (SAR) of 1.0 and 0.7 W/kg for the head and the body, respectively, for 16 weeks (6 h/day, 6 days/week). There is no apparent change at the organ level. However, H&E staining showed that radiation-exposure significantly increased inflammatory cell infiltration in the liver and the lungs with a lesser degree of myocardial cell cytoplasmic vacuolation. In addition, results from γ-H2AX staining suggest that radiation can also cause DNA damage in the brain. Of note, no apparent activation of Caspase-3 in the organs examined. Our data altogether suggest that mobile phone radiation may be more hazardous to both the liver and the lungs, and less toxic to the brain and heart

One-step preparation of optically transparent Ni-Fe oxide film electrocatalyst for oxygen evolution reaction

Optically transparent cocatalyst film materials is very desirable for improved photoelectrochemical (PEC) oxygen evolution reaction (OER) over light harvesting photoelectrodes which require the exciting light to irradiate through the cocatalyst side, i.e., front-side illumination. In view of the reaction overpotential at electrode/electrolyte interface, the OER electrocatalysts have been extensively used as cocatalysts for PEC water oxidation on photoanode. In this work, the feasibility of a one-step fabrication of the transparent thin film catalyst for efficient electrochemical OER is investigated. The Ni-Fe bimetal oxide films, 200 nm in thickness, are used for study. Using a reactive magnetron co-sputtering technique, transparent (> 50% in wavelength range 500-2000 nm) Ni-Fe oxide films with high electrocatalytic activities were successfully prepared at room temperature. Upon optimization, the as-prepared bimetal oxide film with atomic ratio of Fe/Ni = 3:7 demonstrates the lowest overpotential for the OER in aqueous KOH solution, as low as 329 mV at current density of 2 mA cm 2, which is 135 and 108 mV lower than that of as-sputtered FeOx and NiOx thin films, respectively. It appears that this fabrication strategy is very promising to deposit optically transparent cocatalyst films on photoabsorbers for efficient PEC water splitting.This work was financially supported by the National Natural Science Foundation of China (No. 21090340), 973 National Basic Research Program of the Ministry of Science and Technology (No. 2014CB239400) and Solar Energy Action Plan of Chinese Academy of Sciences (KGCX2-YW-399+7-3)

The landscape of the methodology in drug repurposing using human genomic data:a systematic review

The process of drug development is expensive and time-consuming. In contrast, drug repurposing can be introduced to clinical practice more quickly and at a reduced cost. Over the last decade, there has been a significant expansion of large biobanks that link genomic data to electronic health record (EHR) data, public availability of various databases containing biological and clinical information, and rapid development of novel methodologies and algorithms in integrating different sources of data. This review aims to provide a thorough summary of different strategies that utilize genomic data to seek drug-repositioning opportunities. We searched MEDLINE and EMBASE databases to identify eligible studies up until 1st May 2023, with a total of 102 studies finally included after two-step parallel screening. We summarized commonly used strategies for drug repurposing, including Mendelian randomization, multi-omic-based and network-based studies, and illustrated each strategy with examples, as well as the data sources implemented. By leveraging existing knowledge and infrastructure to expedite the drug discovery process and reduce costs, drug repurposing potentially identifies new therapeutic uses for approved drugs in a more efficient and targeted manner. However, technical challenges when integrating different types of data and biased or incomplete understanding of drug interactions are important hindrances that cannot be disregarded in the pursuit of identifying novel therapeutic applications. This review offers an overview of drug repurposing methodologies, providing valuable insights and guiding future directions for advancing drug repurposing studies

Health effects of high serum calcium levels:Updated phenome-wide Mendelian randomisation investigation and review of Mendelian randomisation studies

BACKGROUND: Calcium plays a role in a wide range of biological functions. Here we conducted a phenome-wide Mendelian randomisation (MR-PheWAS) analysis and a systematic review for MR studies to comprehensively investigate the health effects of serum calcium. METHODS: One-hundred and thirty genetic variants strongly associated with serum calcium levels were used as instrumental variables. A phenome-wide association analysis (PheWAS) was conducted to examine the associations of genetically predicted serum calcium with 1473 distinct phenotypes in the UK Biobank including 339,197 individuals. Observed associations in PheWAS were further tested for replication in two-sample MR replication analysis. A systematic review for MR studies on serum calcium was performed to synthesize the published evidence and compare with the current MR-PheWAS findings. FINDINGS: Higher genetically predicted calcium levels were associated with decreased risk of 5 diseases in dermatologic and musculoskeletal systems and increased risk of 17 diseases in circulatory, digestive, endocrine, genitourinary and immune systems. Eight associations were replicated in two-sample MR analysis. These included decreased risk of osteoarthritis and increased risk of coronary artery disease, myocardial infarction, coronary atherosclerosis, hyperparathyroidism, disorder of parathyroid gland, gout, and calculus of kidney and ureter with increased serum calcium. Systematic review of 25 MR studies provided supporting evidence on five out of the eight disease outcomes, while the increased risk of gout, hyperparathyroidism and disorder of parathyroid gland were novel findings. INTERPRETATION: This study found wide-ranged health effects of high serum calcium, which suggests that the benefits and adversities of strategies promoting calcium intake should be assessed. FUNDING: ET is supported by a CRUK Career Development Fellowship (C31250/A22804). XL is supported by the Natural Science Fund for Distinguished Young Scholars of Zhejiang Province. SCL acknowledges research funding from the Swedish Heart Lung Foundation (Hjärt-Lungfonden, 20210351), the Swedish Research Council (Vetenskapsrådet, 2019-00977), and the Swedish Cancer Society (Cancerfonden)