Comparison of First-Line Dual Combination Treatments in Hypertension: Real-World Evidence from Multinational Heterogeneous Cohorts.

Background and objectives: 2018 ESC/ESH Hypertension guideline recommends 2-drug combination as initial anti-hypertensive therapy. However, real-world evidence for effectiveness of recommended regimens remains limited. We aimed to compare the effectiveness of first-line anti-hypertensive treatment combining 2 out of the following classes: angiotensin-converting enzyme (ACE) inhibitors/angiotensin-receptor blocker (A), calcium channel blocker (C), and thiazide-type diuretics (D).Methods: Treatment-naïve hypertensive adults without cardiovascular disease (CVD) who initiated dual anti-hypertensive medications were identified in 5 databases from US and Korea. The patients were matched for each comparison set by large-scale propensity score matching. Primary endpoint was all-cause mortality. Myocardial infarction, heart failure, stroke, and major adverse cardiac and cerebrovascular events as a composite outcome comprised the secondary measure.Results: A total of 987,983 patients met the eligibility criteria. After matching, 222,686, 32,344, and 38,513 patients were allocated to A+C vs. A+D, C+D vs. A+C, and C+D vs. A+D comparison, respectively. There was no significant difference in the mortality during total of 1,806,077 person-years: A+C vs. A+D (hazard ratio [HR], 1.08; 95% confidence interval [CI], 0.97-1.20; p=0.127), C+D vs. A+C (HR, 0.93; 95% CI, 0.87-1.01; p=0.067), and C+D vs. A+D (HR, 1.18; 95% CI, 0.95-1.47; p=0.104). A+C was associated with a slightly higher risk of heart failure (HR, 1.09; 95% CI, 1.01-1.18; p=0.040) and stroke (HR, 1.08; 95% CI, 1.01-1.17; p=0.040) than A+D.Conclusions: There was no significant difference in mortality among A+C, A+D, and C+D combination treatment in patients without previous CVD. This finding was consistent across multi-national heterogeneous cohorts in real-world practice

LiDAR and Camera Fusion Approach for Object Distance Estimation in Self-Driving Vehicles

The fusion of light detection and ranging (LiDAR) and camera data in real-time is known to be a crucial process in many applications, such as in autonomous driving, industrial automation, and robotics. Especially in the case of autonomous vehicles, the efficient fusion of data from these two types of sensors is important to enabling the depth of objects as well as the detection of objects at short and long distances. As both the sensors are capable of capturing the different attributes of the environment simultaneously, the integration of those attributes with an efficient fusion approach greatly benefits the reliable and consistent perception of the environment. This paper presents a method to estimate the distance (depth) between a self-driving car and other vehicles, objects, and signboards on its path using the accurate fusion approach. Based on the geometrical transformation and projection, low-level sensor fusion was performed between a camera and LiDAR using a 3D marker. Further, the fusion information is utilized to estimate the distance of objects detected by the RefineDet detector. Finally, the accuracy and performance of the sensor fusion and distance estimation approach were evaluated in terms of quantitative and qualitative analysis by considering real road and simulation environment scenarios. Thus the proposed lowlevel sensor fusion, based on the computational geometric transformation and projection for object distance estimation proves to be a promising solution for enabling reliable and consistent environment perception ability for autonomous vehicles. © 2020 by the authors.1

Co-occurrence matrix analysis-based semi-supervised training for object detection

One of the most important factors in training object recognition networks using convolutional neural networks (CNNs) is the provision of annotated data accompanying human judgment. Particularly, in object detection or semantic segmentation, the annotation process requires considerable human effort. In this paper, we propose a semi-supervised learning (SSL)-based training methodology for object detection, which makes use of automatic labeling of un-annotated data by applying a network previously trained from an annotated dataset. Because an inferred label by the trained network is dependent on the learned parameters, it is often meaningless for re-training the network. To transfer a valuable inferred label to the unlabeled data, we propose a re-alignment method based on co-occurrence matrix analysis that takes into account one-hot-vector encoding of the estimated label and the correlation between the objects in the image. We used an MS-COCO detection dataset to verify the performance of the proposed SSL method and deformable neural networks (D-ConvNets) as an object detector for basic training. The performance of the existing state-of-the-art detectors (DConvNets, YOLO v2, and single shot multi-box detector (SSD)) can be improved by the proposed SSL method without using the additional model parameter or modifying the network architecture.Comment: Submitted to International Conference on Image Processing (ICIP) 201

Self-selective ferroelectric memory realized with semimetalic graphene channel

A new concept of read-out method for ferroelectric random-access memory (FeRAM) using a graphene layer as the channel material of bottom-gated field effect transistor structure is demonstrated experimentally. The transconductance of the graphene channel is found to change its sign depending on the direction of spontaneous polarization (SP) in the underlying ferroelectric layer. This indicates that the memory state of FeRAM, specified by the SP direction of the ferroelectric layer, can be sensed unambiguously with transconductance measurements. With the proposed read-out method, it is possible to construct an array of ferroelectric memory cells in the form of a cross-point structure where the transconductance of a crossing cell can be measured selectively without any additional selector. This type of FeRAM can be a plausible solution for fabricating high speed, ultra-low power, long lifetime, and high density 3D stackable non-volatile memory

Quasi-Solid-State SiO2 Electrolyte Prepared from Raw Fly Ash for Enhanced Solar Energy Conversion

Quasi-solid-state electrolytes in dye-sensitized solar cells (DSSCs) prevent solvent leakage or evaporation and stability issues that conventional electrolytes cannot; however, there are no known reports that use such an electrolyte based on fly ash SiO2 (FA_SiO2) from raw fly ash (RFA) for solar energy conversion applications. Hence, in this study, quasi-solid-state electrolytes based on FA_SiO2 are prepared from RFA and poly(ethylene glycol) (PEG) for solar energy conversion. The structural, morphological, chemical, and electrochemical properties of the DSSCs using this electrolyte are characterized by X-ray diffraction (XRD), high-resolution field-emission scanning electron microscopy (HR-FESEM), X-ray fluorescence (XRF), diffuse reflectance spectroscopy, electrochemical impedance spectroscopy (EIS), and incident photon-to-electron conversion efficiency (IPCE) measurements. The DSSCs based on the quasi-solid-state electrolyte (SiO2) show a cell efficiency of 5.5%, which is higher than those of nanogel electrolytes (5.0%). The enhancement of the cell efficiency is primarily due to the increase in the open circuit voltage and fill factor caused by the reduced electron recombination and improved electron transfer properties. The findings confirm that the RFA-based quasi-solid-state (SiO2) electrolyte is an alternative to conventional liquid-state electrolytes, making this approach among the most promising strategies for use in low-cost solar energy conversion devices

Fabrication of Large Area, Ordered Nanoporous Structures on Various Substrates for Potential Electro-Optic Applications

Nanoporous structures have attracted great attention in electronics, sensor and storage devices, and photonics because of their large surface area, large volume to surface ratio, and potential for high-sensitivity sensor applications. Normally, electron or ion beam patterning can be used for nanopores fabrication by direct writing. However, direct writing is a rather expensive and time-consuming method due to its serial nature. Therefore, it may not translate to a preferred manufacturing process. In this research, a perfectly ordered large-area periodic pattern in an area of approximately 1 cm2 has been successfully fabricated on various substrates including glass, silicon, and polydimethylsiloxane, using a two-step process comprising visible light-based multibeam interference lithography and subsequent pattern transfer processes of reactive ion etching and nanomolding. Additionally, the multibeam interference lithography templated anodized aluminum oxide process has been described. Since the fabrication area in multibeam interference lithography can be extended by using a larger beam size, it is highly cost effective and manufacturable. Furthermore, although not described here, an electrodeposition process can be utilized as a pattern transfer process. This large-area perfectly ordered nanopore array will be very useful for high-density electronic memory and photonic bandgap and metamaterial applications

A Picosecond-Resolution Digitally-Controlled Timing Generator with One-Clock-Latency at Arbitrary Instantaneous Input

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Fabrication of Large Area, Ordered Nanoporous Structures on Various Substrates for Potential Electro-Optic Applications

Nanoporous structures have attracted great attention in electronics, sensor and storage devices, and photonics because of their large surface area, large volume to surface ratio, and potential for high-sensitivity sensor applications. Normally, electron or ion beam patterning can be used for nanopores fabrication by direct writing. However, direct writing is a rather expensive and time-consuming method due to its serial nature. Therefore, it may not translate to a preferred manufacturing process. In this research, a perfectly ordered large-area periodic pattern in an area of approximately 1 cm2 has been successfully fabricated on various substrates including glass, silicon, and polydimethylsiloxane, using a two-step process comprising visible light-based multibeam interference lithography and subsequent pattern transfer processes of reactive ion etching and nanomolding. Additionally, the multibeam interference lithography templated anodized aluminum oxide process has been described. Since the fabrication area in multibeam interference lithography can be extended by using a larger beam size, it is highly cost effective and manufacturable. Furthermore, although not described here, an electrodeposition process can be utilized as a pattern transfer process. This large-area perfectly ordered nanopore array will be very useful for high-density electronic memory and photonic bandgap and metamaterial applications

Combining Interference Lithography and Two-Photon Lithography for Fabricating Large-Area Photonic Crystal Structures with Controlled Defects

Interference lithography is a promising method for fabricating large-area, defect-free three-dimensional photonic crystal structures which can be used for facilitating the realization of photonic devices with a fast processing time. Although they can be used in waveguides, resonators, and detectors, their repeated regular array patterns can only be used for limited applications. In this study, we demonstrate a method for fabricating large-area photonic crystal structures with controlled defects by combining interference lithography and two-photon lithography using a light-curable resin. By combining regular array structures and controlled patterns, monotonous but large-area regular structures can be obtained. Furthermore, the patterned structures have considerable potential for use in various applications, such as solar cells, sensors, photodetectors, micro-/nano-electronics, and cell growth

Machine Learning Approach Using Routine Immediate Postoperative Laboratory Values for Predicting Postoperative Mortality

Background: Several prediction models have been proposed for preoperative risk stratification for mortality. However, few studies have investigated postoperative risk factors, which have a significant influence on survival after surgery. This study aimed to develop prediction models using routine immediate postoperative laboratory values for predicting postoperative mortality. Methods: Two tertiary hospital databases were used in this research: one for model development and another for external validation of the resulting models. The following algorithms were utilized for model development: LASSO logistic regression, random forest, deep neural network, and XGBoost. We built the models on the lab values from immediate postoperative blood tests and compared them with the SASA scoring system to demonstrate their efficacy. Results: There were 3817 patients who had immediate postoperative blood test values. All models trained on immediate postoperative lab values outperformed the SASA model. Furthermore, the developed random forest model had the best AUROC of 0.82 and AUPRC of 0.13, and the phosphorus level contributed the most to the random forest model. Conclusions: Machine learning models trained on routine immediate postoperative laboratory values outperformed previously published approaches in predicting 30-day postoperative mortality, indicating that they may be beneficial in identifying patients at increased risk of postoperative death