Optimal Pricing Strategy for Multichannel Healthcare Services

As a combination of online and offline channel services, multichannel healthcare services currently play important roles in helping consumers solve their health problems. In this study, we establish a stylized model to investigate how healthcare service providers should price in multi-channels and when consumers should choose online service, taking misdiagnosis rate and the severity of disease problems into account. Our results show that the prices of the online channel and offline channel can increase when the misdiagnosis rate is low and minor problem inspection rate online is high. Moreover, when the diagnosis rate is high, the profit of online channel would increase, and then improve the profit of multichannel services. These findings provide insights for the theoretical research of online healthcare services and practice management on pricing strategies in multichannel healthcare services

All-Dielectric Meta-optics for High-Efficiency Independent Amplitude and Phase Manipulation

Metasurfaces, composed of subwavelength scattering elements, have demonstrated remarkable control over the transmitted amplitude, phase, and polarization of light. However, manipulating the amplitude upon transmission has required loss if a single metasurface is used. Here, we describe high-efficiency independent manipulation of the amplitude and phase of a beam using two lossless phase-only metasurfaces separated by a distance. With this configuration, we experimentally demonstrate optical components such as combined beam-forming and splitting devices, as well as those for forming complex-valued, three-dimensional holograms. The compound meta-optic platform provides a promising approach for achieving high performance optical holographic displays and compact optical components, while exhibiting a high overall efficiency

Intelligent Multi-channel Meta-imagers for Accelerating Machine Vision

Rapid developments in machine vision have led to advances in a variety of industries, from medical image analysis to autonomous systems. These achievements, however, typically necessitate digital neural networks with heavy computational requirements, which are limited by high energy consumption and further hinder real-time decision-making when computation resources are not accessible. Here, we demonstrate an intelligent meta-imager that is designed to work in concert with a digital back-end to off-load computationally expensive convolution operations into high-speed and low-power optics. In this architecture, metasurfaces enable both angle and polarization multiplexing to create multiple information channels that perform positive and negatively valued convolution operations in a single shot. The meta-imager is employed for object classification, experimentally achieving 98.6% accurate classification of handwritten digits and 88.8% accuracy in classifying fashion images. With compactness, high speed, and low power consumption, this approach could find a wide range of applications in artificial intelligence and machine vision applications.Comment: 15 pages, 5 figure

Digital Modeling on Large Kernel Metamaterial Neural Network

Deep neural networks (DNNs) utilized recently are physically deployed with computational units (e.g., CPUs and GPUs). Such a design might lead to a heavy computational burden, significant latency, and intensive power consumption, which are critical limitations in applications such as the Internet of Things (IoT), edge computing, and the usage of drones. Recent advances in optical computational units (e.g., metamaterial) have shed light on energy-free and light-speed neural networks. However, the digital design of the metamaterial neural network (MNN) is fundamentally limited by its physical limitations, such as precision, noise, and bandwidth during fabrication. Moreover, the unique advantages of MNN's (e.g., light-speed computation) are not fully explored via standard 3x3 convolution kernels. In this paper, we propose a novel large kernel metamaterial neural network (LMNN) that maximizes the digital capacity of the state-of-the-art (SOTA) MNN with model re-parametrization and network compression, while also considering the optical limitation explicitly. The new digital learning scheme can maximize the learning capacity of MNN while modeling the physical restrictions of meta-optic. With the proposed LMNN, the computation cost of the convolutional front-end can be offloaded into fabricated optical hardware. The experimental results on two publicly available datasets demonstrate that the optimized hybrid design improved classification accuracy while reducing computational latency. The development of the proposed LMNN is a promising step towards the ultimate goal of energy-free and light-speed AI

Potential Blood Pressure Goals in IgA Nephropathy: Prevalence, Awareness, and Treatment Rates in Chronic Kidney Disease Among Patients with Hypertension in China (PATRIOTIC) Study

Background/Aims: IgA nephropathy is the most prevalent form of primary glomerulonephritis worldwide. Among patients with kidney disease, hypertension is one of the most important risk factors of disease progression. Considering the limited evidence regarding the appropriate blood pressure (BP) goal for patients with IgA nephropathy, our aim was to critically appraise the potential BP goal in IgA nephropathy. Methods: We performed a retrospective analysis of the BP data from 1055 patients with IgA nephropathy, extracted from the database of a nationwide, multi-center, cross-sectional study, including 61 tertiary hospitals in China. Hypertension was defined by a BP ≥140/90 mmHg. Three BP cutoff levels were evaluated as control values: < 140/90 mmHg, < 130/80 mmHg and < 125/75 mmHg. The primary outcome of our study was the prevalence of BP control among patients with a 24-h proteinuria < 1 g/d or ≥ 1 g/d. Multivariate logistic regression analysis was used to identify demographic and clinical factors associated with a decrease in renal function for the different target levels of BP. Results: The overall prevalence of hypertension was 63.3%. BP was controlled under 140/90 mmHg in 49.1% of patients, with 34.3% of patients with proteinuria < 1 g/d reaching the target BP < 130/80 mmHg and only 12.9% of patients with proteinuria > 1 g/d achieving a BP < 125/75 mmHg. Among patients with proteinuria < 1 g/d, the adjusted odds ratios (OR) and 95% confidence interval (95% CI) of a decrease in renal function, for the 3 target BP levels, were as follows (P > 0.05): < 140/90 mmHg, 0.9 (0.5 - 1.6); < 130/80 mmHg, 1.0 (0.5 - 1.8); and < 125/75 mmHg, 1.0 (0.5 - 2.0). With proteinuria ≥1 g/d, the adjusted ORs (95%CI) of attaining the BP targets of < 140/90 mmHg, < 130/80 mmHg and < 125/75 mmHg were 0.4 (0.2 - 0.6), 0.2 (0.1 - 0.4) and 0.3 (0.1 - 0.5), respectively (P < 0.05). Conclusion: Hypertension was common in IgA nephropathy and hypertensive control was suboptimal. Our result supports a benefit of intensive control of BP < 130/80 mmHg for patients with proteinuria ≥1 g/d. However, in patients with proteinuria < 1 g/d, a renoprotective effect of this BP goal was not identified