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Health information-seeking behaviors are critical for individuals managing illnesses, especially in cases like hemifacial spasm (HFS), a condition familiar to specialists but not to general practitioners and the broader public. The limited awareness of HFS often leads to scarce online resources for self-diagnosis and a heightened risk of misdiagnosis. In China, the imbalance in the doctor-to-patient ratio and HFS's low incidence exacerbate information and power asymmetries within doctor-patient relationship. While HCI and CSCW research predominantly focuses on more common chronic conditions, our study delves into HFS, aiming to deepen the understanding of HFS patients' health information-seeking journeys in China, as well as exploring how these patients utilize various stakeholders and online resources to overcome asymmetries in the doctor-patient relationship and access top-tier medical resources. Through interviews with three neurosurgeons and 12 HFS patients from both rural and urban areas, and applying Actor-Network Theory, we offer empirical insights into the interactions and workflows within the health information-seeking network. Our analysis identified five strategies HFS patients adopted to access top-tier medical resources. We also propose design opportunities for technology to aid patients in overcoming the challenges encountered during their health information-seeking journey.

The integrated community energy system (ICES) has emerged as a promising solution for enhancing the efficiency of the distribution system by effectively coordinating multiple energy sources. However, the operational optimization of ICES is hindered by the physical constraints of heterogeneous networks including electricity, natural gas, and heat. These challenges are difficult to address due to the non-linearity of network constraints and the high complexity of multi-network coordination. This paper, therefore, proposes a novel Safe Reinforcement Learning (SRL) algorithm to optimize the multi-network constrained operation problem of ICES. Firstly, a comprehensive ICES model is established considering integrated demand response (IDR), multiple energy devices, and network constraints. The multi-network operational optimization problem of ICES is then presented and reformulated as a constrained Markov Decision Process (C-MDP) accounting for violating physical network constraints. The proposed novel SRL algorithm, named Primal-Dual Twin Delayed Deep Deterministic Policy Gradient (PD-TD3), solves the C-MDP by employing a Lagrangian multiplier to penalize the multi-network constraint violation, ensuring that violations are within a tolerated range and avoid over-conservative strategy with a low reward at the same time. The proposed algorithm accurately estimates the cumulative reward and cost of the training process, thus achieving a fair balance between improving profits and reducing constraint violations in a privacy-protected environment with only partial information. A case study comparing the proposed algorithm with benchmark RL algorithms demonstrates the computational performance in increasing total profits and alleviating the network constraint violations.

The integration of individual microgrids (MGs) into Microgrid Clusters (MGCs) significantly improves the reliability and flexibility of energy supply, through resource sharing and ensuring backup during outages. The dispatch of MGCs is the key challenge to be tackled to ensure their secure and economic operation. Currently, there is a lack of optimization method that can achieve a trade-off among top-priority requirements of MGCs' dispatch, including fast computation speed, optimality, multiple objectives, and risk mitigation against uncertainty. In this paper, a novel Multi-Objective, Risk-Sensitive, and Online Trust Region Policy Optimization (RS-TRPO) Algorithm is proposed to tackle this problem. First, a dispatch paradigm for autonomous MGs in the MGC is proposed, enabling them sequentially implement their self-dispatch to mitigate potential conflicts. This dispatch paradigm is then formulated as a Markov Game model, which is finally solved by the RS-TRPO algorithm. This online algorithm enables MGs to spontaneously search for the Pareto Frontier considering multiple objectives and risk mitigation. The outstanding computational performance of this algorithm is demonstrated in comparison with mathematical programming methods and heuristic algorithms in a modified IEEE 30-Bus Test System integrated with four autonomous MGs.

Astronomy observation is difficult in urban environments due to the background noise generated by human activities. Consequently, promoting astronomy in metropolitan areas is challenging. In this work, we propose a low-cost, educational experiment called Wok the Hydrogen (WTH) that offers opportunities for scientific observation in urban environments, specifically the observation of the $21$ cm ($f_{21} = 1420.4$ MHz) emission from neutral hydrogen in the Milky Way. We demonstrate how to construct a radio telescope using kitchenware, along with additional electronic equipment that can be easily purchased online. The total system cost is controlled within 150 dollars. We also outline the subsequent data analysis procedures for deriving the recession velocity of galactic hydrogen from the raw data. The system was tested on the campus of the Hong Kong University of Science and Technology, which is located approximately 2 km northeast of the nearest residential area with a population of 0.4 million and about 10 km east of the downtown area with a population of 2 million. We show that a precision of $\Delta v \approx \pm 20$ km s$^{-1}$ can be achieved for determining the recession velocity of neutral hydrogen with this relatively simple setup, and the precision can be further improved with longer exposure time., Comment: Updated: minor corrections to typos

The Nash Equilibrium (NE) estimation in bidding games of electricity markets is the key concern of both generation companies (GENCOs) for bidding strategy optimization and the Independent System Operator (ISO) for market surveillance. However, existing methods for NE estimation in emerging modern electricity markets (FEM) are inaccurate and inefficient because the priori knowledge of bidding strategies before any environment changes, such as load demand variations, network congestion, and modifications of market design, is not fully utilized. In this paper, a Bayes-adaptive Markov Decision Process in FEM (BAMDP-FEM) is therefore developed to model the GENCOs' bidding strategy optimization considering the priori knowledge. A novel Multi-Agent Generative Adversarial Imitation Learning algorithm (MAGAIL-FEM) is then proposed to enable GENCOs to learn simultaneously from priori knowledge and interactions with changing environments. The obtained NE is a Bayesian Nash Equilibrium (BNE) with priori knowledge transferred from the previous environment. In the case study, the superiority of this proposed algorithm in terms of convergence speed compared with conventional methods is verified. It is concluded that the optimal bidding strategies in the obtained BNE can always lead to more profits than NE due to the effective learning from the priori knowledge. Also, BNE is more accurate and consistent with situations in real-world markets., Comment: It is old version with mistakes

This two-part paper develops a paradigmatic theory and detailed methods of the joint electricity market design using reinforcement-learning (RL)-based simulation. In Part 2, this theory is further demonstrated by elaborating detailed methods of designing an electricity spot market (ESM), together with a reserved capacity product (RC) in the ancillary service market (ASM) and a virtual bidding (VB) product in the financial market (FM). Following the theory proposed in Part 1, firstly, market design options in the joint market are specified. Then, the Markov game model is developed, in which we show how to incorporate market design options and uncertain risks in model formulation. A multi-agent policy proximal optimization (MAPPO) algorithm is elaborated, as a practical implementation of the generalized market simulation method developed in Part 1. Finally, the case study demonstrates how to pick the best market design options by using some of the market operation performance indicators proposed in Part 1, based on the simulation results generated by implementing the MAPPO algorithm. The impacts of different market design options on market participants' bidding strategy preference are also discussed., Comment: It is old version with mistakes

In face of the pressing need of decarbonization in the power sector, the re-design of electricity market is necessary as a Marco-level approach to accommodate the high penetration of renewable generations, and to achieve power system operation security, economic efficiency, and environmental friendliness. However, existing market design methodologies suffer from the lack of coordination among energy spot market (ESM), ancillary service market (ASM) and financial market (FM), i.e., the "joint market", and the lack of reliable simulation-based verification. To tackle these deficiencies, this two-part paper develops a paradigmatic theory and detailed methods of the joint market design using reinforcement-learning (RL)-based simulation. In Part 1, the theory and framework of this novel market design philosophy are proposed. First, the controversial market design options while designing the joint market are summarized as the targeted research questions. Second, the Markov game model is developed to describe the bidding game in the joint market, incorporating the market design options to be determined. Third, a framework of deploying multiple types of RL algorithms to simulate the market model is developed. Finally, several market operation performance indicators are proposed to validate the market design based on the simulation results., Comment: It is old version with mistakes

The ferroelectric material usually exhibits temperature dependent spontaneous polarization, known as pyroelectricity, which can be used to directly convert thermal energy to electricity from ambient low-grade waste heat. When utilizing the structural phase transformations of the material, the conversion capability can be magnified, consequently the device performance can be strongly boosted by orders of magnitude. However, common ferroelectric oxides suffer the mechanical fatigue and functional degradation over cyclic phase transformations, hindering widespread applications of the energy conversion device. In this paper, we investigate the mechanical and functional reversibility of the material by lattice tuning and grain coarsening. We discover the lead-free compound Ba(Ce$_{0.005}$Zr$_{0.005}$)Ti$_{0.99}$O3-0.10(Ba$_{0.7}$Ca$_{0.3}$)TiO$_3$ (BCZT-0.10BCT) satisfying the compatibility condition among all present phases by its lattice parameters, making the phase transformations highly reversible. We demonstrated that the energy conversion device with the equiaxial coarse grains exhibits exceptional fatigue-resistance, with stable pyroelectric current output at 4$\mu$A/cm$^2$ over 3,000 energy conversion cycles. Our work opens a new way to fabricate high-performance material that advances the pyroelectric energy conversion for practical application in engineering., Comment: 18 pages, 5 figures, 1 table

Mutual events (MEs) are eclipses and occultations among planetary natural satellites. Most of the time, eclipses and occultations occur separately. However, the same satellite pair will exhibit an eclipse and an occultation quasi-simultaneously under particular orbital configurations. This kind of rare event is termed as a quasi-simultaneous mutual event (QSME). During the 2021 campaign of mutual events of jovian satellites, we observed a QSME between Europa and Ganymede. The present study aims to describe and study the event in detail. We observed the QSME with a CCD camera attached to a 300-mm telescope at the Hong Kong Space Museum Sai Kung iObservatory. We obtained the combined flux of Europa and Ganymede from aperture photometry. A geometric model was developed to explain the light curve observed. Our results are compared with theoretical predictions (O-C). We found that our simple geometric model can explain the QSME fairly accurately, and the QSME light curve is a superposition of the light curves of an eclipse and an occultation. Notably, the observed flux drops are within 2.6% of the theoretical predictions. The size of the event central time O-Cs ranges from -14.4 to 43.2 s. Both O-Cs of flux drop and timing are comparable to other studies adopting more complicated models. Given the event rarity, model simplicity and accuracy, we encourage more observations and analysis on QSMEs to improve Solar System ephemerides., Comment: 23 pages, 5 appendixes, 16 figures, 7 tables

Focus scanning is critically important in many terahertz (THz) imaging and sensing applications. A traditional single focusing transmitarray can achieve a good focus when the source is on-axis but moving the source off-axis produces a significant aberration. This paper presents a novel approach to reducing coma in off-axis scanning in the THz region. Here, a dual transmitarray solution is proposed, in which a transmitarray with an optimized phase profile is placed behind a regular phase profile transmitarray. A linearly polarized, dual-transmitarray antenna was fabricated for validation, and the focusing performances were experimentally characterized. The measured results are in good agreement with the theoretical ones. The generated spot of the dual-transmitarray antenna remains focused on an angle up to 50deg, with a -3 dB spot size of less than 4 mm at 290 GHz. The measured near-field sidelobes are all below -10 dB within the whole scanning range., Comment: 9 pages, 14 figures

Amplitude-modulated (AM) leaky-wave antenna (LWA), a concept following amplitude modulation technique from classical communications theory, is a promising structure that enables transforming traveling wave into the radiating wave. In this paper, we provide a different perspective based on the classical holographic theory to gain insight into the physical mechanism of AM LWA and design novel LWAs. In analogy to the classical optical Gabor hologram, we demonstrate that only the amplitude variation of the traveling wave is needed to record both the amplitude and phase information of the object wave. The consistency between the holography theory and previous spatial spectrum approach for explaining AM LWA operating mechanism is also demonstrated. For validation purpose, two novel millimeter-wave (mmW) holographic AM LWAs based on the substrate integrated inset dielectric waveguide (IDW) are designed. The first one is for far-field high-gain applications while the second is for near-field focusing (NFF) applications. Both simulated and measured results demonstrate the effectiveness of the AM holography theory for AM LWAs analysis and design., Comment: 9 pages, 17 figures

The increasing penetration of renewable generations, along with the deregulation and marketization of power industry, promotes the transformation of power market operation paradigms. The optimal bidding strategy and dispatching methodology under these new paradigms are prioritized concerns for both market participants and power system operators, with obstacles of uncertain characteristics, computational efficiency, as well as requirements of hyperopic decision-making. To tackle these problems, the Reinforcement Learning (RL), as an emerging machine learning technique with advantages compared with conventional optimization tools, is playing an increasingly significant role in both academia and industry. This paper presents a comprehensive review of RL applications in deregulated power market operation including bidding and dispatching strategy optimization, based on more than 150 carefully selected literatures. For each application, apart from a paradigmatic summary of generalized methodology, in-depth discussions of applicability and obstacles while deploying RL techniques are also provided. Finally, some RL techniques that have great potentiality to be deployed in bidding and dispatching problems are recommended and discussed., Comment: It is old version with mistakes

We theorize the surface step characterization by reflected incoherent-light differential interference microscopy with consideration of the optical diffraction effect. With the integration of localization analysis, we develop a quantitative differential interference optical system, by which we demonstrate that the axial resolution of measuring surface height variation is sensitive to the shear distance between the two spatially differentiated beams. We fabricate three nanometer-size steps by photolithography, and successfully characterize their 1D height variations with 0.13 nm Hz^(-1/2) axial precision. Our result suggests that the optical differential interference microscopy can be used for real-time characterization of surface structure with a subnanometer accuracy and a large field of view, which is greatly beneficial to the surface characterization of micro/nano-electromechanical systems., Comment: 12 pages, 7 figures